A meta-analysis to examine whether nitrification inhibitors work through selectively inhibiting ammonia-oxidizing
Jilin Lei1, Qianyi Fan1, Jingyao Yu1
1College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.
Abstract:
Nitrification inhibitor (NI) is often claimed to be efficient in mitigating nitrogen (N) losses from agricultural production systems by slowing down nitrification. Increasing evidence suggests that ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) have the genetic potential to produce nitrous oxide (N2O) and perform the first step of nitrification, but their contribution to N2O and nitrification remains unclear. Furthermore, both AOA and AOB are probably targets for NIs, but a quantitative synthesis is lacking to identify the "indicator microbe" as the best predictor of NI efficiency under different environmental conditions. In this present study, a meta-analysis to assess the response characteristics of AOB and AOA to NI application was conducted and the relationship between NI efficiency and the AOA and AOB amoA genes response under different conditions was evaluated. The dataset consisted of 48 papers (214 observations). This study showed that NIs on average reduced 58.1% of N2O emissions and increased 71.4% of soil concentrations, respectively. When 3, 4-dimethylpyrazole phosphate (DMPP) was applied with both organic and inorganic fertilizers in alkaline medium soils, it had higher efficacy of decreasing N2O emissions than in acidic soils. The abundance of AOB amoA genes was dramatically reduced by about 50% with NI application in most soil types. Decrease in N2O emissions with NI addition was significantly correlated with AOB changes (R 2 = 0.135, n = 110, P < 0.01) rather than changes in AOA, and there was an obvious correlation between the changes in concentration and AOB amoA gene abundance after NI application (R 2 = 0.037, n = 136, P = 0.014). The results indicated the principal role of AOB in nitrification, furthermore, AOB would be the best predictor of NI efficiency.
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