Improving estimates of GHG emission factors from livestock production systems on smallholder farms
•
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In sub-Saharan Africa, agriculture is estimated to account for over 60% of GHG emissions, primarily due to land use change and enteric methane production in ruminants; and over 80% of agriculture (both area and production) is smallholder systems. No empirical studies on enteric CH4 emissions and very few studies on GHG emissions from soils in these systems have been conducted. This study sampled 60 farms in western Kenya using static chambers (3 reps) and analyzed soils once for total C/N content, BD and texture and 4 times for soil IN concentration.
![Improving
es-mates
of
GHG
emission
factors
from
livestock
produc-on
systems
on
smallholder
farms
David
Pelster,
Klaus
Bu1erbach-‐Bahl,
Mariana
Rufino,
John
Goopy
and
Todd
Rosenstock
Introduc6on
Lack
of
data
on
GHG
emissions
from
African
agriculture
suggests
inaccuracies
in
na6onal
inventories
• In
September
2014
sub-‐Saharan
Africa,
agriculture
is
es6mated
to
account
for
over
60%
of
GHG
emissions,
primarily
due
to
land
use
change
and
enteric
methane
produc6on
in
ruminants;
and
over
80%
of
agriculture
(both
area
and
produc6on)
is
smallholder
systems
• No
empirical
studies
on
enteric
CH4
emissions
and
very
few
studies
on
GHG
emissions
from
soils
in
these
systems
• Current
na6onal
SSA
inventories
therefore
are
based
on
IPCC
6er
1
methodology;
• Using
emission
factors
from
OECD
states
with
large
industrial
farming
systems
that
likely
do
not
represent
smallholder
systems
where
manure
applica6ons,
not
synthe6c
fer6lizers
are
dominant
source
of
nutrients,
and
where
ruminant
fodder
is
generally
protein-‐poor
and
food
availability
is
oRen
limited
(The
only
available
study
[S.
Africa]
es6mated
that
the
Tier
1
emission
factors
for
ruminant
CH4
produc6on
is
about
50%
of
actual
emissions)
Pictures
Materials
and
methods
Phase
I:
Targe-ng,
priority
seFng
and
infrastructure
Landscape
analysis
and
targe-ng
Landscape
implementa-on
Set-‐up
of
state-‐of-‐the-‐art
laboratory
facili-es
Training
of
laboratory
and
Phase
II:
Data
acquisi-on
Mul--‐dimensional
evalua-on
of
mi-ga-on
op-ons
field
staff
System-‐level
es-ma-on
of
Scalable
and
social
acceptable
mi-ga-on
op-ons
Produc-vity
assessment
1. Soil
GHG
emissions
from
mixed
smallholder
farms
did
not
vary
by
management
(“intensive”
vs
extensive)
however
differences
between
land
classes
were
noted
2. Soil
cumula6ve
emissions
tended
to
be
much
lower
than
previous
studies
in
OECD
states
3. S6ll
require
measurements
of
enteric
CH4
produc6on
• Can
emissions
(g
C
m-‐2
yr-‐1)
Cumula6ve
C-‐CO2
emissions
(mg
C
m-‐2
yr-‐1)
Cumula6ve
C-‐CH4
increase
intensity
of
management
(greater
use
of
fer6lizers)
without
increasing
soil
GHG
emissions
• Suggests
emissions
(mg
N
m-‐2
yr-‐1)
Cumula6ve
N-‐N2O
that
increased
nutrient
inputs
that
increase
agriculture
/
livestock
produc6on
could
be
considered
mi6ga6on
as
it
also
can
decrease
emissions
per
unit
• Development
of
emission
factors
for
using
Tier
II
methodology
to
calculate
na6onal
GHG
inventories
• Determine
feed
strategies
for
increasing
animal
produc6on
while
reducing
CH4
emission
intensi6es
David
Pelster
or
Klaus
Bu[erbach-‐Bahl
dpelster@cgiar.org
;
k.bu[erbach-‐bahl@cgiar.org
●
P.O.
Box
30709-‐00100
Nairobi
Kenya
●
+254
20
422
3513
h[p://aghealth.wordpress.com
●
www.ilri.org
Acknowledgements:
The
CGIAR
Research
Program
on
Climate
Change,
Agriculture
and
Food
Security
(CCAFS)
and
the
Interna6onal
Livestock
Research
Ins6tute
(ILRI)
This
document
is
licensed
for
use
under
a
Crea6ve
Commons
A[ribu6on
–Non
commercial-‐Share
Alike
3.0
Unported
License
September
2014
Results
Research
into
use
mi-ga-on
poten-al
Phase
III:
Development
of
systems-‐
level
mi-ga-on
op-ons
Capacity
building
Phase
IV:
Implementa-on
with
development
partners
GHG
measurements
Profitability
evalua-on
Social
acceptability
assessment
Joint
scien-fic
&
stakeholder
evalua-on
UPCOMING
• Stra6fied
into
5
land
classes
(based
on
remote
sensing);
3
field
types
(based
on
farm
management);
and
3
broad
vegeta6on
classes
• Sampled
1x
per
week
for
one
year
at
60
farms
in
western
Kenya
using
sta6c
chambers
(3
reps)
• Analyzed
soils
once
for
total
C/N
content,
BD
and
texture
and
4
6mes
for
soil
IN
concentra6on
• Classify
livestock
produc6on
systems
and
determine
herd
numbers
within
each
class
• Plan
to
use
enclosed
respira6on
chambers
for
measuring
ruminant
CH4
produc6on
Soil
GHG
emissions
over
6me:
a)
mg
C-‐CO2
m-‐2
hr-‐1;
b)
μg
C-‐CH4
m-‐2
hr-‐1;
c)
μg
N-‐N2O
m-‐2
hr-‐1;
d)
Soil
moisture
content;
e)
soil
IN
(NH4
+
NO3)
concentra6ons
Note:
Do[ed
ver6cal
lines
indicate
plan6ng,
while
dashed
lines
indicate
harves6ng
Landclass:
1
=
lowland
subsistence
farming;
2
=
cash
crops;
3
=
highland
subsistence
farming;
4
=
highland
mixed
farming;
5
=
grasslands
/
pasture
Cumula6ve
soil
GHG
emissions
over
6me
for
the
5
different
land
classes.
