Agricultural greenhouse gas calculators overestimate fluxes in tropical farming systems Poster presented by Meryl Breton Richards at the 3rd Global Science Conference on Climate-Smart Agriculture in Montpellier. Read more: http://ccafs.cgiar.org/3rd-global-science-conference-%E2%80%9Cclimate-smart-agriculture-2015%E2%80%9D#.VRurLUesXX4

1. We compared GHG fluxes and C stock change estimates from two commonly used
agricultural GHG calculators against measured fluxes and stock changes from tropical
developing countries.
Agricultural greenhouse gas calculators overestimate fluxes in tropical farming systems
Figure 3 Experimental GHG measurements used in this study (n=49)
Flooded rice Coffee Napier grass Tea Vegetables Maize Cassava
Cambodia6 (n=8) Costa Rica9 (n=2) Kenya10 (n=1) Kenya10 (n=2) Kenya10 (n=2) Mexico11 (n=4) Tanzania10 (n=1)
China7 (n=6) Tanzania10 (n=7)
Vietnam8 (n= 10) Zimbabwe12 (n=6)
Meryl Richardsa,b*, Ruth Metzelc, Ngonidzashe Chirindad, Proyuth Lye, George Nyamadzawof, Quynh Duong Vug, Andreas de Neergaardi, Myles Oelofsei , Eva Wollenberga,b, Emma Kellerj, Daniella Malink, Jørgen E Olesenl, Jonathan Hillierm, Todd Rosenstockn**
Both Cool Farm Tool (CFT) and Ex-Ante Carbon-Balance Tool (EX-ACT) consistently
overestimated GHG emissions balance (Figure 1).
! Overestimation was due primarily to N2O estimates.
! 23% of estimates were within the 95% confidence interval of the measured value for
both tools.
! GHG calculators should be used with caution in tropical, low input systems. In our
analysis, calculators overestimated N2O emissions and incorrectly predicted emissions
changes nearly half the time.
! Inaccuracy is due not of the construction of the calculators themselves, but to the
underlying models and emission factors from which they have been constructed—the same
as are often used for national GHG accounting.
! Improving the accuracy of GHG calculators requires calibration of the underlying factors
and models to the environmental conditions and systems common in tropical agriculture.
! In the long term, there is a need for additional data to revise and recalibrate these tools for
such conditions. In the short term, IPCC-approved Tier 2 emission factors based on
currently available data may help provide a more reasonable picture of both current fluxes
and mitigation potential.
1Bernoux, M., Branca, G., Carro, A. & Lipper, L. Ex-ante greenhouse gas balance of agriculture and forestry development programs. Sci. Agric. 31–40 (2010).
2Hillier, J. et al. A farm-focused calculator for emissions from crop and livestock production. Environ. Model. Softw. 26, 1070–1078 (2011).
3Keller, E. et al. Footprinting farms: a comparison of three GHG calculators. Greenh. Gas Meas. Manag. 1–34 (2014).
4The Gold Standard. Climate Smart Agriculture: Cool Farm Tool to Calculate Gold Standard Credits for Smallholders. (2014). Accessible at
www.goldstandard.org/climate-smart-agriculture-cool-farm-tool-to-calculate-gold-standard-credits-for-smallholders
5Rosenstock, T. S., Rufino, M. C. & Wollenberg, E. Toward a protocol for quantifying the greenhouse gas balance and identifying mitigation options in smallholder farming systems. Environ. Res. Lett. 021003,
(2013).
6Ly, P., Jensen, L. S., Bruun, T. B. & de Neergaard, A. Methane (CH4) and nitrous oxide (N2O) emissions from the system of rice intensification (SRI) under a rain-fed lowland rice ecosystem in Cambodia. Nutr.
Cycl. Agroecosystems 97, 13–27 (2013).
7Qin, Y., Liu, S., Guo, Y., Liu, Q. & Zou, J. Methane and nitrous oxide emissions from organic and conventional rice cropping systems in Southeast China. Biol. Fertil. Soils 46, 825–834 (2010).
8Pandey, A. et al. Organic matter and water management strategies to reduce methane and nitrous oxide emissions from rice paddies in Vietnam. Agric. Ecosyst. Environ. 196, 137–146 (2014).
9Hergoualc’h, K., Blanchart, E., Skiba, U., Hénault, C. & Harmand, J.-M. Changes in carbon stock and greenhouse gas balance in a coffee (Coffea arabica) monoculture versus an agroforestry system with Inga
densiflora, in Costa Rica. Agric. Ecosyst. & Environ. 148, 102–110 (2012).
