Presentation by Meryl Richards, Science Officer in the Low Emissions Agriculture flagship program at CCAFS, on a new framework for better GHG emissions monitoring.
Standard Assessment of Agricultural Mitigation Potential and Livelihoods our common future 2015
1. Cost-effective guidelines for measurement of
agricultural greenhouse gas emissions and removals
Meryl Richards, Ngonidzashe Chirinda, Klaus Butterbach-Bahl, John Goopy, Ivan Ortiz-
Monasterio, Todd Rosenstock, Mariana Rufino, B Ole Sander, Tek Sapkota, Lini Wollenberg
2. Studies of N2O
emissions from
managed soils
in SSA
Hickman et al. 2014
Virtually no data on
GHG sources and
sinks in tropical
developing
countries
The problem: Lack of data, high uncertainty
Field measurements of N2O
Laboratory measurements of N2O
3. The problem: Lack of data, high uncertainty
Richards et al. 2015
Estimated and
measured changes
in GHG emissions
between control
and alternative
management
practices do not
agree
4. Hotspots of emissions
and mitigation potential
Herold et al., Wednesday 16:30
Parallel session 2218: Land-
based mitigation
UNESCO Fontenoy - Room IX
samples.ccafs.cgiar.org
Robust, standard
methods that reduce
cost of producing data
Emission factors,
models calibrated for
priority systems
7. Innovations in methods: Using diameter
only for tree biomass measurements
To save resources on tree
measurements:
• Allometric equations for trees
on farms can be based solely
on diameter at breast height
• Sampling strategy should
capture the range of tree
sizes found in the landscape
• Future indirect quantification
should focus on diameter at
breast height
Kuyah & Rosenstock 2015
8.
9. Findings: Fallow and straw management
in paddy rice
• Methane (CH4) emissions strongly influenced by fallow
and straw management
• Soil drying between rice crops in the tropics can reduce
CH4 emissions during the subsequent rice crop
Sander et al. 2014
0
500
1000
1500
2000
Flooded Dry Dry + tillage Dry and wet
gCO2e/m-2
With residue
Without residue
a
c
y
c
b
y
x
y
10. Findings: Soil N2O from fertilizer
applicationTesting the non-linearity of N2O emissions
from wheat with N rate above the optimum for
yield
Will provide N2O emission factors for Mexico
(Ortiz-Monasterio et al., forthcoming)
11. Findings: Emission factors for livestock
Source Kg CH4-C / Head. Year EF N2O-N %
IPCC, 2006 0.77 2
Yamluki, 1999 &
Yamluki, 1998
0.26 0.53
SAMPLES trial
0.14 (Friesian)
0.026 (Boran)
0.23 (Friesian)
0.53 (Boran)
Comparison of cumulative emissions and
emission factors for manure management
Butterbach-Bahl, Pelster, Goopy preliminary data
12.
13. Conclusions
• Some systems, sources and practices relatively
well-understood (e.g. CH4 changes with water
management in paddy rice)
• Others less so:
Priorities for data: N2O emissions from tropical soils, CH4
from livestock systems
Priorities for methods: Enteric methane, soil C
monitoring methods, activity data, calibration of models
Standard methods, coordinated data platforms
needed
15. References
• Arias-Navarro C, Díaz-Pinés E, Kieseb R, Rosenstock TS, Rufino MC, Stern D, Neufeldt H, Verchot
LV, Butterbach-Bahl K. (2013) Gas pooling: a sampling technique to overcome spatial heterogeneity
of soil carbon dioxide and nitrous oxide fluxes. Soil Biology and Biochemistry 67: 20-23.
• Hickman JE, Scholes RJ, Rosenstock TS, et al (2014) Assessing non-CO2 climate-forcing emissions
and mitigation in sub-Saharan Africa. Curr Opin Environ Sustain 9-10:65–72. doi:
10.1016/j.cosust.2014.07.010
• Kuyah S, Rosenstock TS (2015) Optimal measurement strategies for aboveground tree biomass in
agricultural landscapes. Agrofor Syst 89:125–133. doi: 10.1007/s10457-014-9747-9
• Richards M, Metzel R, Chirinda N, Ly P, Nyamadzawo G, Duong Vu Q, de Neergaard A, Oelefse M,
Wollenberg E, Keller E, Malin D, Olesen JE, Hillier J, Rosenstock TS (2015) Limits of greenhouse
gas calculators to predict soil fluxes in tropical agriculture. Submitted to Sci. Rep.
• Sander BO, Samson M, Buresh RJ (2014) Methane and nitrous oxide emissions from flooded rice
fields as affected by water and straw management between rice crops. Geoderma 235-236:355–362.
doi: 10.1016/j.geoderma.2014.07.020
• Smith P, Bustamante M, Ahammad H, et al (2014) Agriculture, Forestry and Other Land Use
(AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III
to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer O,
Pichs-Madruga R, Sokona Y, et al. (eds.)]. Cambridge University Press, Cambridge, United Kingdom
and New York, NY, USA.
