Standard Assessment of Agricultural Mitigation Potential and Livelihoods our common future 2015
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Presentation by Meryl Richards, Science Officer in the Low Emissions Agriculture flagship program at CCAFS, on a new framework for better GHG emissions monitoring.
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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
- 5. Innovations in methods: Targeting measurement within landscapes Rufino et al. 2015
- 6. Arias-Navarro et al. 2013 Innovations in methods: Gas pooling
- 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
- 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
- 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
- 17. Cumulative CH4 emissions 0 2 4 6 8 10 CF AWD tCO2-eq/ha*season Bulacan 1 5.3 1.8 -66% 0 2 4 6 8 10 CF AWD tCO2-eq/ha*season Bulacan 2 7.8 1.8 -77% 0 2 4 6 8 10 CF AWD tCO2-eq/ha*season Tarlac -70% 3.7 1.1 0 2 4 6 8 10 CF AWD tCO2-eq/ha*season N E -65%8.6 3.0 Findings: Alternate wetting and drying in paddy rice Sander et al. unpublished
- 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
Notas del editor
- 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)