Short-term increases in relative nitrification rates due to trenching in forest floor and mineral soil horizons of different forest types

The representation of NO3- dynamics within forest growth simulation models could improve forest management. An extensive literature review revealed an 88% probability of measuring a higher relative nitrification index (i.e. RNI = [NO3-] divided by [NO3- + NH4+]) in mineral soil horizons than in forest floors, across a wide range of conifer and hardwood ecosystems. We then hypothesised that humus form and fine root density could be used as two crude variables to predict changes in in situ, potential and relative nitrification rates. Twenty-seven trench plots were established in 1999, across nine contrasting hardwood and coniferous stands in the Eastern Townships of Quebec. Forest floor and mineral soil samples were collected from each plot, and from a 1 m radius surrounding each plot, on three dates during summer 2000. In situ RNI values increased significantly in trench plots as the season progressed. Potential nitrification rates (i.e. NO3- concentrations following incubation) were two orders of magnitude higher in forest floor than in mineral soil samples. RNI was significantly higher in mineral soil than in forest floor samples after incubations, but the relative increase in RNI due to trenching was higher in forest floor samples. Indices of available C were significantly higher in forest floor than mineral soil samples, and decreased only in forest floor samples during incubations. Likewise, trenching significantly reduced available C in forest floor samples only. Seasonal changes in soil temperature and fine root growth were the most plausible explanations for seasonal changes in NO3- dynamics, whereas other factors such soil acidity and moisture appeared less important in determining NO3- dynamics in this study. We conclude that crude assessments of humus form and fine root density have the potential to be used as calibration parameters for the simulation of NO3- dynamics in forest growth and yield models.