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14 June 2013
	
  
Official	
  UNFCCC	
  side	
  event:	
  	
  
	
  
Agriculture	
  and	
  Mi8ga8on:	
  
Towards	
  low	
  emissions	
  
development	
  
	
  
	
  
	
  
	
  
	
  
	
  
	
  
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	
  
	
  
©	
  UNIQUE	
  forestry	
  and	
  land	
  use	
  GmbH	
  
Contents	
  
Agricultural	
  mi2ga2on	
  within	
  
UNFCCC	
  process	
  
Presenta2on	
  of	
  key	
  results	
  of	
  
the	
  review	
  
Recommenda2ons	
  
	
  
	
  
	
  
©	
  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.	
  
	
  
©	
  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	
  
©	
  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	
  
©	
  UNIQUE	
  forestry	
  and	
  land	
  use	
  GmbH	
  
Key	
  elements	
  of	
  na8onal	
  mi8ga8on	
  planning	
  and	
  NAMAs	
  
©	
  UNIQUE	
  forestry	
  and	
  land	
  use	
  GmbH	
  
Mi8ga8on	
  benefits	
  from	
  agricultural	
  NAMAs	
  
©	
  UNIQUE	
  forestry	
  and	
  land	
  use	
  GmbH	
  
Quan8fied	
  Ag.	
  mi8ga8on	
  benefits	
  in	
  Brazil	
  and	
  Ethiopia	
  
©	
  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	
  
©	
  UNIQUE	
  forestry	
  and	
  land	
  use	
  GmbH	
  
Cost	
  &	
  benefit	
  analysis	
  livestock	
  mi8ga8on	
  ac8vi8es	
  in	
  Mongolia	
  
Estimates were made for sheep, beef and dairy cow breeding
programs, fodder production and milk processing:
Breeding programs have significant net benefits, so the abatement costs
($/tCO2) are negative. Irrigated fodder production has high investment cost
and little mitigation potential. Reducing milk losses by small-scale milk
processing 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)	
  
©	
  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	
  
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-­‐landuse.de	
  
www.unique-­‐landuse.de	
  
	
  
Financed	
  &	
  
edited	
  by:	
  
How	
  to	
  determine	
  which	
  site-­‐
specific	
  GHG	
  mi2ga2on	
  op2ons	
  give	
  
the	
  greatest	
  benefits?	
  
Jon	
  Hillier	
  
SBSTA,	
  Bonn,	
  5th	
  June	
  2013	
  
•  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?	
  
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	
  
•  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 C
N
application
rate
Emissions
(kg CO2-
equiv)
reduce N
application
rate*
use low
emissions
fertiliser
employ
no-till
increase
C inputs
use
NIs
. .
. .
. .
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%
. .
. .
Reduce	
  tillage
Increase	
  C	
  inputs
Use	
  BAT
Reduce	
  N	
  rate
Use	
  soil	
  inhibitors
N rate > 200 kg/ha/yr
N <= 100 kg/ha/yr
Reduce	
  tillage
Increase	
  C	
  inputs
Use	
  BAT
Reduce	
  N	
  rate
Use	
  soil	
  inhibitors
150 < N <- 200
(kg/ha/yr)
100 < N <= 150
(kg/ha/yr)
But	
  N	
  affects	
  yield!	
  
Reduce	
  tillage
Increase	
  C	
  inputs
Use	
  BAT
Reduce	
  N	
  rate
Use	
  soil	
  inhibitors
150	
  <	
  N	
  <-­‐	
  200	
  	
  
(kg/ha/yr)	
  
100	
  <	
  N	
  <=	
  150	
  
(kg/ha/yr)	
  
With	
  yield	
  penalty	
  applied	
  
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	
  
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	
  
A	
  system	
  for	
  quan2fica2on	
  of	
  
smallholder	
  agriculture	
  GHGs	
  
Marja-­‐Liisa	
  Tapio-­‐Bistrom	
  
Mi2ga2on	
  of	
  climate	
  change	
  in	
  
Agriculture	
  programme	
  (MICCA)	
  
FAO	
  
	
  
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	
  
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	
  
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)	
  	
  
Emission	
  intensity	
  of	
  milk	
  in	
  East	
  Africa	
  
FAO,	
  2013	
  Source:	
  Global	
  Environmental	
  Assessment	
  Model	
  (GLEAM)	
  
	
  
