The NZAGRC’s former nitrous oxide and soil carbon work streams were combined into one programme this year. This ensures a strong overall framework, closer communication and full GHG analyses across the programme. The programme focusses on three key areas:
1. Identifying and prioritising plant traits for low GHG emissions;
2. Mitigation practices to maintain soil carbon and reduce nitrous oxide emissions at paddock scale; and
3. Defining the achievable soil carbon stabilisation capacity of New Zealand grassland soils.
Current progress and research stories
Biogeography and biophysicochemical traits link N2O emissions, N2O emission potential and microbial communities across New Zealand pasture soils
Sergio E. Morales, Neha Jha, Surinder Saggar, Biogeography and biophysicochemical traits link NO emissions, NO emission potential and microbial communities across New Zealand pasture soils, Soil Biology and Biochemistry, Volume 82, 2015, Pages 87-98, ISSN 0038-0717, http://dx.doi.org/10.1016/j.soilbio.2014.12.018.
The process of denitrification has been studied for decades, with current evidence suggesting that an ecosystem's ability to produce and emit N2O is controlled both by transient ‘proximal’ regulators (e.g. temperature, moisture, N availability) as well as distal regulators (e.g. soil type, microbial functional diversity, geography). In this study we use New Zealand soils as a model system to test the impact of distal regulators (i.e. geography) on microbial communities and their N2O emission potential. Using gas chromatography, soil chemical analyses, 16S amplicon sequencing, terminal restriction fragment length polymorphism (T-RFLP) and quantitative PCR (qPCR) on three denitrifier functional genes (nirS, nirK and nosZ), we assessed the factors linked to N2O emissions across a latitudinal gradient. Results show that soil drainage class, soil texture class, and latitude were powerful regulators of both emissions and emission end products (N2 vs. N2O). Mixed models demonstrate that a few variables (including latitude, texture class, drainage class and denitrifier community data [abundance and diversity] amongst others) were enough to predict both the amount and type of gas emitted. In addition we show that microbial community composition (based on 16S rRNA gene sequencing) can also be used to predict both the gas species and quantity emitted.