Presentation for CCAFS - FAO workshop Smallholder Mitigation: Whole Farm and Landscape Accounting
 27 - 28 October 2011


1. Whole farm accounting for smallholders in developing countries – Activity based monitoring of smallholder farms – experiences from Kenya
Presented by Matthias Seebauer, UNIQUE forestry and land use
at the
CCAFS-FAO expert workshop on smallholder mitigation Rome, 27-28 Ocotber 2011

2. Whole farm accounting
Steps:
1.Define the organizational boundary - what parts of the farm to include?
2.Define the operational boundary - what emission sources to include?
CO2 N2O CH4
Scope 2
indirect
Scope 3
indirect
Production of purchased materials, e.g. fertilizer
Purchased electricity for own use
Scope 1
Direct emissions/
sinks

3. Kenya Agricultural Carbon Project
By promoting sustainable agricultural land management practices, the VI Agroforestry NGO supports farmers in improving their livelihoods. A more sustainable farming system will improve smallholder’s food security and generate new income sources through a better access to market. By restoring soil fertility, the Western Kenya smallholder project will as well contribute to Climate change mitigation.
Features
Kenya Agricultural Carbon Project
Farming systems
• Small-scale, subsistence agriculture
• Average farm size: less than 1 ha
• Mixed-cropping systems
Project developer
VI Agroforestry (also advisory agent)
Aggregator
3000 Registered farmer self help groups covering an area 45,000 ha with about 60,000 farms
Emissions accounted
Fertilizer use, N-fixing species, biomass burning, tree biomass, soil organic carbon

4. Field preparation
for maize planting
Soil terracing to prevent from
Water erosion
Calliandra forage to
increase dairy goat yield
Composting preparation for
Soil fertility
Leguminuous planting for
Soil fertility & fuelwood
Activity
monitoring Project objectives:
•Restoring agricultural production and increasing productivity
•Reducing climate change vulnerability
•Selling emission reduction

5. Smallholder farms in Western Kenya

6. General methodological approach
Activity data X Emission factor
Emission factor = Default value
•IPCC values
•Direct measurement
•Modeling local default values

7. Activity Baseline and Monitoring Survey approach (ABMS)
ABMS farmer
ABMS farmer
ABMS data analysis & management
Soil carbon modelling
Input data
Available datasets
Input data
Model output: default emission factors Activity data & adoption rate
ABMS farmer
Reviewed comparative study
Emission accounting
Project area
•Sample unit is the whole farm, where members of the family will be interviewed
•ABMS farms are permanent throughout the lifetime of the project
•Survey intervals depending on the adoption of SALM practices (annual to 3-5 yrs.)
•Structured interviews

8. Activity Baseline and Monitoring Survey approach (ABMS)
Project requirements
ABMS
Examples
Synergies with project management & extension
Project boundaries
Identification of project areas (GPS farm tracking)
High residue crops areas, tillage areas,
Land use classification & prioritization
Baseline - activities
Identify the actual agricultural management practices
Residue management practices, tillage, manure management practices , crop area, existing trees
Training needs assessment, identification of primary fields for extension and training, sensitization
Project - activity monitoring
Identify adoption of SALM practices
Improved crop land management , mulching, composting…
Project impact assessment, farmer’s commitment
Baseline - soil model input data
Organic matter inputs (biomass and manure); soil cover
Annual crop yields, rotational patterns, crop areas, livestock & grazing assessment
Livelihood assessment, Livestock management
Project - soil model input data
Organic matter inputs (biomass and manure); soil cover
Changes in crop productivity, manure management, crop areas
Food security monitoring

9. 28%/18%
0.9/0.5 t C/ha/application
Total land 0.7/1.1 ha
Adults 2.6/2.7
Children 3.2/4.4
>80% traditional mud houses
Water scarcity 1-4 months 12%/31%
Food security < 6 months 46%/21%
Energy source > 80% wood/charcoal
Farm household Kisumu/ Kitale
Agricultural land
0.5/0.8 ha
2.6/3.2 fields
Grazing land
0.1/0.1 ha
Legend
X/X = Kisumu/ Kitale project location
X = average figure in the project
X% = % of farmers in the project location
% = adoption rate
Chemical fertilizers
24%/84%
Crops
Other crops
(Sorghum, Sweet potatoes, Cassava, Sugarcane, etc.)
Maize 97%/98%
57%/32% of crop area
Beans 31%/63%
16%/22% of crop area Grains
Residues
Residues Beans
1st season 571/1172 kg/ha
2nd season 351/898 kg/ha
1st season 130/156 kg/ha
2nd season 90/276 kg/ha
Livestock 17/20 Dairy cows 4/3 68%/73%
Poultry
10/16
84%/91%
Goats/ Sheep
4/1
76%/49%
Trees on cropland
1.5/6.6 t dm/ha
 45%/53%
Organic inputs
Compost
9%/37%
75%/64%
Mulching
6%/23%
45%/30%
Cover crops
13%/7%
 83%/30%
ABMS farm analysis

