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Phiri Refining GHG estimates using national household survey data Nov 10 2014


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Phiri Refining GHG estimates using national household survey data Nov 10 2014

  1. 1. -­‐ Working Group A -­‐ Innova&ons that decrease the costs of collec&ng biophysical and ac&vity data Session 1: Perspec&ves for refining GHG es&mates using na&onal household survey data FAO CCAFS – Interna&onal Workshop – Rome, 10-­‐12 November
  2. 2. Session 1: Perspec&ves for refining GHG es&mates using na&onal household survey data Session Introduc&on Uwe Grewer, FAO FAO CCAFS – Interna&onal Workshop – Rome, 10-­‐12 November
  3. 3. Modes of u)lizing household survey data for GHG es)ma)ons? • IPCC 2006 NGGI § Household data used in combina&on with the IPCC 2006 Guidelines for Na&onal GHG Inventories (NGGI) • Tier 2 § Allows an increase in the u&liza&on of refined calcula&ons (&er 2) as compared to the most common current prac&ce in non-­‐Annex I countries • Instead an insufficient basis for popula&ng process based models (if used in a tradi&onal way) Session 1: Na,onal household survey data and GHG emission es,mates
  4. 4. Why to use survey data for GHG es)ma)ons? • Availability: § Data already collected for other purposes (na&onal sta&s&cs, livelihood surveys, …) • Scalability: § Na&onal representa&ve • Advanced precision: § Informa&on on land management prac&ces (crop residue use, &llage, soil organic maWer inputs), tree species & tree densi&es, etc. Session 1: Na,onal household survey data and GHG emission es,mates
  5. 5. Main proposes for which the u)liza)on of survey data might be especially useful • Na&onal repor&ng § If sta&s&cal representa&ve data is not yet used § If not all agricultural emission sources and processes are covered that are considered in IPCC-­‐NGGI • NAMA development § Basis for baseline emission scenarios • Na&onal policy priori&es § Iden&fica&on of priority emission sources & mi&ga&on poten&als that can be addressed as part of integrated na&onal policy Session 1: Na,onal household survey data and GHG emission es,mates
  6. 6. George Phiri, FAO Malawi Perspectives 7om the Malawi Integ;ated Household Sur@ey Refining GHG estimates using national household sur@ey data
  7. 7. 7 Outline • Introduc&on § The EPIC Programme • Na&onal AFOLU GHG es&mates in Malawi • GHG es&mates and household data § Adapta&on of IHS § Tier 2 methodology for emission es&mates • Conclusion
  8. 8. 8 1. Introduc&on The Economics & Policy Innova0ons for Climate-­‐Smart Agriculture (EPIC) Programme in Malawi
  9. 9. 9 The EPIC Programme • Being implemented in three countries: Malawi, Zambia and Viet Nam • Quan&ta&ve and qualita&ve analysis of primary and secondary data at household and community level combined with climate and geo-­‐referenced data and with ins&tu&onal data to: § Iden&fy CSA best op&ons in terms of adapta&on but also mi&ga&on and food security (i.e. yield response, cost benefit analysis, mi&ga&on poten&al etc), § Understand barriers to CSA adop&on and their enabling factors § Assess mi&ga&on poten&al as well as costs and benefits of CSA solu&ons as opposed to conven&onal agriculture
  10. 10. 10 Project Framework NEEDS . Develop a policy environment & and agricultural investments to improve food security and provide resilience under climate uncertainty RESEARCH COMPONENT OUTPUTS What are the synergies and tradeoffs between food security, adapta&on and mi&ga&on from ag. prac&ces? What are the barriers to adop&on of CSA prac&ces? Legal & Ins&tu&onal Appraisal: mapping ins&tu&onal rela&onships and iden&fying constraints POLICY SUPPORT COMPONENT Iden&fying where policy coordina&on at the na&onal level is needed and how to do it Facilita&ng na&onal par&cipa&on/inputs to climate and ag interna&onal policy process Evidence Base Strategic Framework & Policy Advice Investment proposals Capacity Building 10 What are the policy levers to facilitate adop&on and what will they cost?
  11. 11. 11 Main achievements in Malawi A number of interes)ng results from the “Evidence Base Analyses” • Various climate related effects over &me and space and nega&ve rela&on with crop produc&vity; • Posi&ve associa&on with the adop&on of adapta&on prac&ces (benefits on both crop yields and food security – resilience); • Higher profitability due to adop&on of CSA prac&ces than to use conven&onal &llage (yields, gross revenue, benefit-­‐cost ra&o).
  12. 12. 12 Other products from the CSA Project • Policy dialogue workshop report; • Climate change and agriculture scenarios for Malawi – 2 workshop reports; • Ins&tu&onal analysis and policy mapping for agriculture and climate change – final report; • Climate-­‐Smart Agriculture Training Manual for Frontline Staff – ready for field pre-­‐tes&ng prior to conduc&ng the actual training; • One MSc completed, 3 almost, and another 3 concluded data collec&on, 1 PhD – course-­‐work completed, finished data collec&on and doing data entry.
