science to support low-emissions development€¦ · –socially acceptable opportunities for...

Todd Rosenstock, M Mpanda, J Kirui, E Massoro, J Rioux, E

Anyekulu, E Luedeling, S Kuyah, A Kimaro, S Franzel, H

Neurfeldt, K Shepherd, C Seeberg-Eldervedt, M Tapio-Bistrom, C

Neely & many many lab and field staff…

FAO | Climate Change Study Group| 27.11.2013

Science to support low-emissions development: Concepts, preliminary results, & key messages from the MICCA Pilot Projects

Funded by Government of Finland

Cereal-based

Slash and burn

Steep hillslope Market-oriented

Livestock-based

Stable land use

Kolero-CARE Kaptumo-EADD

Typical of highland cereal system of E and S Africa

Typical of smallholder dairy systems of E Africa

Participatory Assessment of Low-

emissions Development Practices

Measurement & Monitoring of Soils,

Vegetation, Greenhouse Gases, Productivity and Economics

Improving and Developing

Predictive Tools for Potential Impact

Socio-ecological Targeting of Field-

level Low-emission Practices

Dissemination of Results to Stakeholders to Inform and Prioritize

Agricultural Investments

Verify

Identify

Scale

Scale Verify

3

1

2

2 3

The MICCA Approach: Identify Verify Scale

Co-located multi-criteria and multi-scale research

Imp. stoves

Biogas

Manure Mgmt

ConsAg

AI

Imp. diet

Vacc.

Socioeconomic, Ex-Act, & Capacity Assessment

Imp. diet Animal

health

Animal health

Biogas

ConsAg

Imp. Feed.

Animal health

Agro-for.

Stakeholder focus groups

Manure Mgmt

Participatory process to identify practices in Kaptumo

Man. Mgmt

Agro-for.

Agro-for

Rioux et al. in prep

Almost no data on GHG sources and sinks in Africa

Ex: N2O emissions from managed soils

Hickman, Rosenstock et al. in prep

0 50 100 150 200 250 300 350

010

20

30

40

50

Fertilizer treatment (kg N ha-1)

N2O

flu

x (

g N

ha

-1 d

ay

-1)

0.198 + 0.049x AIC

2.31e0.0065x

AIC

1.29 + 0.018x + 0.00012x2 AIC

=357.27 (linear)

=357.29 (exponential)

=357.77 (quadratic)

GHG and C-

sequestration

quantification

methods

Remote sensing

Biomass inventory Static chamber Feed surveys

Cash crop N2O

Food/cash CH4

White = livelihoods Red = mitigation

Fuel C sequestration

Feed crop

Pasture C sequestration

The challenge

Tittonell et al. 2009

Whole-farm productivity, economic and GHG balance assessment in Kaptumo

CO2 and N2O data from Kaptumo

Rosenstock et al. in prep

Land uses 1975 1995 2005

Area (ha) % Area (ha) % Area (ha) %

Closed forest 10,086.071 47 7005.148 32 6119.204 28 Open forest 3716.716 17 5739.65 27 5692.584 26 Bushland 2813.215 13 3142.305 15 979.108 5 Cultivation 4913.153 23 5639.29 26 8735.102 41

Total 21,529.155 100 21,529.155 100 21,529.108 100

Land use/cover changes from 1975 to 2004 in Kolero

Mpanda et al. in prep

Testing plot level intensification with conservation agriculture

Measurements include

- GHG emissions

- Soil physical and

chemical properties - Biomass and yield

Conventional

Mulch in row, -cover crop

CA w/ annual cc (lablab)

CA w/ Gliricidia

CA w/ fertilizer (30 kgN/ha)

Treatment Avg. GWP in2013LR (Mg CO2e / ha/ 0.5yr)

Cultivation 13.5

Fertilizer 11.1

Gliricidia 15.0

Lablab 14.1

Mulching 13.2

Testing plot level intensification with conservation agriculture

Kimaro et al. in prep

Improved cookstoves to reduce pressure on forests

Emissions reduction/stove (t CO2e/yr)*

Assumption non-renewable biomass

0.12 25%

0.24 50%

0.37 75%

0.44 90%

0.49 100%

*Based on the following assumptions: - Estimates of wood collection in Kolero

socioeconomic survey - Efficiency of improved stoves equivalent to

similar reported stoves (0.4 Mg biomass saved)

