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Bonn Climate Conference Side Event: 4 June 2013


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On 4 June the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) convened a side event on "Agriculture and Mitigation: Towards low emissions development" featuring speakers from FAO (Marja-Liisa Tapio Biström), Ugandan Delegation (Moses Tenywa), University of Abderdeeen (Jon Hillier), Unique Forestry and Land Use (Timm Tennigkeit), KIT Germany (Eugenio Diaz-Pines) and University of Edinburgh (Nicholas Berry). The session was chaired by James Kinyangi, Regional Program Leader for CCAFS East Africa. Read more about the event:

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Bonn Climate Conference Side Event: 4 June 2013

  1. 1. 14 June 2013  Official  UNFCCC  side  event:      Agriculture  and  Mi8ga8on:  Towards  low  emissions  development                
  2. 2. Na2onal  integrated  mi2ga2on  planning  in  agriculture  Timm  Tennigkeit,  Bonn,  04.06.2013  SBSTA  side  event  on:    Current  state  of  agriculture  and  mi2ga2on:  NAMAs,  quan2fying  emissions  and  links  to  adapta2on    
  3. 3. ©  UNIQUE  forestry  and  land  use  GmbH  Contents  Agricultural  mi2ga2on  within  UNFCCC  process  Presenta2on  of  key  results  of  the  review  Recommenda2ons        
  4. 4. ©  UNIQUE  forestry  and  land  use  GmbH  Agricultural  mi8ga8on  within  UNFCCC  process    •  SBSTA  agenda  item  9:  “Issues  rela8ng  to  agriculture”  -  No  consent  to  include  adapta2on  &  mi2ga2on  in  a  work  programme  on  agriculture  to  discuss  related  scien2fic  and  technical  issues  -  Technical  discussion  is  overshadowed  by  the  general  discussion  •  22  NAMA  submissions  to  UNFCCC  (from  a  total  of  62  iden2fied)  propose  agricultural  ac2vi2es.    •  21  Low  emission  development  plans  (LEDS)  consider  GHG  mi2ga2on  in  the  agriculture  sector  (from  a  total  of  32  LEDS)    •  Synergies  with  other  development  objec8ves  have  been  considered  in  all  agricultural  mi2ga2on  plans  e.g.  with  increased  food  security;  reduced  deforesta2on;  improved  efficiency  and  trade  compe22veness;  the  promo2on  of  rural  energy  access;  reduced  water  pollu2on;  and  heightened  adapta2on  to  climate  change.    
  5. 5. ©  UNIQUE  forestry  and  land  use  GmbH  Na8onal  development  planning  and  integra8on  of  NAMAs  Na8onal  Development  Strategy  (NDS)  Low  Emissions  Development  Strategy  (LEDS)  NAMAs  Overarching  na8onal    development  framework      •  Na8onal  vision  •  Budget  and  finance  •  Legal  framework    •  Sustainable  Development    Na8onal    Sectoral    Projects    Investment  plans  Na8onal  goals  and  green  growth    •  GHG  inventory  and  scenarios  Alignment  with  NDS  Sectoral  /  Regional  plans  •  Energy,  Transport,  Agriculture,  Forestry    Alignment  with  NDS  Mainstreaming  climate  change     •  Sectoral  approaches  •  Projects  •  Policies  and  strategies  •  Research  and  development  Support  for  implementa8on  Capacity  building  ,  technology  transfer,  climate  finance    MRV  Interna8onal  and  domes8c  NAMA  procedures  &  registry  
  6. 6. ©  UNIQUE  forestry  and  land  use  GmbH  SeWng  priori8es  and  targets  •  Priori2es  and  targets  for  mi2ga2on  plans  are  generally  set  on  the  basis  of  a  combina2on  of  the  exis2ng  policy  framework,  technical  analysis  of  mi2ga2on  op2ons,  and  stakeholder  consulta2ons  •  Sectoral  mi2ga2on  planning  is  a  process  of  gradually  pu`ng  key  enabling  and  technical  elements  in  place  Source:  FAO,  2013  
  7. 7. ©  UNIQUE  forestry  and  land  use  GmbH  Key  elements  of  na8onal  mi8ga8on  planning  and  NAMAs  
  8. 8. ©  UNIQUE  forestry  and  land  use  GmbH  Mi8ga8on  benefits  from  agricultural  NAMAs  
  9. 9. ©  UNIQUE  forestry  and  land  use  GmbH  Quan8fied  Ag.  mi8ga8on  benefits  in  Brazil  and  Ethiopia  
