-­‐
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

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

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

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
1.
Introduc&on
The
Economics
&
Policy
Innova0ons
for
Climate-­‐Smart
Agriculture
(EPIC)
Programme
in
Malawi

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
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
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
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
2.
Reference
GHG
Assessments
EXISTING
AFOLU
GHG
ESTIMATES
FOR
MALAWI

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
3.
Household
Data
and
GHG
es&mates
in
Malawi
IMPROVING
GHG
IN
MALAWI
ESTIMATES
USING
HOUSEHOLD
DATA

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
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
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
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
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
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
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