Bars
indicate
1
SEM](https://image.slidesharecdn.com/tropentagposterpelsterfinal-140912001425-phpapp02/85/Improving-estimates-of-GHG-emission-factors-from-livestock-production-systems-on-smallholder-farms-1-320.jpg)
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Improving estimates of GHG emission factors from livestock production systems on smallholder farms
- 1. Improving es-mates of GHG emission factors from livestock produc-on systems on smallholder farms David Pelster, Klaus Bu1erbach-‐Bahl, Mariana Rufino, John Goopy and Todd Rosenstock Introduc6on Lack of data on GHG emissions from African agriculture suggests inaccuracies in na6onal inventories • In September 2014 sub-‐Saharan Africa, agriculture is es6mated to account for over 60% of GHG emissions, primarily due to land use change and enteric methane produc6on in ruminants; and over 80% of agriculture (both area and produc6on) is smallholder systems • No empirical studies on enteric CH4 emissions and very few studies on GHG emissions from soils in these systems • Current na6onal SSA inventories therefore are based on IPCC 6er 1 methodology; • Using emission factors from OECD states with large industrial farming systems that likely do not represent smallholder systems where manure applica6ons, not synthe6c fer6lizers are dominant source of nutrients, and where ruminant fodder is generally protein-‐poor and food availability is oRen limited (The only available study [S. Africa] es6mated that the Tier 1 emission factors for ruminant CH4 produc6on is about 50% of actual emissions) Pictures Materials and methods Phase I: Targe-ng, priority seFng and infrastructure Landscape analysis and targe-ng Landscape implementa-on Set-‐up of state-‐of-‐the-‐art laboratory facili-es Training of laboratory and Phase II: Data acquisi-on Mul--‐dimensional evalua-on of mi-ga-on op-ons field staff System-‐level es-ma-on of Scalable and social acceptable mi-ga-on op-ons Produc-vity assessment 1. Soil GHG emissions from mixed smallholder farms did not vary by management (“intensive” vs extensive) however differences between land classes were noted 2. Soil cumula6ve emissions tended to be much lower than previous studies in OECD states 3. S6ll require measurements of enteric CH4 produc6on • Can emissions (g C m-‐2 yr-‐1) Cumula6ve C-‐CO2 emissions (mg C m-‐2 yr-‐1) Cumula6ve C-‐CH4 increase intensity of management (greater use of fer6lizers) without increasing soil GHG emissions • Suggests emissions (mg N m-‐2 yr-‐1) Cumula6ve N-‐N2O that increased nutrient inputs that increase agriculture / livestock produc6on could be considered mi6ga6on as it also can decrease emissions per unit • Development of emission factors for using Tier II methodology to calculate na6onal GHG inventories • Determine feed strategies for increasing animal produc6on while reducing CH4 emission intensi6es David Pelster or Klaus Bu[erbach-‐Bahl dpelster@cgiar.org ; k.bu[erbach-‐bahl@cgiar.org ● P.O. Box 30709-‐00100 Nairobi Kenya ● +254 20 422 3513 h[p://aghealth.wordpress.com ● www.ilri.org Acknowledgements: The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the Interna6onal Livestock Research Ins6tute (ILRI) This document is licensed for use under a Crea6ve Commons A[ribu6on –Non commercial-‐Share Alike 3.0 Unported License September 2014 Results Research into use mi-ga-on poten-al Phase III: Development of systems-‐ level mi-ga-on op-ons Capacity building Phase IV: Implementa-on with development partners GHG measurements Profitability evalua-on Social acceptability assessment Joint scien-fic & stakeholder evalua-on UPCOMING • Stra6fied into 5 land classes (based on remote sensing); 3 field types (based on farm management); and 3 broad vegeta6on classes • Sampled 1x per week for one year at 60 farms in western Kenya using sta6c chambers (3 reps) • Analyzed soils once for total C/N content, BD and texture and 4 6mes for soil IN concentra6on • Classify livestock produc6on systems and determine herd numbers within each class • Plan to use enclosed respira6on chambers for measuring ruminant CH4 produc6on Soil GHG emissions over 6me: a) mg C-‐CO2 m-‐2 hr-‐1; b) μg C-‐CH4 m-‐2 hr-‐1; c) μg N-‐N2O m-‐2 hr-‐1; d) Soil moisture content; e) soil IN (NH4 + NO3) concentra6ons Note: Do[ed ver6cal lines indicate plan6ng, while dashed lines indicate harves6ng Landclass: 1 = lowland subsistence farming; 2 = cash crops; 3 = highland subsistence farming; 4 = highland mixed farming; 5 = grasslands / pasture Cumula6ve soil GHG emissions over 6me for the 5 different land classes. Bars indicate 1 SEM