  10. 10. ©  UNIQUE  forestry  and  land  use  GmbH  MRV  Example:  Kenya‘s  agricultural  MRV+  system  •  Kenya  Climate  Change  Ac2on  Plan  defined  MRV+  principles  for  all  sectors  •  MRV+  system  builds  on  exis2ng  monitoring  and  evalua2on  systems  of  central  government  agencies  and  the  na2onal  sta2s2cal  repor2ng  system  
  11. 11. ©  UNIQUE  forestry  and  land  use  GmbH  Cost  &  benefit  analysis  livestock  mi8ga8on  ac8vi8es  in  Mongolia  Estimates were made for sheep, beef and dairy cow breedingprograms, fodder production and milk processing:Breeding programs have significant net benefits, so the abatement costs($/tCO2) are negative. Irrigated fodder production has high investment costand little mitigation potential. Reducing milk losses by small-scale milkprocessing units has lowest abatement cost and high economic benefits.-­‐33   -­‐44   -­‐4  1703  63   -­‐220  -­‐500  0  500  1000  1500  2000  Sheep  breeding  Beef  breeding  Dairy  AI   irrigated  fodder  produc8on  hay  produc8on  Milk  processing  Abatement  costs  ($/tCO2)  
  12. 12. ©  UNIQUE  forestry  and  land  use  GmbH  Recommenda8ons  •  Align  agricultural  mi2ga2on  plans  with  priori2es  in  na2onal  and  sectoral  development  plans  •  Use  a  step-­‐by-­‐step  approach  to  NAMA  development  •  Combine  climate  finance  with  other  sources  of  finance  •  Clarifying  socio-­‐economic  and  policy  dimensions  of  NAMAs  can  help  target  biophysical  research  •   Research  on  barriers  to  adop2on  is  cri2cal    •  Research  contribu2ng  to  design  of  MRV  systems  should  build  on  exis2ng  systems  in  the  agricultural  sector  •  Build  na2onal  research  capaci2es  For  development  partners:  •  Support  phased  readiness  processes  in  the  agricultural  sector  •  Climate  finance  should  support  both  technical  analysis  and  crea2on  of  enabling  condi2ons  
  13. 13. KONTAKT  CONTACT  CONTACTO      UNIQUE  forestry  and  land  use  GmbH  Schnewlinstr.  10  79098  Freiburg,  Germany  Tel:      +49  -­‐  761  20  85  34  -­‐  0  Fax:      +49  -­‐  761  20  85  34  -­‐  10  eduard.merger@unique-­‐  www.unique-­‐    Financed  &  edited  by:  
  14. 14. How  to  determine  which  site-­‐specific  GHG  mi2ga2on  op2ons  give  the  greatest  benefits?  Jon  Hillier  SBSTA,  Bonn,  5th  June  2013  
  15. 15. •  Key  sources  (sinks)  for  carbon  (arable  crops):  –  Biomass  –  above  and  below  ground.  Depends  on  soil  and  climate.  –  Soil  carbon  flux    -­‐  depends  on  soil  and  climate.  –  Nitrous  oxide    -­‐  depends  on  soil  and  climate.  •  One  size  does  not  fit  all!  –  Effec%veness  of  mi%ga%on  op%ons  varies  with  loca%on  •  Can  we  provide  site/region  specific  decision  support?  
  16. 16. COMBINE  FOUR  SIMPLE  MODELS  –  Soil  carbon  flux  •  No-­‐2ll  (IPCC,  Tier  1  method)  •  Increased  carbon  inputs  (IPCC,  Tier  1  method)  *    –  Soil  N2O  •  Depends  on  soil  clay  content,  drainage,  carbon  stock,  climate.  Bouwman  et  al  2002  •  Impact  of  nitrifica2on  inhibitors  –  Emissions  from  fer8liser  produc8on  •  Newer  have  technologies  substan2ally  lower  emissions  (EFMA,  older  and  abated  fer2liser  produc2on  values)  No  factors  for  tropical  climates.  Assumed  effect  as  in  temperate  climates  
  17. 17. •  Provide  simple  screening  method  for  iden2fica2on  of  promising  op2ons  – If  this  is  my  loca2on  and  produc2on  system  what  is  my  most  effec2ve  op2on  in  terms  of  SOC  or  fer2liser  management?  Drainage Climate Soil CNapplicationrateEmissions(kg CO2-equiv)reduce Napplicationrate*use lowemissionsfertiliseremployno-tillincreaseC inputsuseNIs. .. .. .Good Tropical 3-4% 100-150 1343 46% 16% 65% 44% 13%Good Tropical 4-5% 100-150 1469 43% 18% 77% 54% 16%Poor Temperate 0-1% 150-200 1426 29% 30% 9% 11% 12%Poor Temperate 1-2% 150-200 1485 27% 31% 21% 25% 15%. .. .
