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