0.0
2.0
4.0
6.0
8.0
10.0
12.0
14.0
16.0
18.0
Kenya Uganda United Republic
of Tanzania
KgCO2eq/kgFPCM
CO2, Post-farm
gate
CO2, Direct and
embedded energy
Feed CO2
Feed N20
Manure N20
Manure methane
Enteric
fermentation
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Temperate Arid Humid
KgCO2e/kgFPCM
Kenya: Grazing
systems
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Temperate Arid Humid
Kenya: Mixed systems
CO2, Direct and
embedded energy
Feed CO2
Feed N20
Manure N20
Manure methane
Enteric fermentation
Source:	
  Global	
  Environmental	
  Assessment	
  Model	
  (GLEAM),	
  FAO,	
  2013	
  	
  
60%
2%
6%
28%
2% 1% 1%
Emission	
  intensity	
  
of	
  milk	
  in	
  Kenya	
  
Enteric	
  methane	
  emissions	
  at	
  farm	
  scale	
  
-­‐	
  Kaptumo,	
  Kenya	
  	
  	
  
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
500 1000 1500 2000 2500 3000 3500 4000
KgEntericCH4perlitremilk
Liter of milk per cow per lactation
Source:	
  Based	
  on	
  Global	
  Environmental	
  Assessment	
  Model	
  (GLEAM),	
  farm	
  scale	
  	
  LCA	
  	
  based	
  on	
  
Household	
  data,	
  	
  Opio	
  et	
  al.,	
  2013	
  
Enteric	
  methane	
  -­‐	
  improving	
  feed	
  use	
  
efficiency	
  -­‐	
  Kaptumo,	
  Kenya	
  
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80
KgentericCH4perlitre
milk
Feed 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	
  
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	
  
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?	
  
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	
  
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	
  sources
4. 	
  using	
  consistent,	
  comparable	
  methods	
  and	
  data	
  sharing	
  
networks	
  that	
  enable	
  robust	
  es2mates	
  for	
  different	
  systems	
  
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.	
  
K. Butterbach-Bahl | IMK-IFU | March 2008
KIT – die Kooperation von
Forschungszentrum Karlsruhe GmbH
und Universität Karlsruhe (TH)
Standard Assessment of Mitigation Potential and
Livelihoods 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
Institute for Meteorology and Climate Research,
IMK-IFU
38 6/5/13
"   Very few data on mitigation
"   Mitigation not linked to livelihoods
"   Fragmented and diverse landscapes
"   Multi-criteria approaches missing
The concerns
Develop a low-cost protocol to quantify
greenhouse gas emissions and to identify
mitigation options for smallholders at whole-
farm and landscape levels
The goal
Institute for Meteorology and Climate Research,
IMK-IFU
39 6/5/13
Landscape analysis
and targeting
Landscape
implementation
Multi-dimensional evaluation
of mitigation options
Scalable and social
acceptable mitigation options
System-level estimation
of mitigation potential
Set-up of state-of-the-art
laboratory facilities
Training of laboratory
and field staff
Phase III:
Development of systems-level
mitigation options
Phase I: Targeting, priority setting and infrastructure
Phase II: Data acquisition
Capacitybuilding
Phase IV:
Implementation with
development partners
(UPCOMING)
Productivity
assessment
GHG
measurements
Profitability
evaluation
Social acceptability
assessment
Joint
scientific &
stakeholder
evaluation
Institute for Meteorology and Climate Research,
IMK-IFU
40 6/5/13
How to identify mitigation options at farm and
landscape level?
Institute for Meteorology and Climate Research,
IMK-IFU
41 6/5/13
Complex landscape: f (m, n, o, p, q)
m Landscape units
n Farm types
Land
Livestock
Other assets
Sources of
incomes
p Field types
Characterise
fertility x
management
Physical
environment
GIS analysis,
remote
sensing,
landuse
trends
Food
security,
poverty
levels
Productivity,
GHG
emissions,
crop
preferences
o Common lands
q Land types
Institute for Meteorology and Climate Research,
IMK-IFU
42 6/5/13
Landscape units and landusers Nyando, Kenya
Landscape analysis and targeting
Institute for Meteorology and Climate Research,
IMK-IFU
43 6/5/13
Targeting and upscaling: from
landscape to fields and back…
Institute for Meteorology and Climate Research,
IMK-IFU
44 6/5/13
Taking gas samples from
chambers
Step 1. Landscape analysis
Targeting:
-  Landscape units, farm types,
field types, soils
-  Site selection
Site characterization:
-  Soils, crops, biomass
Installation of chamber
frames
Informing and
interviewing farmers
Step 2. Installing measurement stations
Step 3. Measurements applying
gas pooling
Field work:
-  Overcoming spatial variability
by gas pooling
30 Oct 4 Nov 9 Nov 14 Nov 19 Nov 24 Nov 29 Nov
0
25
50
75
100
250
500
N2
Oflux[µgNm
-2
h
-1
]
2012
0
25
50
75
100
250
500
0
25
50
75
100
250
500
Cropland
Grassland
individual chambers
gas pooling
Forest
Temporal variability of N2O
fluxes at three sites differing
in land use at Maseno,
Kenya.
Arias-Navarro et al., Soil Biol. Biochem., in revision
Institute for Meteorology and Climate Research,
IMK-IFU
45 6/5/13
Lab work:
-  Analyzing gas samples (GC)
-  Calculating concentrations and fluxes
Step 5. Intepretation and upscaling
Step 4. Lab analysis and flux calculations
Synthesis of GHG measurements:
emission factors, empirical models, calibrating and
validating of detailed models
Upscaling: assigning emissions to landscape
elements and/or of GIS coupled biogeochemical
models
Institute for Meteorology and Climate Research,
IMK-IFU
46 6/5/13
Farm
type
Field
type
Profit ($/
ha)
Production
(kg/ha)
Emissions
(t CO2eq
per ha)
Emissions
(kg CO2 per
kg product)
Social
acceptability
(ranking)
1 1 50 500 0.6 1.2 1
1 2 140 5000 3 0.6 2
1 3 120 2000 2 1.0 2
1 4 40 4500 3 0.7 1
2 1 30 800 0.7 0.9 3
2 3 180 8000 3 0.4 2
2 4 250 300 0.5 1.7 1
n m Vn,m Wn,m Xn,m Yn,m Zn,m
Multi-dimensional assessment of mitigation
options
Trade-off analysis on multiple dimensions
K. Butterbach-Bahl | IMK-IFU | March 2008
KIT – die Kooperation von
Forschungszentrum Karlsruhe GmbH
und Universität Karlsruhe (TH)
Thanks for your attention
eugenio.diaz-pines@kit.edu
Small-­‐Holder	
  Agriculture	
  