  18. 18. Reduce  tillageIncrease  C  inputsUse  BATReduce  N  rateUse  soil  inhibitorsN rate > 200 kg/ha/yrN <= 100 kg/ha/yr
  19. 19. Reduce  tillageIncrease  C  inputsUse  BATReduce  N  rateUse  soil  inhibitors150 < N <- 200(kg/ha/yr)100 < N <= 150(kg/ha/yr)
  20. 20. But  N  affects  yield!  
  21. 21. Reduce  tillageIncrease  C  inputsUse  BATReduce  N  rateUse  soil  inhibitors150  <  N  <-­‐  200    (kg/ha/yr)  100  <  N  <=  150  (kg/ha/yr)  With  yield  penalty  applied  
  22. 22. Conclusions  1  •  Effec2veness  of  prac2ces  depends  on  loca2on  •  Good  natural  C  stocks,  or  low  input  system  –  soil  carbon  management  is  best  •  Abated  fer2lisers  is  low  risk,  effec2ve  op2on,  as  are  inhibitors  •  Mi2ga2on  prac2ces  must  consider  the  impact  on  produc2on  –  Best  op2ons  may  be  those  which  increase  produc2on,  e.g.  increased/improved  inputs  or  water  management  
  23. 23. Conclusions  2  •  Other  high  poten2al  mi2ga2on  op2ons  not  included  –  Agroforestry  –  Residue  management  •  Accurate  region  specific  –  N-­‐response  curves  for  a  range  of  crops  to  iden2fy  op2mal  N  for  both  yield  and  GHG  impacts  –  Empirical  emissions  data/meta-­‐models  for  tropical  climates  –  Consistent  datasets  comparing  a  range  of  management  prac2ces,  e.g.  no-­‐2ll  ,  cover  cropping,  agroforestry,  residue  management,  N2O  emissions  
  24. 24. A  system  for  quan2fica2on  of  smallholder  agriculture  GHGs  Marja-­‐Liisa  Tapio-­‐Bistrom  Mi2ga2on  of  climate  change  in  Agriculture  programme  (MICCA)  FAO    
  25. 25. Elements  and  tools  for  mi8ga8on  planning  in  agriculture  •  Data  on  emissions  and  projec2ons  for  a  baseline  •  Mi2ga2on  op2ons  –  LCA  as  a  tool  •  Knowledge  on  farming  prac2ces  •  Emission  factors  •  A  vision  and  means  for    landscape  level  op2ons  for  increasing  the  carbon  content  •  Gree2ngs  from  GHG  quan2fica2on  workshop  •  Food  for  thought  
  26. 26. FAOSTAT  Emissions  from  Agriculture  and  Land  Use  Database  +IPCC  Guidelines  =&    geo-­‐referenced  informa8on  Tier  1,  all  sources  of  emissions  from  agriculture  and  LU,  8me  series  from  1990,  all  countries,  projec8ons  to  2030  and  2050  
  27. 27. Life  Cycle  Analysis  –  iden8fying  mi8ga8on  op8ons  •  LCA  is  an  approach  to  emissions  analysis  which  makes  sense  to  policy  makers,  investors  farmers  since  it  describes  the  system  •  Global  LCA  on  all  livestock  systems  coming  out  soon  (different  intensity  levels,  different  agroecological  zones)    
  28. 28. Emission  intensity  of  milk  in  East  Africa  FAO,  2013  Source:  Global  Environmental  Assessment  Model  (GLEAM) Uganda United Republicof TanzaniaKgCO2eq/kgFPCMCO2, Post-farmgateCO2, Direct andembedded energyFeed CO2Feed N20Manure N20Manure methaneEntericfermentation
  29. 29. Arid HumidKgCO2e/kgFPCMKenya: Grazingsystems0. Arid HumidKenya: Mixed systemsCO2, Direct andembedded energyFeed CO2Feed N20Manure N20Manure methaneEnteric fermentationSource:  Global  Environmental  Assessment  Model  (GLEAM),  FAO,  2013    60%2%6%28%2% 1% 1%Emission  intensity  of  milk  in  Kenya  
  30. 30. Enteric  methane  emissions  at  farm  scale  -­‐  Kaptumo,  Kenya 1000 1500 2000 2500 3000 3500 4000KgEntericCH4perlitremilkLiter of milk per cow per lactationSource:  Based  on  Global  Environmental  Assessment  Model  (GLEAM),  farm  scale    LCA    based  on  Household  data,    Opio  et  al.,  2013  
  31. 31. Enteric  methane  -­‐  improving  feed  use  efficiency  -­‐  Kaptumo,  Kenya 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80KgentericCH4perlitremilkFeed efficiency (litre milk/kg DM intake)Source:  Based  on  Global  Environmental  Assessment  Model  (GLEAM),  farm  scale    LCA    based  on  Household  data,  Opio  et  al.,  2013  
  32. 32. More  analysis  of  farming  prac8ces    -­‐   We  need  rigorous  analysis  of  farming  prac2ces  combining  the  science  and  farmers  experiences  to  develop  climate-­‐smart  prac2ces      -­‐   What  works  ,  where  or  why  not  
  33. 33. Emission  factors      •  Bewer  emission  factors  for  tropical  and  sub-­‐tropical  areas,  major  farming  systems  and  farming  prac2ces  •  A  global  plan,  iden2fying  priority  systems  and  gaps  •  Longer  term  measurements  –  calibra2on  of  models  •  Network  of  research  partners  –  spearheaded  by  CCAFS?  
