Spatial variability characteristics and drivers of surface soil nitrogen fractions in the drylands of northern China

doi:10.1007/s40333-025-0065-z

Journal of Arid Land

2025, Vol. 17

Issue (11): 1558-1575 DOI: 10.1007/s40333-025-0065-z

Research article

Spatial variability characteristics and drivers of surface soil nitrogen fractions in the drylands of northern China

ZHANG Shihang¹^,², CHEN Yusen²^,³, ZHOU Xiaobing³^,^*(

), ZHANG Yuanming³

¹Key Laboratory of Mountain Surface Processes and Ecological Regulation, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
²University of Chinese Academy of Sciences, Beijing 100049, China
³State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

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Abstract

In dryland ecosystems, nitrogen (N) is the primary limiting factor after water availability, constraining both plant productivity and organic matter decomposition while also regulating ecosystem function and service provision. However, the distributions of different soil N fraction stocks in drylands and the factors that influence them remain poorly understood. In this study, we collected 2076 soil samples from 173 sites across the drylands of northern China during the summers of 2021 and 2022. Using the best-performing eXtreme Gradient Boosting (XGBoost) model, we mapped the spatial distributions of the soil N fraction stocks and identified the key drivers of their variability. Our findings revealed that the stocks of total nitrogen (TN), inorganic nitrogen (IN), and microbial biomass nitrogen (MBN) in the top 30 cm soil layer were 1020.4, 92.2, and 40.8 Tg, respectively, with corresponding mean densities of 164.6, 14.9, and 6.6 g/m². Climate variables—particularly mean annual temperature and aridity—along with human impacts emerged as the dominant drivers of soil N stock distribution. Notably, increased aridity and intensified human impacts exerted mutually counteracting effects on soil N fractions: aridity-driven moisture limitation generally suppressed N accumulation, whereas anthropogenic activities (e.g., fertilization and grazing) promoted N enrichment. By identifying the key environmental and anthropogenic factors shaping the soil N distribution, this study improves the accuracy of regional and global N stock estimates. These insights provide a scientific foundation for developing more effective soil N management strategies in dryland ecosystems, contributing to sustainable land use and long-term ecosystem resilience in drylands.

Key words： soil nitrogen fractions total nitrogen (TN) inorganic nitrogen (IN) microbial biomass nitrogen (MBN) machine learning model eXtreme Gradient Boosting (XGBoost) model dryland ecosystems

Received: 09 March 2025 Published: 30 November 2025

Corresponding Authors: ^*ZHOU Xiaobing (E-mail: zhouxb@ms.xjb.ac.cn)

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The first and second authors contributed equally to this work.

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Cite this article:

ZHANG Shihang, CHEN Yusen, ZHOU Xiaobing, ZHANG Yuanming. Spatial variability characteristics and drivers of surface soil nitrogen fractions in the drylands of northern China. Journal of Arid Land, 2025, 17(11): 1558-1575.

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http://jal.xjegi.com/10.1007/s40333-025-0065-z OR http://jal.xjegi.com/Y2025/V17/I11/1558

Fig. 1 Overview of the drylands in northern China based on the land use/land cover (LULC) data and distribution of sampling sites. Note that the figure is based on the standard map (GS(2021)5453) of the Map Service System (http://bzdt.ch.mnr.gov.cn/), and the standard map has not been modified.

Fig. 2 Flow chart for estimating the TN, IN, and MBN stocks in this study. N, nitrogen; TN, total nitrogen; IN, inorganic nitrogen; MBN, microbial biomass nitrogen; MAT, mean annual temperature; MAP, mean annual precipitation; HI, human impacts; clay, soil clay content; sand, soil sand content; NDVI, Normalized Difference Vegetation Index; XGBoost, eXtreme Gradient Boosting; RF, Random Forest; SVM, Support Vector Machine; R², coefficient of determination; MAE, mean absolute error; RMSE, root mean square error.

Fig. 3 Scatter plots indicating the effectiveness of the RF, XGBoost, and SVM models in predicting MBN (a-c), IN (d-f), and TN (g-i) densities in the drylands of northern China. The gray shading area represents the 95% confidence interval.

Fig. 4 Spatial distribution patterns of soil TN (a), IN (b), and MBN (c) densities in the drylands of northern China. Note that the figure is based on the standard map (GS(2021)5453) of the Map Service System (http://bzdt.ch.mnr.gov.cn/), and the standard map has not been modified.

Table 1 Estimation of densities and stocks of soil nitrogen (N) fractions for different land use/land cover (LULC) types in the drylands of northern China

Fig. 5 Piecewise structural equation modeling (PiecewiseSEM) accounting for the direct and indirect effects of different predictors on TN (a), IN (b), and MBN (c) in the drylands of northern China. The gray arrows indicate negative correlations, and the orange arrows indicate positive correlations. The thickness of the arrow is proportional to the magnitude of standardized path coefficients and indicative of the strength of the relationship. The coefficients adjacent to the climate and soil environmental factors represent the independent effects of each factor. Marginal R² and conditional R² denote the proportion of variance explained by the included predictors without and with accounting for random effects of the 'sampling site', respectively. df, degree of freedom; AIC, Akaike Information Criterion; BIC, Bayesian Information Criterion. ^*, P<0.050 level; ^**, P<0.010 level; ^***, P<0.001 level.

Fig. 6 Variance partitioning analysis (VPA) revealing the relative contributions of different environmental factors to the variations in TN (a), IN (b), and MBN (c) in the drylands of northern China. Only positive relative contributions are shown in the figure. Residual indicates the portion of variation in the target variable that cannot be explained by the model.


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