Mi2ga2on	
  Benefit	
  Assessment	
  
	
  
Funded	
  by:	
  
Nicholas	
  Berry,	
  Andrew	
  Cross,	
  Casey	
  Ryan	
  
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	
  
Activity informationEnvironmental data
SHAMBA
Environmental
datasets
Baseline
information
CSA activities
Site specific
baseline info.
Monitoring data
User defined
CSA activities
Estimates of
GHG emission
reductions and
removals
User defined
data
GHG
accounting
models
and tools
An	
  example	
  for	
  Malawi	
  
	
  
Baseline	
  
•  Conven2onal	
  
maize	
  
Conserva8on	
  
agriculture	
  
•  With	
  and	
  without	
  
reduced	
  2llage	
  
Agroforestry	
  
•  Alley	
  cropping	
  
•  Intercropping	
  
hwp://shamba.ourecosystem.com	
  
SHAMBA	
  tool	
  (Malawi	
  demo)	
  
hwp://shamba.ourecosystem.com	
  
SHAMBA	
  methodology	
  
hwp://www.2nyurl.com/shambatool	
  
	
  
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	
  
	
  
nicholas.berry@ed.ac.uk	
  
534 June 2013
	
  
Official	
  UNFCCC	
  side	
  event:	
  	
  
	
  
Agriculture	
  and	
  Mi8ga8on:	
  
Towards	
  low	
  emissions	
  
development	
  
	
  
	
  
www.ccafs.cgiar.org	
  
	
  
	
  
	
  
	
  

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Bonn Climate Conference Side Event: 4 June 2013

