摘要

In order to accurately predict N2O emissions from agricultural soils and to develop effective management strategies, it is important to understand mechanisms underlying N2O emissions under field conditions. This involves identification of sources of N2O, which is currently methodologically challenging, especially under field conditions. We assessed the suitability of N-15 tracers and natural abundance N-15 to study N cycling and sources of N2O after a rainfall simulation in an annual cropping system in the Central Valley of California. Our natural abundance N-15 approach differed from other studies due to a combination of emphasizing a per-event (e.g. rainfall simulation in this study) assessment of N2O emissions, applying high temporal sampling frequency during this event, determination of N-15 of NH4+ and NO3- in addition to N2O, and data analysis using isotope models. In our study, the suitability of N-15 tracers to assess N cycling and sources of N2O emissions was limited, likely due to a combination of a fine soil texture, the use of undisturbed soil cores, and a low N-15 application rate. Based on natural abundance N-15, we were able to calculate gross NH4+ mineralization, NH4+ immobilization, nitrification and NO3- immobilization rates of 5.37 +/- 1.72, 2.70 +/- 1.72, 3.01 +/- 1.13 and 0.15 +/- 0.29 mu g N g(-1) soil d(-1), respectively. Natural abundance N-15 was, however, a rather poor predictor of the contribution of nitrification versus denitrification to N2O production. Nevertheless, important trends in N2O reduction rates could be observed, showing a sharp increase from 48% to 78% in reduction of produced N2O between 2 hours and 24 hours after rainfall simulation, followed by a gradual decrease to 46% of reduction by the fifth day after rainfall simulation. We conclude that the natural abundance N-15 approach is very promising to elucidate mechanisms driving N-cycling and N2O emissions during agricultural management or weather events, especially if isotope dynamics are incorporated in site-specific biogeochemical process models.

  • 出版日期2013-1-15