From Microbial Functions to Measurable Indicators: A Framework for Predicting Grassland Productivity and Stability
Abstract
1. Introduction
2. Regulatory Factors of Grassland Productivity
2.1. Climatic Factors: The Ultimate Environmental Filters
2.2. Soil Nutrient Availability: The Elemental Building Blocks
2.3. Anthropogenic Disturbances: The Overlying Human Imprint
2.4. The Unexplained Variance and the Path Forward
3. Core Mechanisms of Plant–Microbe Interactions in Regulating Grassland Productivity
3.1. Nutrient Cycling and Resource-Use Efficiency: The Microbial Metabolic Engine
3.1.1. Nitrogen (N) Cycle
3.1.2. Phosphorus (P) Cycle
3.1.3. Carbon (C) Cycle
3.2. Mutualism and Symbiosis: Forging Strategic Alliances
3.2.1. Mycorrhizal Fungi: The Extended Root System
3.2.2. Plant Growth-Promoting Rhizobacteria (PGPR) and Endophytes: The Bio-Stimulants and Bodyguards
- (i)
- (ii)
- Phytohormone modulation: PGPR that produce auxins (IAA), cytokinins, and gibberellins can enhance root length and branching by about 20–40%, thereby improving water and nutrient uptake [113].
- (iii)
- (iv)
- Stress tolerance amelioration: By synthesizing osmoprotectants (proline, glycine betaine) and antioxidants, PGPR and endophytes enhance drought and heat tolerance. Epichloë in cool-season grasses produces alkaloids that deter herbivores and reduce evapotranspiration losses [116].
3.2.3. The Carbon Cost of Mutualism: A Dynamic Trade-Off
3.3. Pathogenic and Saprotrophic Effects: The Antagonistic and Competitive Forces
3.3.1. Pathogenic Effects: The Regulators of Plant Fitness and Diversity
3.3.2. Saprotrophic Effects: The Decomposers and Competitors
3.4. Plant Functional Trait–Microbe Feedbacks: Shaping the Ecosystem from the Bottom Up
3.4.1. Root Traits as the Primary Filter for the Rhizosphere Microbiome
3.4.2. Leaf Traits and the Afterlife Effects on Decomposition
3.4.3. Biodiversity-Stability Feedback Loops
3.5. Interactions at the Community and Network Level: The Emergence of System-Level Properties
3.5.1. Network Complexity as a Predictor of Ecosystem Stability
3.5.2. Microbial Indicators as Proxies for Network Function and Productivity Trends
4. Regional and Global Case Studies: Context-Dependent Manifestations of Plant-Microbe Interactions
4.1. Chinese Grasslands: A Natural Laboratory Along Environmental Gradients
4.1.1. Qinghai–Tibet Plateau (Alpine Meadow): Cold and Nitrogen-Limited Dynamics
4.1.2. Inner Mongolia Steppe (Temperate Arid and Semi-Arid): Water-Limited Resilience
4.1.3. Xinjiang Desert Grassland (Hyper-Arid): Survival Under Multiple Stresses
4.2. Global Comparative Analysis: Contrasting Responses to Global Change Drivers
4.2.1. North American Great Plains (Temperate Prairie): CUE and Management Interactions
4.2.2. Pampas of Argentina (Subtropical Grassland): Functional Simplification Under Intensive Conversion
4.2.3. African Sahel (Tropical Dry Savanna): Degradation Feedback Loops and Fire Interactions
4.2.4. Australian Drylands (Arid and Semi-Arid): Phosphorus Constraints and Partner Specificity
4.3. Synthesis: Universal Principles and Contextual Divergences
5. Methodology and Research Approaches
5.1. Multi-Omics and Isotopic Tracing
5.2. Experimental and Observational Frameworks
5.3. Data Integration, Modeling, and Scaling
5.4. Methodological Integration Framework
6. Implications for Application and Management
6.1. Microbial-Driven Grassland Restoration and Rehabilitation
6.2. Carbon Sequestration and the “Dual Carbon” Strategy
6.3. Enhancing Productivity for Livestock and Food Security
6.4. Grassland Health and Socioeconomic Benefits
7. Frontiers and Challenges
7.1. From Correlation to Causal Mechanisms
7.2. Multi-Factor Interactions Under Global Change
7.3. Microbial Tipping Points and Grassland Resilience
7.4. Cross-Scale and Cross-System Integration
7.5. Towards a Microbially Explicit Productivity Framework
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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| Indicator | What It Captures | Recent Quantitative/Directional Evidence |
|---|---|---|
| Microbial CUE | Balance of biomass production vs. respiration; predictor of SOC retention | Global analyses: microbial CUE is a dominant control on SOC storage: a 2% increase in CUE can raise global SOC by ~10%, and CUE is at least four times as important as other evaluated factors (carbon inputs, decomposition rates, etc.) in explaining SOC variation [99,189]. |
| AMF status (diversity/abundance) | Strength of mutualist network | A global meta-analysis nutrient enrichment significantly reduced AMF diversity (by 12–20%) and abundance, with root colonization, spore density, and extrarhioidal hyphae decreasing by approximately 18–25%, 15–30%, and 20–40%, respectively. This negative effect was further exacerbated with longer trial durations and higher MAT and MAP [190]. |
| nifH abundance | Proxy for biological N fixation capacity | In multiple grassland fertilization experiments, especially under relatively high N inputs, N addition often reduces diazotrophic nifH gene abundance by ~30–50% [191]. However, a recent grassland meta-analysis found no consistent overall response of N-fixation genes to global change factors, suggesting strong Environmental dependence [192]. |
| Phosphatase activity | P-mineralization potential, P stress signal | Phosphatase activity rises under P limitation and declines with P addition: meta-analyses show monoesterase activity increases by ~+70% under low P but decreases by ~−20% with inorganic P fertilization [193,194] |
| Microbial network complexity (connectance/modularity) | Community robustness, multifunctionality under stress | Long-term warming experiments in grasslands show consistent losses of soil microbial biodiversity, with bacterial, fungal and protistan richness decreasing by ~8–15% under experimental warming [195], and global meta-analyses reporting average declines of 2–4% across multiple global change drivers [196]. |
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Yang, Y.; Zhang, X.; Du, X.; Fan, Y.; Gao, J. From Microbial Functions to Measurable Indicators: A Framework for Predicting Grassland Productivity and Stability. Agronomy 2025, 15, 2765. https://doi.org/10.3390/agronomy15122765
Yang Y, Zhang X, Du X, Fan Y, Gao J. From Microbial Functions to Measurable Indicators: A Framework for Predicting Grassland Productivity and Stability. Agronomy. 2025; 15(12):2765. https://doi.org/10.3390/agronomy15122765
Chicago/Turabian StyleYang, Yishu, Xing Zhang, Xiaoxuan Du, Yuchuan Fan, and Jie Gao. 2025. "From Microbial Functions to Measurable Indicators: A Framework for Predicting Grassland Productivity and Stability" Agronomy 15, no. 12: 2765. https://doi.org/10.3390/agronomy15122765
APA StyleYang, Y., Zhang, X., Du, X., Fan, Y., & Gao, J. (2025). From Microbial Functions to Measurable Indicators: A Framework for Predicting Grassland Productivity and Stability. Agronomy, 15(12), 2765. https://doi.org/10.3390/agronomy15122765