• Van Vuuren DP, Stehfest E, den Elzen MGJ, et al (2011) RCP2.6: Exploring the possibility to keep
global mean temperature increase below 2°C. Clim Change 109:95–116. doi: 10.1007/s10584-011-
0152-3
16. Findings: Tillage and crop establishment
in rice-wheat systems
Tillage Crop
establishment
kg CH4-C ha-1 yr-1 kg N2O-N ha-1 yr-1
Conventional
tillage
Puddling,
transplanting
20.83 1.83
Zero till, residue
removed
Direct seeding 0.54 2.05
Zero till residue
left on field
Direct seeding 3.98 2.65
18. Rochette and Eriksen-Hamil 2008
60% of 360 studies of N2O emissions were inadequate to have
confidence in results
The problem: Validity of data
19. Arias-Navarro et al. 2013 SBB
Research
constraints
Development of new context specific methods
Analytical capacity in the lab
Small-scale spatial heterogeneity
20. Why measure and monitor emissions from
agriculture?
van Vuuren et al. 2011
21. Why measure and monitor emissions from
agriculture in developing countries?
Smith et al. 2014
GtCO2e/year
Editor's Notes
Red cross: laboratory measurements
Red circle: field measurements+
~60% of global N2O budget
20 studies, primary cause is fertilizer
This lack of data means that common estimation methods, such as IPCC equations and empirical models, have to rely on data primarily from temperate, developed countries, and their accuracy and precision in tropical developing countries is questionable.
Change in GHG balance between control and alternative management practices (e.g. continuous flooding vs. multiple drainage in rice). Points in the upper right and lower left quadrants represent cases where the calculator predicted the same direction of change as observed in the field study. Points in the lower right and upper left quadrants represent cases where the calculator predicted the opposite direction of change as observed in the field study.
The tools correctly predicted the direction of change for a little over half the data points
The calculators were less able to predict directional changes when a combination of practices was used (in particular a change in water management and organic inputs in flooded rice) or where N2O emissions were so low that differences were barely distinguishable, such as maize cultivation without fertilizer. On the other hand, the calculators predicted the direction of change correctly for practice changes with relatively well-understood effects on emissions, such as differing levels of mineral nitrogen fertilizer or intermittent drainage of flooded rice with no change in organic inputs.
Sample pooling technique collects a composite gas sample from several chambers instead of the conventional practise of analyzing samples from chambers individually, thus reducing numbers of gas samples. Similar to pooling soil samples. Reduces lab analysis cost, a major limitation for GHG measurement of soil fluxes.
Top figure: Accuracy vs. financial implication: the mean relative error (error %) of equations derived from a limited number of trees but applied to all 72 trees and the cost of sampling trees of different sizes. The trees are ordered by increasing diameter at breast height.
Bottom figure: Scatter plot of aboveground biomass against diameter at breast height for the 72 trees harvested in western Kenya
-Methods, in the field, in the laboratory have a significant impact on the results
-And there is a large variation in the type of methods used
-This study reviewed 360 chamber measurement of N2O and found nearly 60% made insufficiently rigorous measurement
What we do know is a lot about measuring emissions
We know a lot about the appropriate methods. This chart is referring to soils but an equivalent one could be animals using
Stoichiometry relationships
Sulfur hexafloride tracers
Respiration chambers
Their advantages and disadvantages
Annual GHG emissions (mainly CH4 and N2O) from agricultural production in 2000─2010 were estimated at 5.0─5.8 GtCO2eq/yr, comprising about 10─12% of global anthropogenic emissions. Annual GHG flux from land use and land‐use change activities accounted for approximately 4.3─5.5 GtCO2eq/yr, or about 9─11% of total anthropogenic greenhouse gas emissions. The total contribution of the AFOLU sector to anthropogenic emissions is therefore around one quarter of the global anthropogenic total.
a) Total anthropogenic GHG emissions (GtCO2e yr-1) by economic sectors and country income groups. Emissions from agriculture, forestry, and other land use (AFOLU) represent 20-24% of emissions globally, but an even greater percentage in low and lower-middle income countries.
b) Emissions from agriculture, forestry, and other land use (AFOLU) for the last four decades. AFOLU emissions decreased overall in the last decade, but crop and livestock agriculture became the dominant AFOLU emission source
CCAFS estimates that smallholder farming in the developing world contributes about 1/3 of agricultural emissions (1.7 GtCO2e/yr) and 1/3 of emissions from deforestation due to agriculture (0.8 GtCO2e/yr) (2010). Smallholder agricultural emissions alone account for ~ 3.4% of total global emissions—four times the agricultural emissions of the EU or US.
Agriculture in tropical developing countries produces about 7-9% of annual anthropogenic greenhouse gas (GHG) emissions and contributes to additional emissions through land use change (Smith et al. 2014)
Mitigation activities in agriculture are likely to be in developing countries as nearly 70% of the technical mitigation potential in the agricultural sector occurs in these countries (Smith et al. 2008)