Potential Net Nitrification Rates Research Articles

Gross nitrogen transformations in tropical pasture soils as affected by Urochloa genotypes differing in biological nitrification inhibition (BNI) capacity

Several tropical grasses, particularly Urochloa humidicola (Rendle) Schweick.) (Syn. Brachiaria humidicola), have been associated with low soil nitrate content and reduced nitrogen (N) losses from pasture systems. Previous studies have detected that root exudates of certain Urochloa genotypes can inhibit ammonium oxidation in in vitro bioassay with Nitrosomonas and reduce net nitrification in soils, a phenomenon termed biological nitrification (BNI). However, net nitrification rates reflect the result of several N transformation processes that co-occur and together determine changes in NO3− concentrations in soils. Thus, to better understand the mechanisms underlying BNI, gross rates of nitrification, ammonification and inorganic N immobilization need to be assessed. In this study we used, for the first time, stable isotopes techniques to disentangle the different gross N transformation processes that determine net nitrification rates in soil of Urochloa genotypes differing in BNI. Intact soil cores were collected from two experimental sites in Colombia under different Urochloa genotypes, classified as low-BNI and high-BNI based on net nitrification rates determined in previous studies. Soil cores were labeled with (15NH4)2SO4 and K15NO3 to quantify gross N ammonification and nitrification, respectively and calculate the immobilization rates. Our results do not confirm lower gross nitrification rates under the high-BNI genotypes, despite reduced potential net nitrification rates and lower abundance of ammonia oxidising organisms, hence not supporting the widely accepted mechanism of BNI. Instead, the low potential nitrification rates could be explained by increased inorganic N immobilization under high-BNI genotypes, both in NH4+-N and NO3−-N form. The lack of differences might be explained by a strong N-limitation in our experiment, unlike previous BNI studies when large amounts of fertilizers were applied. Our results suggest that a combination of different mechanisms, particularly stimulation of N immobilization may be responsible for the BNI capacity observed as low NO3− soil content and reduced N losses.

Disturbance Decouples Biogeochemical Cycles Across Forests of the Southeastern US

Biogeochemical cycles are inherently linked through the stoichiometric demands of the organisms that cycle the elements. Landscape disturbance can alter element availability and thus the rates of biogeochemical cycling. Nitrification is a fundamental biogeochemical process positively related to plant productivity and nitrogen loss from soils to aquatic systems, and the rate of nitrification is sensitive to both carbon and nitrogen availability. Yet how these controls influence nitrification rates at the landscape scale is not fully elucidated. We, therefore, sampled ten watersheds with different disturbance histories in the southern Appalachian Mountains to examine effects on potential net nitrification rates. Using linear mixed model selection (AIC), we narrowed a broad suite of putative explanatory variables into a set of models that best explained landscape patterns in potential net nitrification. Forest disturbance history determined whether nitrification and nitrogen mineralization were correlated, with the effect apparently mediated by microbially available carbon. Undisturbed forests had higher available carbon, which uncoupled potential net nitrification from potential net nitrogen mineralization. In contrast, disturbed watersheds had lower available carbon, and nitrification rates were strongly correlated to those of nitrogen mineralization. These data suggest that a history of disturbance at the landscape scale reduces soil carbon availability, which increases ammonium availability to nitrifiers at the micro-scale. Landscape-level soil carbon availability then appears to determine the coupling of autotrophic (nitrification) and heterotrophic (nitrogen mineralization) biogeochemical processes, and hence the relationship between carbon and nitrogen cycling in soils.

Spatial patterns of soil nitrification and nitrate export from forested headwaters in the northeastern United States

Nitrogen export from small forested watersheds is known to be affected by N deposition but with high regional variability. We studied 10 headwater catchments in the northeastern United States across a gradient of N deposition (5.4 − 9.4 kg ha−1 yr−1) to determine if soil nitrification rates could explain differences in stream water NO3− export. Average annual export of two years (October 2002 through September 2004) varied from 0.1 kg NO3−‐N ha−1 yr−1 at Cone Pond watershed in New Hampshire to 5.1 kg ha−1 yr−1 at Buck Creek South in the western Adirondack Mountains of New York. Potential net nitrification rates and relative nitrification (fraction of inorganic N as NO3−) were measured in Oa or A soil horizons at 21–130 sampling points throughout each watershed. Stream NO3− export was positively related to nitrification rates (r2 = 0.34, p = 0.04) and the relative nitrification (r2 = 0.37, p = 0.04). These relationships were much improved by restricting consideration to the 6 watersheds with a higher number of rate measurements (59–130) taken in transects parallel to the streams (r2 of 0.84 and 0.70 for the nitrification rate and relative nitrification, respectively). Potential nitrification rates were also a better predictor of NO3− export when data were limited to either the 6 sampling points closest to the watershed outlet (r2 = 0.75) or sampling points <250 m from the watershed outlet (r2 = 0.68). The basal area of conifer species at the sampling plots was negatively related to NO3−export. These spatial relationships found here suggest a strong influence of near‐stream and near‐watershed‐outlet soils on measured stream NO3− export.

Soil nitrification in a large forested watershed, Ranch Brook (Vermont), mirrors patterns in smaller northeastern USA catchments

Soil nitrification rates in northeastern USA forested ecosystems appear to be regulated by a number of factors and are likely influenced by continuing N deposition. Among other factors, rates across small watersheds have been found to relate to the soil C/N ratio and tree species composition. We measured potential net nitrification rates in the Ranch Brook watershed, a relatively large (9.6 km 2) forested basin in north central Vermont, to determine if relationships found in smaller catchments were evident at a larger scale. The stream network was divided into eight reaches to determine the variability within the watershed. Sampling points (6–15 along each reach, total of 74) were established along transects that paralleled the major watershed tributaries. At each point, we measured net rates of nitrification and ammonification in the uppermost humified soil horizon (Oa or A), using a one-day lab incubation. The basal area and density of all tree species were measured in a 10-m radius plot, along with a number of topographic metrics such as slope, aspect and elevation. In a stepwise regression, 39% of the variability in net nitrification rates was explained by the density of Picea rubens, elevation and the thickness of the forest floor. When net nitrification rates were normalized to soil C concentration, 60% of the variability was explained by soil N concentration, C/N ratio, elevation and the density of P. rubens. The significant negative influence of P. rubens density, and not basal area, was consistent with a previous cross-site study of 10 smaller northeastern USA watersheds. No influence of sugar maple basal area or density was found. Other relationships, similar to those found in smaller watersheds, were net nitrification rates predicted by the fraction of inorganic N as NO 3 - , net nitrification predicted by the C/N ratio and the C/N ratio predicted by tree species. The consistent influence of tree species on potential net nitrification rates demonstrates a role for future forest management in influencing ecosystem processes.