High-yielding maize-based crop systems require maize to take up large quantities of nitrogen over short periods of time. Nitrogen management in conventional crop systems assumes that soil N mineralization alone cannot meet rapid rates of crop N uptake, and thus large pools of inorganic N, typically supplied as fertilizer, are required to meet crop N demand. Net soil N mineralization data support this assumption; net N mineralization rates are typically lower than maize N uptake rates. However, net N mineralization does not fully capture the flux of N from organic to inorganic forms. Gross ammonification may better represent the absolute flux of inorganic N produced by soil N mineralization. Methods Here we utilize a long-term cropping systems experiment in Iowa, USA to compare the peak rate of N accumulation in maize biomass to the rate of inorganic N production through gross ammonification of soil organic N. Results Peak maize N uptake rates averaged 4.4 kg N ha −1 d −1 , while gross ammonification rates over the 0-80 cm depth averaged 23 kg N ha −1 d −1. Gross ammonification was highly stratified, with 63% occurring in the 0-20 cm depth and 37% in the 20-80 cm depth. Neither peak maize N uptake rate nor gross ammonification rate differed significantly among three cropping systems with varied rotation lengths and fertilizer inputs. Conclusions Gross ammonification rate was 3.4 to 4.5 times greater than peak maize N uptake across the cropping systems, indicating that inorganic N mineralized from soil organic matter may be able to satisfy a large portion of crop N demand, and that explicit consideration of gross N mineralization may contribute to development of strategies that reduce crop reliance on large soil inorganic N pools that are easily lost to the environment.
Core Ideas
Gross N mineralization and PMN are related to different SOM properties.
Multiple linear regressions generated predictions of N mineralization that were validated across diverse agroecosystems.
Organic soil amendments consistently increased N mineralization.
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 (R2 > 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.
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