Minimal soil carbon model incorporating rhizosphere priming effects

Overview

Soil organic matter (SOM) is the largest terrestrial carbon reservoir, yet current mathematical models used to predict how global soil carbon stocks respond to climate change and land management are overly simplistic. They fail to adequately represent microbial processes, particularly the priming effect, where fresh plant carbon inputs accelerate the turnover of existing soil organic matter. This project develops and tests a minimal soil carbon model that incorporates these rhizosphere priming effects, enabling more accurate predictions of soil carbon dynamics over the long term.

The research addresses a critical gap in Earth system and agricultural modeling by proposing that any minimal model capable of capturing priming behavior must include SOM pool turnover as a non-linear function of rhizodeposition—the release of carbon compounds from living plant roots into surrounding soil. This work is supported by a novel field dataset that provides independent validation of competing model formulations.

Technical specifications

Key features:

• Non-linear priming formulation: Moves beyond conventional first-order kinetics (dSOM/dt = I – k × SOM) by making the turnover rate constant k a non-linear function of fresh organic carbon input
• Stable isotope fractionation methodology: Uses a C4 grass growing in C3 soil to distinguish plant-derived carbon fluxes from existing soil organic matter fluxes
• Depth-resolved field measurements: Simultaneous monitoring of soil temperature, moisture, and other drivers of SOM turnover at multiple depths
• Naturally-structured field soils: Experimental system preserves real-world soil architecture rather than relying on controlled laboratory conditions
• Model-data fusion approach: Systematic testing of alternative model formulations against field observations to identify optimal structures
• Coupling detection: Captures tight coupling between photosynthesis and SOM turnover on both diurnal and seasonal timescales

Technology readiness level

The modeling framework is at an early-to-mid research stage. The underlying field methodology and dataset are well established at Cranfield University, with existing datasets already demonstrating priming effects through tight photosynthesis–SOM turnover coupling. The next phase involves setting up a series of candidate model formulations and systematically testing them against both existing and newly collected field data. The project is positioned to deliver validated model formulations that can be integrated into larger Earth system or agricultural models, with potential applications in climate change prediction, carbon farming, and land management decision support.