Standard assessment of mitigation potentials

and livelihoods in smallholder systems

Klaus Butterbach-Bahl, Mariana Rufino, David Pelster, Todd Rosenstock, Lini Wollenberg,

Outline

• Agriculture and GHG emissions• Why we need a GHG lab at ILRI• What have I done before?• What do we want to do?• On-going projects• Outlook

Biosphere as source for atmospheric trace gases

CH4

CO2

VOCNOx

N2O

60-70%

60-70%

Isoprenoid-production

90%

Nitrification DenitrificationMethanogenesisCH4-Oxidation

Photosynthesis

The Biosphere• major source/ sink for trace

substances (N2O, CH4, NOx, CO2, VOC)

• dynamic exchange with atmosphere

• effects chemical composition of the atmosphere

• and, thus, environmental conditions on earth (e.g. climate and air pollution)

Atmospheric composition change and sources of GHG‘s

IPCC, 2007

66.7%

33.3%

Biogen Anthropogen

Fossil fuel burning

Land use change

Biogen test

Industrial sources

Livestock, rice paddies, wetlands

Biogen test

Industrial sources

Agriculture, forests, oceans

Atmospheric composition change and sources of GHG‘s

IPCC, 2007

66.7%

33.3%

Biogen Anthropogen

Fossil fuel burning

Land use change

Biogen test

Industrial sources

Livestock, rice paddies, wetlands

Biogen test

Industrial sources

Agriculture, forests, oceans

Food systems contribute 19%–29% of global anthropogenic greenhouse gas (GHG) emissions,

releasing 9,800–16,900 megatonnes of carbon dioxide equivalent (MtCO2e) in 2008.

Agricultural production, including indirect emissions associated with land-cover change, contributes 80%–86% of total food system emissions, with significant

regional variation.(Vermeulen et al. 2012, Annu. Rev. Environ. Res.)

Why do we need a GHG lab at ILRI?• In developing countries GHG emissions from

agricultural activities are the dominant source

Why do we need a GHG lab at ILRI?• No measurements available. Countries need to rely on EF

obtained from other climate zones.• Without data, countries have no chance to move from

Tier 1, to Tier 2 or 3 more accurate, better targeting• Verification of agricultural intensification: produce more

with less emissions (or environmental impacts)• Verification of climate smart agriculture: how can this be

demonstrated• No expertise in Sub-Saharan Africa capacity building• Plenty of project opportunities, e.g. World Bank has a

focus on agricultural production at lower GHG emission costs. Should this be done only by desktop studies?

What I have done before?• PhD on strategies to mitigate CH4 emissions

from rice paddies

Rice varieties significantly affect the CH4 emission strength. Thus, choosing a high yielding variety with low

emission potential would significantly reduce CH4 emissions from rice paddies

• Postdoc: N deposition effects on forest functions and GHG fluxes

What I have done before?

Atmospheric N deposition due to agricultural activities has significantly enhanced N trace gas fluxes from forests

and leaching of NO3 from forest soils.

What I have done before• Scientist: Global source strength of forests for N2O• Combining measurements and modeling

Identifying regional and global hotpsots of GHG emissions and improving global estimates

• Running a number of projects worldwide on GHG emissions from various ecosystems, identifying involved processes, estimating GHG emissions at regional and global scales and identifying possible mitigation options

What I have done before?

Measurements are needed for improving models (even simple EF models), regional and global estimates. Process studies allow necessary insights to improve mechanistic

models, which are the most promising tools for developing mitigation strategies in view of global

environmental changes

What do we want to do?• Enable ILRI to develop capacity for quantifying

GHG emissions from agricultural sources• Make ILRI a competence centre for GHG

measurements in Africa• Build a network of GHG labs across Africa and

elsewhere to allow developing countries to obtain country specific information about their agricultural GHG emissions

• ……

On-going projects

SAMPLESIdentifying pro-poor mitigation options for

smallholder agriculture in the developing world -

a multi-criteria and across-scales assessment

• Mitigation not linked to livelihoods • Fragmented and diverse landscapes• No data on mitigation• 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

How to identify mitigation options at farm and landscape level?

