GLOBAL NITROUS OXIDE DATABASE FOR IMPROVED ANALYSIS AND EXTRAPOLATION

DORICH, C.D1_{., CONANT, R.T}1_{., GRACE, P}2_{., SNOW, V}3_{., VOGELER, I}4_{., VAN DER WEERDEN, T}3_{., ALBANITO, F}5_.

1 _{Natural Resource Ecology Lab, Colorado State University, USA;} 2_{Queensland University of Technology, Australia;} 3_{AgResearch, New}

Zealand; 4_{Plant and Food Research, New Zealand;} 5_{Institute of Biological and Environmental Science, University of Aberdeen, UK}

INTRODUCTION

Nitrous oxide emissions are notoriously variable – through time, across space, and with management and

environmental conditions. Time and budgetary constraints force compromises in N2O emission sampling extent,

frequency, and duration with field measurements of N2O emissions typically carried out only during the growing

season and at a roughly bi-weekly time scale. Given the episodic nature of N2O emissions, spotty measurements

can easily result in unreliable N2O estimates, especially when field measurements are extrapolated to annual

values (Reeves and Wang, 2015; Barton et al., 2015). Sampling and data analysis have therefore proven difficult and techniques used for gap filling missing data and extrapolating measurements have been largely inadequate. Further, previous analysis efforts primarily focus on examining a single site and research project being maintained by a research group, with methods not carrying over between sites. Proper extrapolation and gap filling techniques are needed in order to better estimate fluxes in lieu of missing measurement data. However, studies

of ‘gap filling’ and timing of N2O emissions are limited in the literature (Mishurov and Kiely, 2011; Reeves and

Wang, 2015; Barton et al., 2015). This makes N2O emissions an important research area as N2O is a potent

greenhouse gas (GHG) with agriculture representing the largest source of N2O emissions. There is substantial

interest in N2O reduction from agriculture as a part of the global climate change mitigation strategy (Paustian et

al., 2016), yet methods for measuring or estimating N2O emissions remain highly uncertain. Intensification of crop

production, at least in part through increased fertilizer inputs, is essential for feeding a growing and hungry planet.

In order to address concerns around N2O emissions, improve methods and estimation techniques, and set up

mitigation pathways and potential markets, we need a more comprehensive research analysis to better

understand N2O emissions. We are creating the first global N2O database in collaboration with researchers from

around the world in order to use advanced statistics to analyze data across sites and improve the research

communities understanding of N2O.

MATERIAL AND METHODS

Publically available data sets, like that found in Albanito et al (2017), will be combined with national level databases like GRACEnet to form the first global data set. The design and implementation procedures for this new, publically available database will be based on stakeholder feedback during a planning workshop held within the year. The database will be used for statistical analysis of auto chamber and flux tower data in order to develop

gap-filling methods and to improve extrapolation techniques for making annual estimates of annual N2O

emissions. Analysis will be conducted on a calibration data set and then validated against independent data. The

N2O emission database will be housed on an interactive website created and hosted at Colorado State University.

Using the global database we intend to pursue a variety of methods, including; gap filling, extrapolation, Bayesian,

mixed models and more in order to fully utilize the available N2O data and develop a better understanding of

emissions.

RESULTS AND DISCUSSION

Given the current practice of extrapolating measurement data to an annual value using a simple average or linear interpolation between measurements, the accuracy of field measurements is questionable (Reeves and Wang,

2015). Comparing empirical N2O equation estimates to these field estimated annual N2O fluxes extrapolated from

more reliable estimate of N2O emissions if sampling and analysis is done properly (Reeves and Wang, 2015). If

done improperly, using a simple average from field data or extrapolating from these emissions can potentially skew the result, either due to a high volume of sampling around peak emissions or the lack thereof. A global database will also allow for a thorough review of management practices and nitrogen inputs to further examine

emission factors (EF) and our understanding of N2O emissions. Reeves and Wang (2015) examined one site in

Australia with three years of auto chamber data, comparing continuous data to various simulated sampling campaigns. Their analysis examined sampling from a tri-weekly basis up to fortnightly, with other scenarios also ‘chasing peaks’ by sampling around large (>20 mm) rainfall events on top of the routine sampling. As shown in Erreur ! Source du renvoi introuvable.1, triweekly sampling resulted in the lowest deviation from field emissions. This comes as no surprise as it is the closest sampling structure to the timing of auto chamber sampling, and thus the most complete dataset. However, weekly measurements with biweekly or triweekly measurements around large rainfall events resulted in reliable results as well (Reeves and Wang, 2015).

Figure 1. Static and auto chamber measured N2O emissions compared at various typical sampling regime protocols. Taken

from Reeves and Wang, 2015, Figure 5

CONCLUSION

This project will close the gap by examining global N2O sites to determine better techniques for gap filling data,

improved extrapolation techniques, provide more accurate methods for estimating annual emissions, and

allowing proper comparison of N2O methodologies to field emissions. We believe the database and resulting work

will lead to a better understanding of N2O emissions, new pathways for future research, and improved

methodologies and estimation techniques. If you are interested in participating in this exercise or providing data please contact us.

REFERENCES

Albanito, F., Lebender, U., Cornulier, T., Sapkota, T.B., Brentrup, F., Stirling, C., and Hillier, J., 2017. Direct nitrous oxide emissions from tropical and sub-tropical agricultural systems – a review and modelling of emission factors. Nature, Scientific Reports, 7:44235.

Barton, L., Wolf, B., Rowlings, D., Scheer, C., Kiese, R., Grace, P., Stefanova, K., and Butterbach-Bahl, K., 2015. Sampling frequency affects estimates of annual nitrous oxide fluxes. Nature Scientific Reports 5:15912.

Mishurov, M., and Kiely, G., 2011. Gap-filling techniques for the annual sums of nitrous oxide fluxes. Agricultural and Forest Meteorology, 151, 1763-1767.

Paustian, K., Lehmann, J., Ogle, S., Reay, D., Robertson, G.P., and Smith, P., 2016. Climate-smart soils. Nature Perspectives 532, 49-57.

Reeves, S., and Wang, W., 2015. Optimum sampling time and frequency for measuring N2O emissions from a rain-