Freeman et al. in prep

Menu of MICCA practices

Income & food

security

Mitigation of GHG

CO2 CH4 N2O

Improved fodder & feeding + +/- + +

Animal breeding + + +

Manure management + + +/-

Conservation agricutlure + + +

Improved cookstoves + +/-

Grazing intensity +/- +/- +/-

+/-

Pasture species introduction + + +/-

Agroforestry + + +/-

Stakeholder generated and scientifically verified

site-specific management options

From ‘+’s to quantities and

ranges

• We have more data than ever before for targeting interventions

• Data gaps will remain

• Uncertainties will remain

• Decisions must be made in the face of imperfect information

• How can we decide where interventions will be successful?

• Raster algebra and Probabilistic modeling with Monte Carlo simulations

Targeting interventions and investments at field,

project, and national levels

• randomization to minimize local biases that might arise from convenience sampling

Biophysical assessment: Land degradation surveillance framework

Site (100 km2) 16 Clusters (1 km2) 10 Plots (1000 m2) 4 Sub-Plots (100 m2)

Quantify major risks to land health

Basis for targeted land management

interventions

a spatially stratified, hierarchical, randomized sampling design

Links with the AfSIS and CCAFS programs

Field site characterization

Soil spectroscopy

Total topsoil carbon - Kaptumo

Interpolated by ordinary

kriging

SOC

?

Target field and household interventions for extension services in a spatially-explicit way

Economic constraints -Baseline houselhold survey -Econometric analysis of constraints to adoption of conservation ag

Biophysical baseline - Soil physical/chemical

prop

Kaptumo, Kenya

Ideal

Kolero, Tanzania

Unknown

High livestock density Good connection to markets

Secure tenure

Low livestock density Poor connection to markets

Insecure tenure

Sub-Saharan smallholders with near-ideal conditions for CA No information available

Targeting programmatic interventions

Parameter 90% confidence interval of yield effect (% change)

Precipitation -29% to +16% < 600 mm yr-1 -15% to +14% 600 - 1000 mm yr-1 -23% to +16% > 1000 mm yr-1 -29% to + 4%

A simple yield model for converting from conventional production to conservation agriculture

Effects from literature and expert calibrated probabilities

Rosenstock et al. in press AGEE

0.0

0.2

0.4

0.6

-6 -3 0 3 6Yield effect: Mg maize per ha

den

sit

y

names

Ideal

Kaptumo

Kolero

Unknown

Yield effect: % change

-200 -100 -50 0 50 100 200

Predicted yield outcomes by shifting from conventional to conservation agriculture of maize

Most probable outcomes

Less likely but possible outcomes

Livestock pressure:

Competition for biomass at Kaptumo

Connectivity:

Poor access to markets and

information at Kolero

Drivers of poor CA performance differ among sites

Outputs

Significance

Audience/Scaling/Dissemination opportunities

Atmosphere-biosphere exchange of GHG fluxes in East African agricultural landscapes

1st data Tests how right or wrong estimates have can be Looks at sustainable intensification at scales relevant to the farmer

Development orgs, national policy makers, orgs interested in accounting

Whole-farm GHG balances Development orgs. interested in GHG accounting

Trade-offs between productivity and GHGs in the context of sustainable intensification

Development partners Other orgs working with similar technologies or agroecologies

Targeting place-based CSA interventions

Provides a framework for analysis and scaling of results

EADD and CARE extension programs Governments in E. Africa

Research significance and scaling opportunities

Key messages from the MICCA Pilots (tentative)

– Strong links between science and development enable selection of socially appropriate management practices.

– Socially acceptable opportunities for climate change mitigation and food production synergies are available in smallholder farming systems of Africa.

– Future extension activities, programmatic investments and [possibly] policies can be informed and strengthened by place-based data.

Identify

Verify

Scale

THANK YOU

t.rosenstock@cgiar.org

janie.rioux@fao.org