10Rosenstock et al. Forthcoming
11Dendooven, L. et al. Greenhouse gas emissions under conservation agriculture compared to traditional cultivation of maize in the central highlands of Mexico. Sci. Total Environ. 431, 237–44 (2012).
12Nyamadzawo, G. et al. Combining organic and inorganic nitrogen fertilisation reduces N2O emissions from cereal crops: a comparative analysis of China and Zimbabwe. Mitig. Adapt. Strateg. Glob. Chang. (2014).
Objective
Results: Magnitude of emissions
Methods
Conclusions
Agricultural greenhouse gas (GHG) calculators are tools to estimate fluxes of nitrous oxide
(N2O) from soils, carbon (C) sequestration in above and belowground biomass, and methane
(CH4) production from flooded rice cultivation and other farm activities.
What are agricultural GHG calculators?
We asked two questions:
1. Can the tools predict the magnitude of emissions from a given system?
2. Can the tools predict the relative change in GHG emissions
associated with a change in management, such as increased fertilizer
use or reduced tillage?
Why?
! GHG calculators are increasingly being used to quantify and verify
emission reductions from climate-smart agriculture practices3,4,
however, the emission factors and empirical models used in GHG
calculators rely on data largely from temperate, developed countries
and their precision and accuracy in tropical developing countries is
unknown5.
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0
5
10
15
0 2 4 6
Measured t CO2−eq ha−1
yr−1
PredictedtCO2−eqha−1
yr−1
● CFT
EX−ACT
Figure 1 Comparison between
measured and calculator-predicted
GHG balances.
Dashed line is a 1:2 line; data points
above this line represent an
overestimation by a factor of 2 or more.
Predicted emissions exceed twice those
obtained through field measurements
in: 45% of CFT cases and 41% of EX-
ACT cases
Solid line is a 1:1 line; data points above
this line represent an over-estimation of
GHG emissions by the calculator.
! They are often web or Microsoft excel-based, and less
time- and knowledge-intensive than in-situ measurement
or process-based models.
! Two examples are Ex-Ante Carbon-Balance Tool (EX-
ACT)1 and Cool Farm Tool (CFT)2, tested in this
study.
! They use ‘activity data’—data on the magnitude of
human activity that generates emissions or removals—
combined with empirical models and emission factors
(IPCC Tier 1).
! The scientific basis underlying GHG calculators is
largely the same as that used for national reporting to
the UNFCCC.
= ?
! We compiled GHG emissions data from field experiments in tropical farming
systems and compared them with predicted emissions from two of the most
commonly used GHG calculators, EX-ACT1 and Cool Farm Tool2 (Figure 3).
! 8 studies, 8 countries, 7 farming systems
! Because much of the available measured data generally did not calculate whole-
farm GHG balances, we used only selected modules from the GHG calculators
for comparison.
References
a CGIAR Research Program on Climate Change, Agriculture and Food Security,
Copenhagen, Denmark
b Gund Institute of Ecological Economics, University of Vermont, Burlington VT, USA
c Yale School of Management & School of Forestry and Environmental Studies, New
Haven CT, USA
d International Center for Tropical Agriculture, Cali, Colombia
e United Nations Development Programme, Phnom Penh, Cambodia
f Department of Soil Science and Agricultural Engineering, University of Zimbabwe,
Harare, Zimbabwe
g Institute for Agricultural Environment, Vietnamese Academy of Agricultural Sciences,
Hanoi, Vietnam
Author affiliations
CFT and EX-ACT correctly predicted the direction of change for 64% and 50% of cases,
respectively (Figure 2).
! Correct predictions: relatively well-understood effects on emissions, such as increased
use of mineral nitrogen fertilizer.
! Incorrect predictions: multiple interacting practices, such as a change in water
management and organic inputs in flooded rice.
Results: Emission changes with management changes
Figure 2 Change in GHG balance between
control and alternative management
practices.
Upper right quadrant: calculator and field
measurement both predicted an increase in
emissions.
Lower left quadrant: calculator and field
measurement both predicted a decrease in
emissions.
Lower right and upper left quadrants:
calculator predicted the opposite direction
of change as observed in the field study.
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0
50
100
−50 0 50
Measured % change
Predicted%change
● CFT
EX−ACT
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