Key Takeaways & Executive Findings
- •• • Grassland topsoil exhibited the highest carbon content (40.36±22.92 g·kg−1) but also the highest variability (CV=0.57), indicating heterogeneous carbon storage potential across karst landscapes. • • Forestland showed the most stable carbon and nitrogen contents (CV=0.35 and 0.26, respectively) with nitrogen content of 2.89±0.76 g·kg−1, suggesting that forest soils provide reliable carbon and nitrogen sequestration. • • Cultivated land had the lowest carbon (34.06±14.57 g·kg−1) and nitrogen (2.50±0.65 g·kg−1) contents, with moderate variability (CV=0.43 for C), highlighting the negative impact of agricultural management on soil organic matter. • • Across all land use types, soil carbon and nitrogen were strongly positively correlated (r>0.5, P<0.001), indicating coupled dynamics that are critical for predicting carbon-nitrogen interactions under land use change.
Abstract
This study investigates the effects of different land use types on topsoil carbon (C) and nitrogen (N) contents and their spatial distribution in the lower reaches of the Sancha River Basin, a karst region of the Yunnan-Guizhou Plateau. Grid sampling collected 0–20 cm topsoil from forestland (n=23), cultivated land (n=25), and grassland (n=32). Total C and N were measured. Results showed that topsoil C content followed grassland (40.36±22.92 g·kg−1) > forestland (37.05±12.83 g·kg−1) > cultivated land (34.06±14.57 g·kg−1), while N content followed forestland (2.89±0.76 g·kg−1) > grassland (2.67±1.19 g·kg−1) > cultivated land (2.50±0.65 g·kg−1). One-way ANOVA revealed no significant differences among land use types (P>0.05). Soil C and N were significantly positively correlated across all land uses (r>0.5, P<0.001). Coefficient of variation (CV) indicated grassland had the highest C variability (0.57), while forestland showed the most stable C and N (CV=0.35 and 0.26, respectively). Cultivated land had CVs of 0.43 for C and 0.26 for N. Spatially, forestland exhibited concentrated high C values with significant N heterogeneity; grassland had higher C in southern and eastern areas but scattered distribution, with generally low and variable N; cultivated land showed uniform but lowest C and N. Land use types significantly drive topsoil C and N dynamics through vegetation input, soil disturbance, and management practices, underscoring the importance of rational land use planning for enhancing carbon sink functions and sustainable development in karst watersheds.
1. Introduction
Karst ecosystems in the Yunnan-Guizhou Plateau are particularly vulnerable to land use change, yet the specific impacts on topsoil carbon and nitrogen dynamics remain poorly quantified. Existing studies often focus on single land use types or fail to capture spatial heterogeneity, limiting the development of targeted land management strategies. The Sancha River Basin, with its mosaic of forestland, grassland, and cultivated land, provides an ideal setting to assess how land use alters soil organic matter pools in karst terrains.
This study addresses the bottleneck by systematically sampling topsoil across three dominant land use types and employing rigorous statistical and spatial analyses. By quantifying carbon and nitrogen contents and their variability, we aim to identify land use practices that enhance carbon sequestration while maintaining soil fertility. The findings provide empirical evidence for optimizing land use planning in karst regions, contributing to global carbon cycle models and sustainable development goals.
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LI Hong, LI Canfeng, SHEN Run, HUANG Chao, CHEN Jianglin, MAO Jianmei, YANG Chaolei (2026). Impact of Land Use Types on Topsoil Carbon and Nitrogen and Their Spatial Distribution Characteristics in the Karst Area of the Yunnan-Guizhou Plateau: A Case Study of the Sancha River Basin. Environmental Chemistry. https://doi.org/10.7524/j.issn.0254-6108.2026022502
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Frequently Asked Questions
What are the specific carbon and nitrogen contents in topsoil for each land use type, and how do they compare statistically?
Grassland had the highest carbon content (40.36±22.92 g·kg−1), followed by forestland (37.05±12.83 g·kg−1) and cultivated land (34.06±14.57 g·kg−1). Nitrogen content was highest in forestland (2.89±0.76 g·kg−1), then grassland (2.67±1.19 g·kg−1), and lowest in cultivated land (2.50±0.65 g·kg−1). One-way ANOVA showed no significant differences among land use types (P>0.05), indicating that land use alone does not lead to statistically distinct topsoil carbon and nitrogen levels in this karst basin.
How does spatial variability of soil carbon and nitrogen differ among land use types, and what are the implications for sampling strategies?
Grassland exhibited the highest carbon variability (CV=0.57), while forestland had the most stable carbon and nitrogen (CV=0.35 and 0.26, respectively). Cultivated land showed moderate carbon variability (CV=0.43) and low nitrogen variability (CV=0.26). These differences imply that sampling designs must account for land use-specific heterogeneity; for grassland, more intensive sampling is required to accurately estimate carbon stocks, whereas forestland may require fewer samples due to lower variability.
What is the correlation between soil carbon and nitrogen across land use types, and why is it important for carbon-nitrogen coupling studies?
Soil carbon and nitrogen were significantly positively correlated across all land use types (r>0.5, P<0.001). This strong coupling suggests that processes affecting carbon storage also influence nitrogen retention, which is critical for predicting ecosystem responses to land use change and for modeling carbon-nitrogen interactions in karst soils.
How do land use types influence the spatial distribution of soil carbon and nitrogen in the Sancha River Basin?
Forestland showed concentrated high carbon values with significant nitrogen spatial heterogeneity. Grassland had higher carbon in southern and eastern areas but scattered distribution, with generally low and variable nitrogen. Cultivated land exhibited uniform but lowest carbon and nitrogen contents. These patterns reflect differences in vegetation input, soil disturbance, and management practices, highlighting the need for land use-specific management to enhance soil carbon sequestration.
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