Stochastic models of soil denitrification.

Area of Science:

• Environmental microbiology
• Soil science
• Ecological modeling

Background:

• Soil denitrification is a crucial microbial process with high spatial variability.
• Existing deterministic models fail to capture the lognormal distribution of denitrification rates.
• Understanding this variability is key for accurate ecological assessments.

Purpose of the Study:

• To develop probabilistic (stochastic) models for soil denitrification rates.
• To investigate how combined environmental variables create skewed distributions.
• To identify a model that accurately predicts observed denitrification rate variability.

Main Methods:

• Developed three stochastic models incorporating denitrification enzyme activity and CO2 production.
• Models included second-order, threshold, and saturation functional relationships.
• Estimated model parameters using 12 datasets and validated with 3 independent datasets (180 replicates each).

Main Results:

• Model 2, a second-order model with a threshold, best predicted denitrification rate distributions.
• Model 2 produced distributions statistically similar to measured rates (P > 0.1).
• The model accurately predicted mean rates and captured the process's stochastic nature.

Conclusions:

• Stochastic modeling, specifically Model 2, effectively represents the lognormal distribution of soil denitrification rates.
• This approach accounts for the high spatial variability inherent in soil microbiological processes.
• The methodology may be applicable to other highly variable ecological processes.