quantifying greenhouse gas emissions from managed and natural soils
DESCRIPTION
Presented by Klaus Butterbach-Bahl, Bjoern Ole Sander, David Pelster and Eugenio Diaz-Pines at the FAO‐CCAFS Workshop on Reducing the Costs of GHG Estimates in Agriculture to Inform Low Emissions Development, Rome, 10-12 November 2014TRANSCRIPT
Quan&fying greenhouse gas emissions from managed and natural soils
Klaus Bu(erbach-‐Bahl1,2, Bjoern Ole Sander3, David Pelster1, Eugenio Díaz-‐Pinés2
Rome, Reducing the costs of GHG es&mates in agriculture to inform low emissions
development, FAO-‐CCAFS Workshop, November 10-‐12, 2014
1Interna(onal Livestock Research Ins(tute, Kenya; 2Karlsruhe Ins(tute of Technology, Germany; 3Interna(onal Rice Research Ins(tute, Phillipines; 4The University of Western Australia, Australia
Agricultural GHG emissions and developing countries
• Agriculture is responsible for 47 and 84% of anthropogenic CH4 and N2O emission, respec@vely (Smith et al. 2007) • But these es@mates are based on studies in Europe / N America / Australia
• Importance of smallholder farms (e.g. in SSA) • 75% of agricultural produc@on and 75% of job produc@on in SSA (Africa Development Bank, 2010)
• 80% of farms in SSA < 2 ha (FAO 2010) • Yield are very low (~1 Mg ha-‐1)
GHG emissions and underlying mechanisms
Emission = produc@on (microbial/ chemical) – consump@on (microbial/ chemical) BuZ
erbach-‐Bahl et al, 2013, Phil. Trans. R. Soc.
GHG emissions processes and measuring techniques
BuZerbach-‐Bahl et al, 2013, Phil. Trans. R. Soc.
Drivers of soil GHG emissions
Turner et al. 2008, Plant & Soil Van Beek et al. 2010, Nutr. Cycl Agroecosys.
• Soil proper@es and soil environmental condi@ons • Agricultural management (e.g. fer@liza@on, irriga@on, residue management…)
• Microbe-‐plant interac@ons and microbial diversity • ……..
Advantages of chamber techniques
Plus • Simple, low cost, „easy“ to apply • Allows studying of management effects • Can be established elsewhere • Existence of protocolls (e.g. USDA, GRA)
Minus • Change in soil environmental condi@ons • Spa@al and temporal variability • Accuracy of measurements • ….
Chamber techniques – general points
Chamber techniques – chamber placement
Chamber techniques – chamber placement
Chamber techniques – spa&al variability
Arias-‐Navarro et al., 2013, Soil Biol. Biochem.
Chamber techniques – temporal variability Barton et al., 2014, in prep.
OVERALL OBJECTIVE
Investigate the effect of sample frequency on estimates of annual N2O fluxes, using published data collected:
• On a sub-daily basis using automated chamber systems
• From a variety of climates and land-uses Measuring soil N2O emissions from a cropped
soil using chambers. Photo: Graeme Schwenke, NSW, Australia
Chamber techniques – temporal variability APPROACH
Daily fluxes by averaging sub-‐daily fluxes (removed diurnal varia0on)
Annual fluxes at different sampling frequencies
Propor@on of ‘daily’ annual flux es@mated by each sample frequency = % devia0on of ‘daily’ annual flux
For each data set, we calculated:
Barton et al., 2014, in prep.
Chamber techniques – temporal variability
9%
Measurement frequency0 5 10 15 20 25 30
% D
evia
tion
of a
nnua
l flu
x
-50
0
50
100
150
200
250
300
350
SAMPLING FREQUENCY & ANNUAL FLUX: ‘Highly’ episodic
Steppe grassland, semi-‐arid climate, Inner Mongolia
Barton et al., 2014, in prep.
Chamber techniques – temporal variability
9%
Barton et al., 2014, in prep.
Measurement frequency0 7 14 21 28
Num
ber o
f dat
a-se
ts
0
5
10
15
20
25Within 10%Within 20% Within 30%
8%
RECOMMENDED SAMPLING FREQUENCY Annual flux within 10%, 20% and 30%
Chamber techniques – data processing
9%
Chamber techniques – auxiliary measurements and repor&ng
9%
Summary
9%
• Measurements are needed, not only GHG fluxes, but also auxilliary data
• Chamber techniques are best suited to address the diversity of systems in developing countries, but
• hierachical approach should be considered (very detailed, detailed, basic)
• Piralls at every step, QA/ QC is essen@al • Targe@ng is needed, to close gaps in knowledge