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![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](https://image.slidesharecdn.com/richardsgacsajune152016speakerscorner-160615153459/75/Improving-data-on-greenhouse-gas-emissions-and-mitigation-potentials-from-agriculture-16-2048.jpg)
Improving data on greenhouse gas emissions—and mitigation potentials—from agriculture
The document discusses methods for improving data on greenhouse gas emissions and mitigation potentials from agriculture, particularly focusing on precision in estimating emissions while considering various agricultural practices. It emphasizes the need for better data and methodologies, especially in tropical developing countries, to accurately assess greenhouse gas balances and the impact of climate-smart agriculture. The manuscript also highlights the importance of coordinated data platforms and innovative measurement techniques to enhance understanding and estimation of emissions.
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More from CCAFS | CGIAR Research Program on Climate Change, Agriculture and Food Security
Improving data on greenhouse gas emissions—and mitigation potentials—from agriculture
- 1. Meryl Richards CGIAR ResearchProgram on Climate Change, Agriculture and Food Security Improving data on greenhouse gas emissions—and mitigation potentials—from agriculture With contributions from: Todd Rosenstock Lini Wollenberg Klaus Butterbach-Bahl Mariana Rufino and many others
- 2. • How canwe tell when we’re reducing emissions? On the farm In the life cycle of a product At subnational and national scales Many CSA practices and policies have potential to lower emissions from agriculture
- 3. Estimating emissions IPCC 1996and 2006 guidelines Emissions = Activity x Emissions Factor Nitrous oxide = Annual amount of synthetic fertilizer N applied to soils, kg N yr-1 x 1%
- 4. How accurate arethese methods in tropical systems? • Compared EX-ACT and Cool Farm Tool with field measurements of soil fluxes • 9 studies, 8 countries, 51 data points • Maize, rice, vegetable crops, tea, coffee, fodder grass 1. GHG balance of systems (sites, practices) 2. Changes in GHG balance with changes in practice 4 Comparison of GHG calculators with field measurements = ?
- 5. 5Richards et al.2016 1. Greenhouse gas balance
- 6. 2. Change inGHG balance between control and alternative management practices Richards et al. 2016
- 7. Studies of N2O emissionsfrom managed soils in SSA Hickman et al. 2014 Very little data on GHG sources and sinks in tropical developing countries What is needed for better estimates? 1. More data
- 8. What is neededfor better estimates? 2. Use the data we already have Tier 2 emission factors where available Calibration of empirical models with data more representative of tropical developing countries Coordinated data platforms
- 9. Resources to supportbetter estimation www.samples.ccafs.cgiar.org
- 10. • Reducing costof measurement • Handling heterogeneous landscapes • Livelihoods as a primary concern
- 12. Data examples: Fallowand 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
- 13. Data examples: Excretafrom African cattle Source Kg C-CH4 / Head. Year EF N-N2O % IPCC, 2006 0.77 2 Yamluki, 1999 and Yamluki, 1998 0.26 0.53 This study 0.14 (Friesian) 0.026 (Boran) 0.23 (Friesian) 0.53 (Boran) Pelster et al. 2016 • Emissions from manure and urine patches on pasture much lower than IPCC Tier 1
- 14. 14:00 - 14:30 Climate-SmartAgriculture Compendium: The scientific basis of CSA Todd Rosenstock (World Agroforestry Centre)
- 15. Discussion • How doyou use information on GHG emissions and mitigation potentials? • What are your biggest challenges in estimating emissions and emission reductions? • What information do you or your organization need most in order to estimate the mitigation potential of your activities?