  34. 34. Maximizing  carbon  content  –  landscape  approach  •  Tap  the  mi2ga2on  poten2al  at  landscape  level  trough  holis2c  par2cipatory  land  use  planning  •  CSA  sourcebook  gives  ideas  how  •  Aboveground  biomass  as  a  proxy?  –stable  or  increasing  J  •  Opportuni2es  for  remote  sensing  –  land  degrada2on  in  grasslands  
  35. 35. Gree8ngs  from  GHG  quan8fica8on  workshop  The  current  systems  are  complex  and  expensive,  not  appropriate  for  most  low-­‐income  countries.  à  We  must  invest  in  crea8ve,  low-­‐cost  systems  for  data  collec8on  and  analysis,  such  as  1.  targe2ng  global  mi2ga2on  priori2es  and  hotspots  (or  key  categories)  in  landscapes  and  farming  systems    2.  combining  modeling,  remote  sensing  and  field  measurements  (crowd-­‐sourcing  and  mobile  technology)  3.  building  on  exis2ng  ac2vity  data  from  other  sources4.  using  consistent,  comparable  methods  and  data  sharing  networks  that  enable  robust  es2mates  for  different  systems  
  36. 36. Food  for  thought  •  How  exact  do  we  need  to  know  the  net  emission  reduc2ons?  •  Depends  on  the  funding  source  –  climate  funding  vs.  agricultural  investments    •  We  need  to  transform  the  way  we  produce  food  –  more  efficient,  more  resilient,  with  mi2ga2on  co-­‐benefit.  
  37. 37. K. Butterbach-Bahl | IMK-IFU | March 2008KIT – die Kooperation vonForschungszentrum Karlsruhe GmbHund Universität Karlsruhe (TH)Standard Assessment of Mitigation Potential andLivelihoods in Smallholder Systems (SAMPLES)Eugenio Díaz-Pinés, Mariana Rufino, Todd Rosenstock, Klaus Butterbach-Bahl,Lini Wollenberg et al.Current state of agriculture and mitigation:NAMAs, quantifying emissions and links to adaptation.June 2013, Bonn, Germany
  38. 38. Institute for Meteorology and Climate Research,IMK-IFU38 6/5/13"   Very few data on mitigation"   Mitigation not linked to livelihoods"   Fragmented and diverse landscapes"   Multi-criteria approaches missingThe concernsDevelop a low-cost protocol to quantifygreenhouse gas emissions and to identifymitigation options for smallholders at whole-farm and landscape levelsThe goal
  39. 39. Institute for Meteorology and Climate Research,IMK-IFU39 6/5/13Landscape analysisand targetingLandscapeimplementationMulti-dimensional evaluationof mitigation optionsScalable and socialacceptable mitigation optionsSystem-level estimationof mitigation potentialSet-up of state-of-the-artlaboratory facilitiesTraining of laboratoryand field staffPhase III:Development of systems-levelmitigation optionsPhase I: Targeting, priority setting and infrastructurePhase II: Data acquisitionCapacitybuildingPhase IV:Implementation withdevelopment partners(UPCOMING)ProductivityassessmentGHGmeasurementsProfitabilityevaluationSocial acceptabilityassessmentJointscientific &stakeholderevaluation
  40. 40. Institute for Meteorology and Climate Research,IMK-IFU40 6/5/13How to identify mitigation options at farm andlandscape level?