  • 1. 14 June 2013   Official  UNFCCC  side  event:       Agriculture  and  Mi8ga8on:   Towards  low  emissions   development                
  • 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. ©  UNIQUE  forestry  and  land  use  GmbH   Contents   Agricultural  mi2ga2on  within   UNFCCC  process   Presenta2on  of  key  results  of   the  review   Recommenda2ons        
  • 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. ©  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. ©  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. ©  UNIQUE  forestry  and  land  use  GmbH   Key  elements  of  na8onal  mi8ga8on  planning  and  NAMAs  
  • 8. ©  UNIQUE  forestry  and  land  use  GmbH   Mi8ga8on  benefits  from  agricultural  NAMAs  
  • 9. ©  UNIQUE  forestry  and  land  use  GmbH   Quan8fied  Ag.  mi8ga8on  benefits  in  Brazil  and  Ethiopia  
  • 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. ©  UNIQUE  forestry  and  land  use  GmbH   Cost  &  benefit  analysis  livestock  mi8ga8on  ac8vi8es  in  Mongolia   Estimates were made for sheep, beef and dairy cow breeding programs, fodder production and milk processing: Breeding programs have significant net benefits, so the abatement costs ($/tCO2) are negative. Irrigated fodder production has high investment cost and little mitigation potential. Reducing milk losses by small-scale milk processing units has lowest abatement cost and high economic benefits. -­‐33   -­‐44   -­‐4   1703   63   -­‐220   -­‐500   0   500   1000   1500   2000   Sheep   breeding   Beef   breeding   Dairy  AI   irrigated   fodder   produc8on   hay   produc8on   Milk   processing   Abatement  costs  ($/tCO2)  
  • 12. ©  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. 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-­‐landuse.de   www.unique-­‐landuse.de     Financed  &   edited  by:  
  • 14. How  to  determine  which  site-­‐ specific  GHG  mi2ga2on  op2ons  give   the  greatest  benefits?   Jon  Hillier   SBSTA,  Bonn,  5th  June  2013  
  • 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. 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. •  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 C N application rate Emissions (kg CO2- equiv) reduce N application rate* use low emissions fertiliser employ no-till increase C inputs use NIs . . . . . . 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. Reduce  tillage Increase  C  inputs Use  BAT Reduce  N  rate Use  soil  inhibitors N rate > 200 kg/ha/yr N <= 100 kg/ha/yr
  • 19. Reduce  tillage Increase  C  inputs Use  BAT Reduce  N  rate Use  soil  inhibitors 150 < N <- 200 (kg/ha/yr) 100 < N <= 150 (kg/ha/yr)
  • 20. But  N  affects  yield!  
  • 21. Reduce  tillage Increase  C  inputs Use  BAT Reduce  N  rate Use  soil  inhibitors 150  <  N  <-­‐  200     (kg/ha/yr)   100  <  N  <=  150   (kg/ha/yr)   With  yield  penalty  applied  
  • 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. 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. A  system  for  quan2fica2on  of   smallholder  agriculture  GHGs   Marja-­‐Liisa  Tapio-­‐Bistrom   Mi2ga2on  of  climate  change  in   Agriculture  programme  (MICCA)   FAO    
  • 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. 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. 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. Emission  intensity  of  milk  in  East  Africa   FAO,  2013  Source:  Global  Environmental  Assessment  Model  (GLEAM)     0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 Kenya Uganda United Republic of Tanzania KgCO2eq/kgFPCM CO2, Post-farm gate CO2, Direct and embedded energy Feed CO2 Feed N20 Manure N20 Manure methane Enteric fermentation
  • 29. 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 Temperate Arid Humid KgCO2e/kgFPCM Kenya: Grazing systems 0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 Temperate Arid Humid Kenya: Mixed systems CO2, Direct and embedded energy Feed CO2 Feed N20 Manure N20 Manure methane Enteric fermentation Source:  Global  Environmental  Assessment  Model  (GLEAM),  FAO,  2013     60% 2% 6% 28% 2% 1% 1% Emission  intensity   of  milk  in  Kenya  
  • 30. Enteric  methane  emissions  at  farm  scale   -­‐  Kaptumo,  Kenya       0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 500 1000 1500 2000 2500 3000 3500 4000 KgEntericCH4perlitremilk Liter of milk per cow per lactation Source:  Based  on  Global  Environmental  Assessment  Model  (GLEAM),  farm  scale    LCA    based  on   Household  data,    Opio  et  al.,  2013  
  • 31. Enteric  methane  -­‐  improving  feed  use   efficiency  -­‐  Kaptumo,  Kenya   0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40 1.60 1.80 KgentericCH4perlitre milk Feed 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. 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. 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. 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. 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  sources 4.  using  consistent,  comparable  methods  and  data  sharing   networks  that  enable  robust  es2mates  for  different  systems  
