Path to robust digital mapping of soil C:N ratio: geographic bias and uncertainty gap
Year: 2025
Type: article
Abstract: The carbon-to-nitrogen (C:N) ratio is a key indicator of soil quality, nutrient cycling, and ecosystem function. While advances in digital soil mapping offer powerful tools for spatially resolving soil properties, understanding of the spatial patterns of soil C:N ratio and their drivers and implications remains limited. In this review, we synthesized recent progress by analyzing 51 peer-reviewed articles published up to August 2025. Our analysis reveals that despite a rapid increase in publications, research on mapping soil C:N ratio remains limited compared to other soil properties and is characterized by biases. Research is geographically concentrated in China (45%), heavily focused on cropland ecosystems, and predominantly limited to topsoil (0–30 cm). Methodologically, direct modeling of soil C:N ratio (80%) and 2D spatial models (94%) are common. Despite recent methodological advances, major challenges remain, which include the lack of uncertainty analysis (reported in only 10% of studies), the geographic and vertical sampling biases, and the tendency for predictive models to underestimate the true range of soil C:N ratio. Future perspectives should prioritize the adoption of 3D modeling frameworks, the integration of mechanistic understanding with empirical data-driven approaches through novel environmental covariates, and the standardization of reporting for sampling and validation protocols. Future efforts should focus on broadening the geographic and contextual scope of soil C:N mapping, mandating uncertainty quantification, and enhancing methodological transparency to produce more robust and globally relevant soil information.
Source: Soil & Environmental Health
Authors: Yujie Hong, Lebing Wu, Peipei Xue, Bifeng Hu, Zhou Shi, Songchao Chen
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $1,800
Awards: G3136773629, G7988465943
OpenAlex ID: W4417079381