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

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

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

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

Smallholder farms in Western Kenya

General methodological approach

Activity data X Emission factor

Emission factor = Default value

• IPCC values

• Direct measurement

• Modeling local default values

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

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

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

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

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

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

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

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

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

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

Addressing uncertainty – interview situation

• 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

• 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