Abstract
The study examines how six soil organic matter models respond to changes in their parameters while simulating the decomposition of liquid manure, solid manure and a low-nitrogen crop residue.
Two model choices shape the analysis: the mathematical kinetics used to calculate decomposition rates and the treatment of nitrogen shortage during carbon decomposition. The study compares first-order, Monod and reverse-Monod kinetics. Each is paired with either nitrogen inhibition, which reduces decomposition when nitrogen is insufficient, or carbon overflow, which directs excess carbon into a polysaccharide storage pool.
Sensitivity was assessed for simulated carbon dioxide emissions and soil mineral nitrogen over a 189-day laboratory incubation with constant water content and temperature. The three organic inputs had progressively higher carbon-to-nitrogen ratios, allowing the models to be examined under contrasting substrate conditions.
Parameters that mattered most
A relatively small group of parameters and interactions accounted for consistent changes in model output. The influential terms were mainly connected with microbial biomass, the distribution of added carbon among input pools and the decomposition constants assigned to those pools. In models using Monod kinetics, carbon dioxide output was also responsive to changes in half-saturation constants.
Nitrogen shortage and stored carbon
When nitrogen became limiting, the carbon-overflow approach allowed more pool decomposition than the nitrogen-inhibition approach. Carbon transferred to the polysaccharide pool then decomposed slowly, making model outputs comparatively insensitive to changes in that pool’s decay constant.
Comparison of kinetic approaches
Across the six-month simulation, classical Monod models generally produced more organic matter decomposition than first-order models, while reverse-Monod models produced less. This pattern reflected the way microbial biomass growth influenced decomposition rates. Monod models also represented positive priming of native organic matter after amendment, with the effect related to microbial biomass growth and the decomposability of the added material.
Conclusion
The analysis indicates that calibration can concentrate on the limited set of parameters that meaningfully affect carbon dioxide and mineral-nitrogen predictions. This narrower focus can make model fitting more efficient while retaining attention to the processes most responsible for output variation.