Modeling soil moisture effects on net nitrogen mineralization in loamy wetland soils

Abstract

Nutrient dynamics in wetland ecosystems are largely controlled by soil moisture content. Therefore, the influence of soil moisture content on N mineralization should be explicitly taken into account in hydro-ecological models. The aim of this research was to establish relationships between N mineralization and soil moisture content in loamy to silty textured soils of floodplain wetlands in central Belgium. Large undisturbed soil cores were taken, incubated for 3 months under various moisture contents, and zero order and first order N mineralization rates were calculated. We used the percentage water-filled pore space (WFPS) as an expression of soil moisture because it is a better index for aeration dependent biological processes than volumetric moisture content or water retention. The relationship between the N mineralization rate and %WFPS was described by a Gaussian model. The optimum WFPS for N mineralization ranged between 57% and 78%, with a mean of 65% +/- 6% WFPS. Expected annual net N mineralization rates at field temperature (9.7 degrees C) and at optimal moisture content varied between 30 and 186 kg N ha(-1) (0-15 cm depth) year(-1), with a mean of 110 +/- 42 kg N ha(-1) (0-15 cm) year(-1). The mean N turnover rate amounted to 2.3 +/- 1.1 g N 100 g(-1) N year(-1). Multiple linear regressions between N mineralization and general soil parameters showed that soil structure has an overriding impact on N mineralization in wetland ecosystems

Soil Nitrogen Transformations and Availability in Upland Pine and Bottomland Alder Forests

“…The results in the UPF corresponded to the general established patterns of N availability in forest soils [12][13][14][15][16]18]. For example, net N transformation rates increased with soil temperature and SWC (Table 2, Figure 2).…”

“…Environmental controls of N mineralization and availability, particularly temperature and soil moisture, have been investigated through a theoretical model [11], literature review [9,12], laboratory experiments [13][14][15], and field manipulations [16][17][18][19]. For example, soil moisture varies significantly in space and time, and affects various biogeochemical processes in different ways [20][21][22].…”

Prediction of Soil Nitrogen Supply in Potato Fields using Soil Temperature and Water Content Information

“…It is known that water (Stanford et al, 1974;Myers et al, 1982;Franzluebbers, 1999;Sleutel et al, 2008) and that the optimum 9 for N mineralization is commonly near field capacity (Linn and Doran, 1984). Although the specific shape of the tesponse of N mineralization rate to 9 is still under debate, a new biophysical watet ftinction which takes into account surface reactions and pore volume filling was recently proposed 201 lb).…”

A biophysical water function to predict the response of soil nitrogen mineralization to soil water content

“…The effect of soil water content on respiration or net soil N mineralization has been described using water content expressed as matric potential (Orchard and Cook, 1983;Sierra, 1997), gravimetric or volumetric water content (Stanford and Epstein, 1974;Wildung et al, 1975), fractions of water holding capacity (Howard and Howard, 1993;Ishaque and Cornfield, 1972), water-filled pore space (Doran et al, 1991;Drury et al, 2003;Franzluebbers, 1999;Sleutel et al, 2008), precipitation indices (Raich and Potter, 1995), depth to water table (Oberbauer et al, 1992), and relative water content between a minimum and a maximum value (Myers et al, 1982;Paul et al, 2003). Several relationships have been proposed for relating N mineralization to soil water content.…”

“…De Neve and Hofman (2002) concluded that simple linear or quadratic functions cannot be used to describe the effect of soil water on net N mineralization over a wide range of water contents. Several researchers have concluded that a logistic or a Gaussian function seems more suitable for deriving a relationship between water content and net N mineralization, and both functions yield parameters that have biological significance (Cassman and Munns, 1980;Gonçalvez and Carlyle, 1994;Reichman et al, 1966;Sleutel et al, 2008). The logistic function parameters are the minimum and maximum net N mineralization rates and the Gaussian parameters are the mineralization rate at optimum soil water content, the optimum soil water content and a moisture-dependence parameter (De Neve and Hofman, 2002).…”