Landscape analysisand 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

Capacity building

Phase IV:Implementation with development partners

(UPCOMING)

Productivity assessment

GHG measurements

Profitability evaluation

Social acceptability assessment

Joint scientific & stakeholde

r evaluation

Complex landscape: f (m, n, o, p, q)

m Landscape units

n Farm typesLand

LivestockOther assetsSources of incomes

p Field typesCharacterise

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

Nyando, western Kenya

Landscape structure

Landscape units and land users

Sampling intensity (sites: area)

In terms of a 250 m square grid

class sites area (km2) sites:areacultivated (cash and subsistence) 28 2.74 10.23cultivated (cash) 47 5.94 7.91cultivated (grasslands and pastures) 47 12.69 3.70cultivated (subsistence) 141 41.54 3.39mixed 93 34.69 2.68uncultivated vegetation 4 2.39 1.67

Targeting and upscaling: from landscape to fields and back…

Step 1. Landscape analysis

Step 2. Installing measurement stations

Targeting:- Landscape units, farm types,

field types, soils- Site selection

Site characterization:- Soils, crops, biomass

DEM-N

yand

o,Ken

ya

Installation of chamber frames

Informing and interviewing farmers

Step 3. Measurements applying gas pooling

Step 4. Lab analysis and flux calculations

Field work:- Overcoming spatial variability

by gas pooling method

Gas sampling(closed chamber method)

Storage of gas samples in vials

Determination of trace gas concentrations via gas chromatography

Lab work:- Analyzing gas samples- Calculating concentrations and

fluxes

9

6

10**

10*60***

mCh

Ch

VA

VMwbF

Flux calculation formula

Arias-Navarro et al., Soil Biol. Biochem. submitted

Step 5. Interpretation and upscaling

30 Oct 4 Nov 9 Nov 14 Nov 19 Nov 24 Nov 29 Nov

0255075

100

250500

N2O

flu

x [µ

g N

m-2 h

-1]

2012

0255075

100

250500

0255075

100

250500

Cropland

Grassland

individual chambers gas pooling

Forest

Temporal variability of N2O fluxes at three sites differing in land use at Maseno, Kenya.

Synthesis of GHG measurements: information useful to derive emission factors, empirical models, calibrating and validating of detailed models

Upscaling: using the targeting approach (assigning emissions to landscape elements) and/or of GIS coupled biogeochemical models

Arias-Navarro et al., Soil Biol. Biochem. submitted

0

5

10

15

20

Cum

ulat

ive

N2O

-flu

xes

[mg

N m

-2]

Highland Control Highland NPK Lowland Control Lowland NPK

-60

-40

-20

0

Cum

ulative CH

4 -fluxes

[mg C

m-2]

23 Apr 7 May 21 May 4 Jun 18 Jun 2 Jul 16 Jul

0

50

100

150

Cum

ulat

ive

CO

2-f

luxe

s

[g C

m-2]

23 Apr 7 May 21 May 4 Jun 18 Jun 2 Jul 16 Jul

0

20

40

60 Cum

ulative GH

G fluxes

[CH

4 +N

2 O: C

O2 eq ha

-1]

Complex landscape: f (m, n, o, p, q)

m Landscape units

n Farm typesLand

LivestockOther assetsSources of incomes

p Field typesCharacterise

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

Farmtype

Fieldtype

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

Landscape analysisand 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

Capacity building

Phase IV:Implementation with development partners

(UPCOMING)

Productivity assessment

GHG measurements

Profitability evaluation

Social acceptability assessment

Joint scientific & stakeholde

r evaluation

• Why multiple scales? -> landscape redesign• Why multi-criteria? -> landusers are (often) poor

On-going projects - manure

The source of manure matters…

Summary and Outlook

• Agriculture is a key source for atmospheric GHG• Little is known for developing countries• Little competence in Sub-Saharan Africa• … the chance for ILRI, since this topic has a huge

importance for funding organizations („sustainable intensification“)

ILRI becomes a competence centre for GHG

Thanks for your attention

K.Butterbach-Bahl@cgiar.org