  13. 13. 13 2. Reference GHG Assessments EXISTING AFOLU GHG ESTIMATES FOR MALAWI
  14. 14. 14 Exis&ng AFOLU GHG es&mates for Malawi • 2nd Na)onal Communica)on § AFOLU net emissions: 12 961 giga grammes (Gg) CO2-­‐e (2000) § Ac&vity data is procured mainly from na&onal sta&s&cs and complemented by various other available sources § Not all data sources are based on na&onal representa&ve data • FAOSTAT Database § AFOLU net emissions: 8 292 Gg CO2-­‐e (2000), 10 464 Gg CO2-­‐e (2011) § Ac&vity data is procured mainly from na&onal sta&s&cs (reported to FAOSTAT) and selected other interna&onal informa&on sources • Evalua)on § Very good first approach § All data sources should be na&onal representa&ve as far as possible § Pure Tier 1 approach: Progression towards Tier 2 desirable § Not all emission sources are included: o Key issue: Soil and grassland rehabilita&on/degrada&on Introduc&on
  15. 15. 15 3. Household Data and GHG es&mates in Malawi IMPROVING GHG IN MALAWI ESTIMATES USING HOUSEHOLD DATA
  16. 16. 16 Suitability of household data • Na&onal household surveys do not necessarily include most of the mi&ga&on-­‐relevant ques&ons. • Mi&ga&on issues are understandably not the first priority of the data collec&on efforts -­‐ main objec&ve is to provide and update sta&s&cs in MW on poverty, health, educa&on, food security and welfare. • But: High complementarity between climate change adapta&on and mi&ga&on related informa&on.
  17. 17. 17 EPIC Work with the Malawi Integrated Household Survey (HIS) • IHS also func&ons at the same &me as Living Standard Measurement Survey (LSMS) • Review and proposi&ons by the EPIC programme led to the inclusion of addi&onal targeted ques&ons on land management to the IHS (star&ng from IHS 2013) • This includes mainly: – Adop&on of soil and water conserva&on measures – Management of agricultural residues – Detailed &llage prac&ces – Use of cover crops – Tree removals from produc&ve plots -­‐> Improved star&ng point for using the IHS for mi&ga&on assessments
  18. 18. 18 Methodological approach: Towards Tier 2 assessments A) Soil Carbon dynamics on managed cropland – Usually not considered by na&onal communica&ons nor the FAOSTAT GHG database – IPCC NGGI provides an indica&ve methodology for es&ma&ng soil carbon dynamics based on: • Tillage, soil organic maWer inputs, ini&al soil carbon stocks • University of Aberdeen proposed under the EPIC programme: – The use of the Harmonized World Soil Database for ini&al soil carbon stocks – The above outlined IPCC-­‐NGGI default coefficients for impacts from soil organic maWer inputs & &llage
  19. 19. 19 Mi&ga&on poten&als from improved agricultural prac&ces: Single prac)ces Annual mi&ga&on poten&al of low-­‐input maize systems in Malawi Country average • IPCC NGGI predicts that the mi)ga)on ac)ons can have a relevant impact strength • Mi)ga)on ac)ons show spa)ally homogeneous effec)veness
  20. 20. 20 Methodological approach: Towards ,er 2 assessments B) Nitrous oxide emissions on managed cropland – Usually calculated at na&onal level based on total na&onal applica&on rates of synthe&c fer&lizer and animal manure – IPCC NGGI is mainly derived from a database by Stehfest & Bouwman that allows plot specific es&mates of N2O: • N applica&on rate • Soil Carbon, ph & texture • Climate • Crop type • University of Aberdeen proposed under the EPIC programme: – The use of the Harmonized World Soil Database for ini&al soil carbon stocks, ph & soil texture in combina&on with the Stehfest & Bouwman database -­‐> Site specific N2O emission es&mates at plot level
  21. 21. 21 Conclusion • Na&onal representa&ve household data provides following: – May importantly improve the data quality from agricultural ac&vi&es where na&onal representa&ve data is not yet used; – Allows to ship from using na&onal aggregated data to more plot specific es&ma&ons (Tier 2); and – Allows to consider further sources of GHG fluxes that are currently not considered in repor&ng (e.g. soil and grassland carbon dynamics) • EPIC project inten&on: – Deriving approximate mi&ga&on poten&als for future ac&on; and – The proposal outlined here for possible combina&on of household data & Tier 2 methodology for na&onal es&ma&ons as a secondary outcome. • The presented methodology for soil carbon and nitrous oxide is an ini&al approach that will certainly need refinement at a later stage. • Tier 2 es&ma&ons should be validated with targeted field measurements.
  22. 22. 22 Thank you! EPIC website
  23. 23. Discussion Ques)ons • What is the availability of household survey data in your country? – Representa&ve at na&onal level? – Containing specific informa&on relevant for mi&ga&on assessments? • Cropland management prac&ces • Land use change dynamics • Agroforestry tree species and plan&ng densi&es • How do you currently collect and aggregate data for na&onal repor&ng? – Na&onal representa&ve? Specificity of informa&on (see above)? • Are there ini&a&ves that intend to establish baseline emission levels for the agricultural sector (e.g. NAMA)? Which methodology do they use? Session 1: Na,onal household survey data and GHG emission es,mates