  41. 41. Institute for Meteorology and Climate Research,IMK-IFU41 6/5/13Complex landscape: f (m, n, o, p, q)m Landscape unitsn Farm typesLandLivestockOther assetsSources ofincomesp Field typesCharacterisefertility xmanagementPhysicalenvironmentGIS analysis,remotesensing,landusetrendsFoodsecurity,povertylevelsProductivity,GHGemissions,croppreferenceso Common landsq Land types
  42. 42. Institute for Meteorology and Climate Research,IMK-IFU42 6/5/13Landscape units and landusers Nyando, KenyaLandscape analysis and targeting
  43. 43. Institute for Meteorology and Climate Research,IMK-IFU43 6/5/13Targeting and upscaling: fromlandscape to fields and back…
  44. 44. Institute for Meteorology and Climate Research,IMK-IFU44 6/5/13Taking gas samples fromchambersStep 1. Landscape analysisTargeting:-  Landscape units, farm types,field types, soils-  Site selectionSite characterization:-  Soils, crops, biomassInstallation of chamberframesInforming andinterviewing farmersStep 2. Installing measurement stationsStep 3. Measurements applyinggas poolingField work:-  Overcoming spatial variabilityby gas pooling30 Oct 4 Nov 9 Nov 14 Nov 19 Nov 24 Nov 29 Nov0255075100250500N2Oflux[µgNm-2h-1]201202550751002505000255075100250500CroplandGrasslandindividual chambersgas poolingForestTemporal variability of N2Ofluxes at three sites differingin land use at Maseno,Kenya.Arias-Navarro et al., Soil Biol. Biochem., in revision
  45. 45. Institute for Meteorology and Climate Research,IMK-IFU45 6/5/13Lab work:-  Analyzing gas samples (GC)-  Calculating concentrations and fluxesStep 5. Intepretation and upscalingStep 4. Lab analysis and flux calculationsSynthesis of GHG measurements:emission factors, empirical models, calibrating andvalidating of detailed modelsUpscaling: assigning emissions to landscapeelements and/or of GIS coupled biogeochemicalmodels
  46. 46. Institute for Meteorology and Climate Research,IMK-IFU46 6/5/13FarmtypeFieldtypeProfit ($/ha)Production(kg/ha)Emissions(t CO2eqper ha)Emissions(kg CO2 perkg product)Socialacceptability(ranking)1 1 50 500 0.6 1.2 11 2 140 5000 3 0.6 21 3 120 2000 2 1.0 21 4 40 4500 3 0.7 12 1 30 800 0.7 0.9 32 3 180 8000 3 0.4 22 4 250 300 0.5 1.7 1n m Vn,m Wn,m Xn,m Yn,m Zn,mMulti-dimensional assessment of mitigationoptionsTrade-off analysis on multiple dimensions
  47. 47. K. Butterbach-Bahl | IMK-IFU | March 2008KIT – die Kooperation vonForschungszentrum Karlsruhe GmbHund Universität Karlsruhe (TH)Thanks for your
  48. 48. Small-­‐Holder  Agriculture  Mi2ga2on  Benefit  Assessment    Funded  by:  Nicholas  Berry,  Andrew  Cross,  Casey  Ryan  
  49. 49. Greenhouse  gas  accoun2ng  for  different  purposes    Requirements   Data  Carbon  offsets   Precise-­‐or-­‐conserva2ve  es2mate  of  mi2ga2on  Local  measurements  and/or  modelling  Performance-­‐based  finance  Evidence  that  mi2ga2on  targets  have  been  met  Regional  default  values  and  emission  factors  Planning  and  evalua8on   Comparison  between  projects  or  areas  Na2onal  default  values  and  emission  factors  
  50. 50. Activity informationEnvironmental dataSHAMBAEnvironmentaldatasetsBaselineinformationCSA activitiesSite specificbaseline info.Monitoring dataUser definedCSA activitiesEstimates ofGHG emissionreductions andremovalsUser defineddataGHGaccountingmodelsand tools
  51. 51. An  example  for  Malawi    Baseline  •  Conven2onal  maize  Conserva8on  agriculture  •  With  and  without  reduced  2llage  Agroforestry  •  Alley  cropping  •  Intercropping  hwp://  
  52. 52. SHAMBA  tool  (Malawi  demo)  hwp://  SHAMBA  methodology  hwp://    Berry  N.J.  and  Ryan  C.M.  (2013)  Overcoming  the  risk  of  inac2on  from  emissions  uncertainty  in  smallholder  agriculture.  Environmental  Research  Le8ers  8  011003  doi:10.1088/1748-­‐9326/8/1/011003  
  53. 53. 534 June 2013  Official  UNFCCC  side  event:      Agriculture  and  Mi8ga8on:  Towards  low  emissions  development