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Background and aims <p>Traditional assessments of soil fertility and health often rely on physicochemical properties, with insufficient attention paid to the rapid and sensitive responses of biological processes. Here, we aimed to establish a concise panel of biological indicators that can reliably discriminate soil fertility status across major cropping systems in Shaanxi Province, China.</p> Methods <p>Five major crop types were selected across four distinct ecological regions—the Northern Sandy Area, the Weibei Dry Plateau, the Guanzhong Plain, and the Southern Qinling Mountains. Fertility levels were classified using fuzzy membership functions based on soil physicochemical properties and crop yields. Soil biological properties were assessed at different fertility levels. The key biological indicators of soil fertility differentiation were identified through clustering, correlation, and random forest analyses.</p> Results <p>Microbial physiological activity and enzyme activity were enhanced in high-fertility plots than in low-fertility plots (e.g., by 52.2–156.4% for soil nitrification intensity) and were positively correlated with soil nutrient contents. High-fertility plots showed greater microbial alpha-diversity (Shannon–Wiener index), with increased proportions of 12 genus-level biomarkers (e.g., <i>Saccharomonospora</i>, <i>Massilia</i>, <i>Mesorhizobium</i>). Two biomarker clusters were significantly correlated with soil nutrient contents, microbial physiological activity, and enzyme activity. The biomarker network in high-fertility plots exhibited higher complexity and stability (positive edges: 82.2%) compared to that of low-fertility plots (70.6%).</p> Conclusions <p>Twenty key biological indicators representing microbial physiological, enzymatic, and taxonomic responses effectively distinguished between high- and low-fertility plots. This study provides a calibrated set of metrics for fertility-oriented soil monitoring in (semi)arid agricultural areas of Shaanxi Province and, with local benchmarking, in ecologically comparable regions.</p>

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Assessing soil biological indicators across a fertility gradient in agricultural areas of Shaanxi Province, China

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Zirong Kong,
  • Meilin Zhang,
  • Yingying Jiang,
  • Kerong Fan,
  • Yulong Li,
  • Qiao Guo,
  • Hangxian Lai

摘要

Background and aims

Traditional assessments of soil fertility and health often rely on physicochemical properties, with insufficient attention paid to the rapid and sensitive responses of biological processes. Here, we aimed to establish a concise panel of biological indicators that can reliably discriminate soil fertility status across major cropping systems in Shaanxi Province, China.

Methods

Five major crop types were selected across four distinct ecological regions—the Northern Sandy Area, the Weibei Dry Plateau, the Guanzhong Plain, and the Southern Qinling Mountains. Fertility levels were classified using fuzzy membership functions based on soil physicochemical properties and crop yields. Soil biological properties were assessed at different fertility levels. The key biological indicators of soil fertility differentiation were identified through clustering, correlation, and random forest analyses.

Results

Microbial physiological activity and enzyme activity were enhanced in high-fertility plots than in low-fertility plots (e.g., by 52.2–156.4% for soil nitrification intensity) and were positively correlated with soil nutrient contents. High-fertility plots showed greater microbial alpha-diversity (Shannon–Wiener index), with increased proportions of 12 genus-level biomarkers (e.g., Saccharomonospora, Massilia, Mesorhizobium). Two biomarker clusters were significantly correlated with soil nutrient contents, microbial physiological activity, and enzyme activity. The biomarker network in high-fertility plots exhibited higher complexity and stability (positive edges: 82.2%) compared to that of low-fertility plots (70.6%).

Conclusions

Twenty key biological indicators representing microbial physiological, enzymatic, and taxonomic responses effectively distinguished between high- and low-fertility plots. This study provides a calibrated set of metrics for fertility-oriented soil monitoring in (semi)arid agricultural areas of Shaanxi Province and, with local benchmarking, in ecologically comparable regions.