10. Modeled Emission factors
Use of local default values based on parameterized (ABMS data) model (RothC) that has been validated via research
•Soil organic carbon
•Fertilizer use, N-fixing species, biomass burning, tree biomass  application of IPCC default values and existing tools (e.g. CDM tools)
Introduction of mulching
Composted manure
Cover crops
Increasing tree cover
Kisumu (tCO2/ha/year)
1st season
0.29
0.25
0.41
1.60
2nd season
0.20
0.27
Kitale (tCO2/ha/year)
1st season
0.25
0.12
0.47
1.69
2nd season
0.21
0.13

11. Conclusions
Experience from the Kenya case study shows that whole farm accounting systems should:
•be designed to achieve multiple benefits apart from carbon accounting
•be transparent to guarantee ownership
•provide mutual benefits for project implementation, extension and impact monitoring
•provide general livelihood and socio-economic impact monitoring
•Farmer commitment, self-learning structures
27-28 October 2011
Activity based monitoring of smallholder farms
Matthias Seebauer

12. For further information please contact: Matthias.Seebauer@unique-landuse.de Katalin.Solymosi@unique-landuse.de
Image sources: - http://www.soultravelmultimedia.com/ - http://dogwoodinitiative.org - http://www.regionalentwicklung.de
- Vi Agroforestry

13. Whole farm accounting - Overview of existing methods
Farm
Product
Tier 1
• LCA of cocoa in Ghana
• Farm level LCA of dairy
farms in Southern
Germany
• DEFRA study on
agricultural commodities
• Evaluation of European
livestock systems
Tier 2
• Australian FullCAM Tool
• UK farm-based GHG accounting
tools (e.g. CALM)
• US Comet-VR
• Unilever Cool Farm Tool
Tier 3
- Direct measurement
- Activity based estimation
- Activity monitoring and
modeling
• Activity based modeling
approach in the Western
Kenya Smallholder
Agriculture Carbon
Finance project
• Farm level GHG accounting for
dairies in NL

14. Suitability to smallholder conditions
Whole farm considered
Complexity
Data requirements
Technical requirements
Usefulness for smallholders in developing countries
1. Farm tools derived from national GHG inventory systems
yes
Very high
Very high
high
?
2. Whole farm tools for commodities
yes
high
high
low
partly
3. Methods combining activity monitoring and modeling
No, only certain practices
moderate
moderate
low
high
4. Product based accounting systems
For some small- holders
high
high
low
possibly

15. Discussion
-The question for smallholders: why monitor?
 accounting for carbon credits?
 meeting compliance requirements in the future?
 to take part in outgrower schemes (carbon footprint offsets for large companies)
 keeping track of production factors (soil quality, water use, yields, etc.)
-Important: the goal should determine the design of the tool
27-28 October 2011
Whole farm accounting for smallholders in developing countries – an overview of methods
Matthias Seebauer

16. Managing uncertainty
3 broad sources of uncertainty:
–related to land-use and management activities,
–related environmental data, and
– SOC default values
Uncertainty in the activity-based crop monitoring contributes to uncertainty in the soil carbon model-based estimate in a linear fashion
Field level:
–ABMS sampling procedure  random errors
– interview situation  systematic errors

17. Addressing uncertainty – interview situation

18. •Training of surveyors
•Awareness of potential error sources during the interview
•Pretesting of the ABMS
•Plausibility checks
•Retesting 10% of samples
Addressing uncertainty – interview situation

19. •Required precision level:15 % at the 95% confidence interval
•Mean values, standard deviation and standard errors of residue and manure production are calculated
•Lower and upper bounds of the confidence interval are calculated for each model input parameter
•Soil model response is calculated with the minimum and maximum values of the input parameters  The range of model responses demonstrates the uncertainty of the soil modelling
Uncertainty of input parameters – random errors