- 16. 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
- 17. Innovations in methods:Targeting measurement within landscapes
- 18. Arias-Navarro et al.2013 SBB Innovations in methods: Gas pooling
- 19. 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
- 20. Static chamber forsoil flux measurements Respiration chamber for enteric methane measurement
- 21. Calculator estimates arewithin the range of error in the IPCC 2006 Guidelines 21
- 22. Example: carbon accumulationin existing trees • EX-ACT assumes no biomass C sequestration without land use change • EX-ACT estimate could be improved by using Tier 2 factors (e.g. tree growth rates) -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 Full sun Shade Coffee, Costa Rica (N2O, biomass C, soil C) GHGemissions(tCO2eha-1yr-1) Measured Cool Farm Tool EX-ACT Data: Hergoualc'h et al. 2012 22
- 23. Caveat: Measurements aren’tperfect either • In low-emissions environments, standard errors associated with soil GHG flux measurements may be of nearly the same magnitude as the fluxes themselves. 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 Conventional tillage No-till, legume intercrop No-till, mineral fertilizer No-till, heavy mulch No-till, leguminous trees tCO2eha-1yr-1 Data: Kimaro, A. A. et al. (2015) Nutr. Cycl. Agroecosyst. 23
Editor's Notes
- #3 Ask the people present what methods they have used Write them on the white board IPCC methods GHG calculators Models Explain what calculators are Emphasize that they generally have the same underlying methodology: IPCC guidelines and default emission factors
- #4 At first glance, quantification seems like it should be easy. There are IPCC guidelines, which calculate emissions and removals (e.g. carbon storage) based on activity data (data on the magnitude of human activity that generates emissions or removals) paired with an emission factor, or in some cases, an empirical model. Not only do we not know baseline emissions, we don’t know the potential for emissions reductions from various practices For those who aren’t familiar, emissions are usually quantified by quantifying the amount of a certain activity that takes place (e.g. the area under rice) and multiplying by a factor –emission factor- that relates that activity to the emissions it releases But, in order to do that accurately, you need emission factors specific to a certain activity in a certain location. We don’t have those. In many cases, especially livestock systems, we don’t have information on the magnitude of the activity either (e.g. animals and what they’re eating).
- #5 The issue is that emission factors and empirical models on which IPCC methods, and greenhouse gas calculators, are based were developed mostly from temperate, developed country agriculture, and we don’t know how well they represent tropical, developing country agriculture. So we compared field measurements with estimates from two common GHG calculators.
- #6 Comparison between measured and calculator-predicted soil fluxes for N2O, CH4, and the net balance (CO2e). The solid line is a 1:1 line; data points above this line represent an over-estimation of GHG emissions by the calculator. The dashed line is a 1:2 line; data points above this line represent an overestimation by a factor of 2 or more. CFT tends to overestimate N2O emissions (77% of cases). EX-ACT was only slightly more likely to overestimate as to underestimate emissions (55% of cases). Calculator estimates were within the 95% confidence interval of the measured value in just 30% of cases. Better agreement of CH4 estimates with measured values than N2O N2O highly variable in space and time, drivers not as well quantified as CH4
- #7 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. Calculator predictions and measurements contradicted each other for 41% of cases, indicating that GHG balance changes with the management change were negative when measured but positive when estimated by the calculator, or vice versa. CFT and EX-ACT correctly predicted the direction of change for 60% and 50% of cases, respectively Poor prediction: Combination of practices were used (e.g. change in water management and organic inputs in flooded rice) N2O emissions were so low that differences were barely distinguishable, such as maize cultivation without fertilizer. Good prediction: 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.
- #8 Our current ability improve our understanding of mechanisms driving GHG emissions under tropical conditions is data-limited. Therefore, in the long-term, there is a need for additional data to revise and recalibrate these calculators for tropical systems. We can’t measure everything, but there are a few critical gaps: For example, there are few published studies from Africa on the response function of N2O to N inputs, and few long-term (> 10 years) studies of soil carbon sequestration in the tropics. Data characterizing enteric CH4 emissions from livestock systems are another critical gap; we were unable even to compare calculator-produced estimates with measured emissions due to the lack of published studies with field measurements from tropical developing countries. Considering the dependence of quantification approaches on data and the current data deficit for smallholder systems, it is clear that in situ measurements must be a core part of initial and future strategies to improve GHG inventories and develop mitigation measures for smallholder agriculture. Once more data are available, especially for farming systems of high priority (e.g., those identified through global and regional rankings of emission hotspots or mitigation leverage points), better cumulative estimates and targeted actions will become possible.
- #9 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. N2O estimates would be improved by emission factors adjusted by nitrogen input levels and, perhaps, by additional factors such as nitrogen source, placement, timing as well as soil moisture, plant composition, or soil fauna16,25,26. Calibration of empirical models, such as the N2O model used in the CFT12,25 for conditions more representative of tropical developing countries would likely also improve estimates16. Given the large variation in emission rates, both below 1% of nitrogen applied23 and above 4% of nitrogen input derived from top-down approaches27 in tropical and temperate systems, there is a significant need to better understand the mechanisms involved in GHG evolution from agricultural soils in a way that can be used in simple calculators.
- #11 There are plenty of textbooks and guidelines out there for how to measure GHG emissions
- #12 The data base contains emission factors for practices and systems that aren’t in the IPCC guidelines; for example straw and water management during the rice fallow season, and emission factors for manure management from African cattle
- #19 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.
- #20 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
- #22 IPCC methods aren’t really meant for field-scale estimation anyway