Predicting Gross Nitrogen Mineralization and Potentially Mineralizable Nitrogen using Soil Organic Matter Properties

Gross N mineralization is a fundamental soil process that plays an important role in determining the supply of soil inorganic N, highlighted by recent research demonstrating that plants can effectively compete with microbes for inorganic N. However, predictions of the supply of plant available N from soil have largely neglected gross N mineralization. As soil organic matter (SOM) is the substrate that microbes use in the process of N mineralization, characteristics of SOM fractions that are relatively easy to measure may hold value as predictors of gross N mineralization. To improve understanding of predictive relationships between SOM fraction properties and gross N mineralization, we assessed 32 measures of SOM quality and quantity, including physically, chemically, and biologically defined SOM fractions, for their ability to predict gross N mineralization across a wide range of soil types (Aridisols to Mollisols) and crop management systems (organic vs. inorganic based fertility) in Israel and the United States. We also assessed predictions of a commonly employed indicator of soil N availability, potentially mineralizable N (PMN, determined by 7-d anaerobic incubation). Organic fertility management systems consistently enhanced gross N mineralization and PMN compared with inorganic fertility management systems. While several SOM characteristics were significantly correlated with both gross N mineralization and PMN, other characteristics differed in their relationships with gross N mineralization and PMN, highlighting that these assays are controlled by different factors. Multiple linear regressions (MLR) were utilized to generate N mineralization predictions: five (gross N mineralization) or six (PMN) predictor models explained >80% of the variation in both gross N mineralization and PMN (R-2 > 0.8). The MLR models successfully predicted gross N mineralization and PMN across diverse soil types and management systems, indicating that the relationships were valid across a wide range of diverse agroecosystems. The ability to develop predictive models that apply across diverse soil types can aid soil health assessment and management efforts.