
%0 Journal Article
%A Paul, K. I. 
%A Polglase, P. J. 
%A O'Connell, A. M. 
%A Carlyle, J. C. 
%A Smethurst, P. J. 
%A Khanna, P. K. 
%T Soil nitrogen availability predictor (SNAP): a simple model for predicting mineralisation of nitrogen in forest soils
%J Soil Research
%D 2002/09/04
%V 40
%N 6
%P 1011-1026
%K basal, mineralisation, potential, seasonal, temperature, water.
%R https://doi.org/10.1071/SR01114
%U https://www.publish.csiro.au/paper/SR01114 
%X A new empirical model (SNAP) combines a simple laboratory measurement of the basal rate of N mineralisation with the modifying effects of daily temperature and water content to predict seasonal and annual rates of mineralisation of forest soils. Short-term (20-60-day) aerobic incubations of either undisturbed or bulked and mixed soil were found suitable for prediction of the basal rate of N mineralisation. Data from laboratory incubations of a range of soils were used to calibrate empirical relationships describing the effects of temperature (Tm) and water (Wm) on rates of N mineralisation. Submodels for predicting daily average temperature (STUF) and water content (SWUF) for up to 3 surface soil layers were developed and used to provide inputs to the Tm and Wm functions, respectively. Inputs required for SNAP are restricted to variables whose values are easily obtained. In addition to the amount of N mineralised during a short aerobic laboratory incubation, other soil properties required are bulk density, gravel and clay content, and upper and lower limits of soil water content. Climatic data required included daily air temperature, rainfall, and solar radiation. Other inputs are slope, leaf area index of the stand, and approximate mass and height of litter. Predicted rates of N mineralisation have been verified using data from 9 native forests, 12 radiata pine plantations, and 12 eucalypt plantations from across southern Australia. Despite the wide range of forest types, soil types, climatic regions, and management systems, predicted annual rates of N mineralisation were in close agreement with those observed in the field, regardless of whether daily soil temperature and water content were predicted (R2 = 0.76, P < 0.001, n = 127) or observed (R2 = 0.78, P < 0.001, n = 68). Sensitivity analysis showed that it was most important to minimise analytical error in inputs used to calculate the basal rate of N mineralisation (i.e. soil temperature, water content, and N mineralised during laboratory incubation). The model was more sensitive to daily soil temperature than to daily soil water content. 
