Phiri Refining GHG estimates using national household survey data Nov 10 2014
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Transcript of Phiri Refining GHG estimates using national household survey data Nov 10 2014
Innova&ons that decrease the costs of collec&ng biophysical and ac&vity data
-‐ Working Group A -‐
FAO CCAFS – Interna&onal Workshop – Rome, 10-‐12 November
Session 1: Perspec&ves for refining GHG
es&mates using na&onal household survey data
Session 1: Perspec&ves for refining GHG es&mates using
na&onal household survey data
Uwe Grewer, FAO FAO CCAFS – Interna&onal Workshop – Rome, 10-‐12 November
Session Introduc&on
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
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
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
George Phiri, FAO Malawi
Perspectives 7om the Malawi Integ;ated Household Sur@ey
Refining GHG estimates using national
household sur@ey data
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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
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The Economics & Policy Innova0ons for Climate-‐Smart Agriculture (EPIC) Programme in Malawi
1. Introduc&on
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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
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Project Framework
. Develop a policy environment & and agricultural investments to improve food security and provide resilience under climate uncertainty
OUTPUTS RESEARCH COMPONENT NEEDS
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
What are the synergies and tradeoffs between food security, adapta&on and
mi&ga&on from ag. prac&ces?
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
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What are the policy levers to facilitate adop&on and what will they cost?
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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).
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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.
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EXISTING AFOLU GHG ESTIMATES FOR MALAWI
2. Reference GHG Assessments
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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
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IMPROVING GHG IN MALAWI ESTIMATES USING HOUSEHOLD DATA
3. Household Data and GHG es&mates in
Malawi
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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.
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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
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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
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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
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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
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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.
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Thank you!
EPIC website www.fao.org/climatechange/epic
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