  • 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. K. Butterbach-Bahl | IMK-IFU | March 2008 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH) Standard Assessment of Mitigation Potential and Livelihoods 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. Institute for Meteorology and Climate Research, IMK-IFU 38 6/5/13 "   Very few data on mitigation "   Mitigation not linked to livelihoods "   Fragmented and diverse landscapes "   Multi-criteria approaches missing The concerns Develop a low-cost protocol to quantify greenhouse gas emissions and to identify mitigation options for smallholders at whole- farm and landscape levels The goal
  • 39. Institute for Meteorology and Climate Research, IMK-IFU 39 6/5/13 Landscape analysis and targeting Landscape implementation Multi-dimensional evaluation of mitigation options Scalable and social acceptable mitigation options System-level estimation of mitigation potential Set-up of state-of-the-art laboratory facilities Training of laboratory and field staff Phase III: Development of systems-level mitigation options Phase I: Targeting, priority setting and infrastructure Phase II: Data acquisition Capacitybuilding Phase IV: Implementation with development partners (UPCOMING) Productivity assessment GHG measurements Profitability evaluation Social acceptability assessment Joint scientific & stakeholder evaluation
  • 40. Institute for Meteorology and Climate Research, IMK-IFU 40 6/5/13 How to identify mitigation options at farm and landscape level?
  • 41. Institute for Meteorology and Climate Research, IMK-IFU 41 6/5/13 Complex landscape: f (m, n, o, p, q) m Landscape units n Farm types Land Livestock Other assets Sources of incomes p Field types Characterise fertility x management Physical environment GIS analysis, remote sensing, landuse trends Food security, poverty levels Productivity, GHG emissions, crop preferences o Common lands q Land types
  • 42. Institute for Meteorology and Climate Research, IMK-IFU 42 6/5/13 Landscape units and landusers Nyando, Kenya Landscape analysis and targeting
  • 43. Institute for Meteorology and Climate Research, IMK-IFU 43 6/5/13 Targeting and upscaling: from landscape to fields and back…
  • 44. Institute for Meteorology and Climate Research, IMK-IFU 44 6/5/13 Taking gas samples from chambers Step 1. Landscape analysis Targeting: -  Landscape units, farm types, field types, soils -  Site selection Site characterization: -  Soils, crops, biomass Installation of chamber frames Informing and interviewing farmers Step 2. Installing measurement stations Step 3. Measurements applying gas pooling Field work: -  Overcoming spatial variability by gas pooling 30 Oct 4 Nov 9 Nov 14 Nov 19 Nov 24 Nov 29 Nov 0 25 50 75 100 250 500 N2 Oflux[µgNm -2 h -1 ] 2012 0 25 50 75 100 250 500 0 25 50 75 100 250 500 Cropland Grassland individual chambers gas pooling Forest Temporal variability of N2O fluxes at three sites differing in land use at Maseno, Kenya. Arias-Navarro et al., Soil Biol. Biochem., in revision
  • 45. Institute for Meteorology and Climate Research, IMK-IFU 45 6/5/13 Lab work: -  Analyzing gas samples (GC) -  Calculating concentrations and fluxes Step 5. Intepretation and upscaling Step 4. Lab analysis and flux calculations Synthesis of GHG measurements: emission factors, empirical models, calibrating and validating of detailed models Upscaling: assigning emissions to landscape elements and/or of GIS coupled biogeochemical models
  • 46. Institute for Meteorology and Climate Research, IMK-IFU 46 6/5/13 Farm type Field type Profit ($/ ha) Production (kg/ha) Emissions (t CO2eq per ha) Emissions (kg CO2 per kg product) Social acceptability (ranking) 1 1 50 500 0.6 1.2 1 1 2 140 5000 3 0.6 2 1 3 120 2000 2 1.0 2 1 4 40 4500 3 0.7 1 2 1 30 800 0.7 0.9 3 2 3 180 8000 3 0.4 2 2 4 250 300 0.5 1.7 1 n m Vn,m Wn,m Xn,m Yn,m Zn,m Multi-dimensional assessment of mitigation options Trade-off analysis on multiple dimensions
  • 47. K. Butterbach-Bahl | IMK-IFU | March 2008 KIT – die Kooperation von Forschungszentrum Karlsruhe GmbH und Universität Karlsruhe (TH) Thanks for your attention eugenio.diaz-pines@kit.edu
  • 48. Small-­‐Holder  Agriculture   Mi2ga2on  Benefit  Assessment     Funded  by:   Nicholas  Berry,  Andrew  Cross,  Casey  Ryan  
  • 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. Activity informationEnvironmental data SHAMBA Environmental datasets Baseline information CSA activities Site specific baseline info. Monitoring data User defined CSA activities Estimates of GHG emission reductions and removals User defined data GHG accounting models and tools
  • 51. An  example  for  Malawi     Baseline   •  Conven2onal   maize   Conserva8on   agriculture   •  With  and  without   reduced  2llage   Agroforestry   •  Alley  cropping   •  Intercropping   hwp://shamba.ourecosystem.com  
  • 52. SHAMBA  tool  (Malawi  demo)   hwp://shamba.ourecosystem.com   SHAMBA  methodology   hwp://www.2nyurl.com/shambatool     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     nicholas.berry@ed.ac.uk  
  • 53. 534 June 2013   Official  UNFCCC  side  event:       Agriculture  and  Mi8ga8on:   Towards  low  emissions   development       www.ccafs.cgiar.org