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Seebauer Unique methods oct 2011


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Presentation for CCAFS - FAO workshop
Smallholder Mitigation: Whole Farm and Landscape Accounting

27 - 28 October 2011

Published in: Science
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Seebauer Unique methods oct 2011

  1. 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. 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. 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. 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. 5. Smallholder farms in Western Kenya
  6. 6. General methodological approach Activity data X Emission factor Emission factor = Default value •IPCC values •Direct measurement •Modeling local default values
  7. 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. 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. 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. 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. 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. 12. For further information please contact: Image sources: - - - - Vi Agroforestry
  13. 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. 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. 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. 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. 17. Addressing uncertainty – interview situation
  18. 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. 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