+ All Categories
Home > Science > Phiri Refining GHG estimates using national household survey data Nov 10 2014

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

Date post: 30-Jun-2015
Category:
Upload: ccafs-cgiar-program-climate-change-agriculture-and-food-security
View: 234 times
Download: 0 times
Share this document with a friend
Description:
Presentation for
23
Innova&ons that decrease the costs of collec&ng biophysical and ac&vity data Working Group A FAO CCAFS – Interna&onal Workshop – Rome, 1012 November Session 1: Perspec&ves for refining GHG es&mates using na&onal household survey data
Transcript
Page 1: 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  

Page 2: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

Page 3: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

Page 4: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

Page 5: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

Page 6: Phiri Refining GHG estimates using national household survey data Nov 10 2014

George  Phiri,  FAO  Malawi  

Perspectives  7om  the  Malawi  Integ;ated  Household  Sur@ey  

Refining  GHG  estimates  using  national  

household  sur@ey  data  

Page 7: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

Page 8: Phiri Refining GHG estimates using national household survey data Nov 10 2014

8  

The  Economics  &  Policy  Innova0ons  for  Climate-­‐Smart  Agriculture  (EPIC)  Programme  in  Malawi  

1.  Introduc&on  

Page 9: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

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

10  

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  

 

 

10  

What  are  the  policy  levers    to  facilitate  adop&on  and  what  will  they  cost?  

Page 11: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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).    

Page 12: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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.  

 

Page 13: Phiri Refining GHG estimates using national household survey data Nov 10 2014

13  

EXISTING  AFOLU  GHG  ESTIMATES  FOR  MALAWI  

2.  Reference  GHG  Assessments  

Page 14: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

Page 15: Phiri Refining GHG estimates using national household survey data Nov 10 2014

15  

IMPROVING  GHG  IN  MALAWI  ESTIMATES  USING  HOUSEHOLD  DATA  

3.  Household  Data  and  GHG  es&mates  in  

Malawi  

Page 16: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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.  

 

Page 17: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

Page 18: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

Page 19: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

Page 20: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  

Page 21: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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.  

Page 22: Phiri Refining GHG estimates using national household survey data Nov 10 2014

22  

Thank  you!  

EPIC  website  www.fao.org/climatechange/epic  

Page 23: Phiri Refining GHG estimates using national household survey data Nov 10 2014

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  


Recommended