TITLE:
Net Nitrogen Mineralization in Natural Ecosystems across the Conterminous US
AUTHORS:
LeeAnna Y. Chapman, Steven G. McNulty, Ge Sun, Yang Zhang
KEYWORDS:
Nitrogen Limitation; Total Kjeldahl Nitrogen; Forest; Grassland; Linear Regression; Plant Available Nitrogen
JOURNAL NAME:
International Journal of Geosciences,
Vol.4 No.9,
November
29,
2013
ABSTRACT: Nitrogen is the primary nutrient limiting ecosystem productivity over most of the US. Although soil nitrogen content is important, knowledge about its spatial extent at the continental scale is limited. The objective of this study was to estimate net nitrogen mineralization for the conterminous US (CONUS) using an empirical modeling approach by scaling up site level measurements. Net nitrogen mineralization and total soil nitrogen data across the CONUS were obtained from three different ecosystems: low elevation forests, high elevation forests, and grasslands. Equations to predict net nitrogen mineralization were developed through stepwise linear regression using total Kjeldahl nitrogen, air temperature, precipitation, and nitrogen deposition as predictor variables for four categories: low elevation high temperature forests (coefficient of determination, R2 = 0.83), low elevation low temperature forests (R2 = 0.74), high elevation forests (R2 = 0.80), and grasslands (R2 = 0.88). A map of net nitrogen mineralization was developed in GIS using these equations and national-scale databases for the CONUS. The result shows that net nitrogen mineralization varies widely across the US. Grasslands were predicted to have the lowest net nitrogen mineralization, while low elevation forests in the east had the highest. Mean values were 14.3 kg·ha-1·yr-1 for grasslands, 22.6 kg·ha-1·yr-1 for high elevation forests, 58 kg·ha-1·yr-1 for low elevation low temperature forests, and 82.9 kg·ha-1·yr-1 for low elevation high temperature forests. This continental scale estimation of net nitrogen mineralization provides a means of comparing net nitrogen mineralization across regions, and the databases developed from this study are useful for accounting for nitrogen limitations in large scale ecosystem modeling.