The Kasigau Corridor REDD Project
Phase I – Rukinga Sanctuary

Project Document (PD) For Validation
Using the Voluntary Carbon Standard (VCS) 2007.1 / Sectoral Scope 14
VM0009 Methodology for Avoided Mosaic Deforestation of Tropical Forests
Version 9
January 31st, 2011
In partnership with the Landowner

RukingaRanching Co Ltd.

Technical Assistance Provided by

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Acknowledgements
This Project Description was developed for the Kasigau Corridor REDD Project - Phase I, Rukinga
Sanctuary by Mike Korchinsky, Founder and CEO, Jeremy Freund, VP Carbon Development, and Jessie
Parteno at Wildlife Works. The authors would like to acknowledge the following individuals andorganizations, without whom this effort would not have been possible:
Kyle Holland and Ryan Anderson, at EcoPartners for technical assistanceRob Dodson, VP African Field Ops, and the Wildlife Works Kenyan team, Jamie Hendriksen, Lara
Cowan, Laurian Lenjo, Hassan Sachedina, Patrick Kabatha, Eric Sagwe and the ranger team, Joel
Mwandiga and Mwololo Muasa and the plot sampling teamfor field support and for making this project such a success. Wildlife Works would like to extend sincere
thanks to all mentioned above, as well as the many other contributors to the project.
Asante Sana!

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

TABLE OF CONTENTS
Acknowledgements....................................................................................................... 2
Section 4 Applicability Conditions .............................................................................. 5
Section 5 Project Boundaries ...................................................................................... 7
Section 5.1 Spatial Boundaries ............................................................................................................. 7
Kasigau Corridor Phase I - Rukinga Ranch .......................................................................................... 7
The following table shows the landcover strata for Rukinga Ranch and their respective areas. Strata
sum to the total area for the Ranch, 30,168.66 ha. .............................................................................. 9Land Ownership .................................................................................................................................... 9
Section 5.2 Temporal Boundaries ......................................................................................................... 9
Section 5.3 Greenhouse Gases ........................................................................................................... 12
Section 5.4 Carbon Pools ..................................................................................................................... 12
Size Class Diameter Selection and Justification ................................................................................. 12
Section 5.5 Project Grouping ............................................................................................................... 13

Section 6 Baseline Scenario ...................................................................................... 14
Section 6.1 Obvious Agents and Drivers of Deforestation ............................................................... 14
List of Obvious Agents and drivers of deforestation ........................................................................... 14
Narrative describing why the agents of deforestation are evident ...................................................... 14
List of Project Activities designed to mitigate deforestation ................................................................ 16
A List of External Drivers of Deforestation (Covariates) Used in the Deforestation Model................. 22
Section 6.2 Participatory Rural Appraisal .......................................................................................... 22
Analysis of Agents of Deforestation .................................................................................................... 22
Analysis of Drivers of Deforestation .................................................................................................... 23
Section 6.3 The Reference Region ...................................................................................................... 23
Delineation of the Reference Area ...................................................................................................... 23
Narrative describing the rationale for selection of the reference region boundaries .......................... 23
Delineated boundaries ........................................................................................................................ 24
Infrastructure (roads, major markets, land tenure).............................................................................. 25
Defining the Reference Period ............................................................................................................ 26
Section 6.4 The Cumulative Deforestation Model.............................................................................. 28
Historic Imagery Used to Build the Cumulative Deforestation Model ................................................. 28
Sampling Deforestation to Build the Cumulative Deforestation Model ............................................... 28
Minimizing Uncertainty in the Cumulative Deforestation Model.......................................................... 34
Fitting the Cumulative Deforestation Model ........................................................................................ 34
Linear Prediction of Deforestation ....................................................................................................... 35
Estimating Uncertainty in the Cumulative Deforestation Model .......................................................... 37
Section 6.5 Soil Carbon Loss Model ................................................................................................... 37
Sampling Soil Carbon Loss ................................................................................................................. 37
Description of Soil Types .................................................................................................................... 44
Minimizing Uncertainty ........................................................................................................................ 46
Fitting the Soil Carbon Loss Model ..................................................................................................... 47
Predicting Soil Carbon Loss ................................................................................................................ 47
Estimating Uncertainty in the Soil Carbon Loss Model ....................................................................... 48
Section 6.6 Baseline Scenario for Selected Carbon Pools ............................................................... 48
Selecting the Proportion of Below Ground Biomass Removed from Large Trees.............................. 48
Selecting the Proportion of Wood Products ........................................................................................ 48© Wildlife Works Carbon, LLC 20103

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Section 6.7 Baseline Reevaluation ...................................................................................................... 49

Section 7 Additionality................................................................................................ 50
Section 8 Baseline Emissions .................................................................................... 52
8.1 Estimating Emissions from Above Ground Large Tree Biomass ............................................. 52
8.2 Estimating Emissions from Above Ground Small Tree Biomass ............................................. 52
8.3 Estimating Emissions from Above Ground Non-Tree Biomass................................................ 52
8.4 Estimating Emissions from Below Ground Large Tree Biomass ............................................. 52
8.5 Estimating Emissions from Below Ground Small Tree Biomass ............................................. 53
8.6 Estimating Emissions from Below Ground Non-Tree Biomass ................................................ 53
8.7 Estimating Emissions from Standing Dead Wood ..................................................................... 53
8.8 Estimating Emissions from Lying Dead Wood ........................................................................... 53
8.9 Estimating Emissions from Soil ................................................................................................... 53
8.10 Estimating Emissions from Wood Products ............................................................................. 53

Section 9 Project Emissions ...................................................................................... 54
9.0 Forest Fires ...................................................................................................................................... 54
9.1 Emissions from Burning ................................................................................................................ 54

Section 10 Leakage ..................................................................................................... 55
Section 10.1 Leakage Mitigation Strategies ....................................................................................... 55
Section 10.2 Delineation of the Leakage Area ................................................................................... 57
Section 10.3 The Leakage Model ......................................................................................................... 57
Sampling Deforestation and Degradation to Build the Leakage Model .............................................. 57
Fitting the Leakage Model ................................................................................................................... 61
Section 10.4 Estimating the Leakage Factor and Emissions from Leakage................................... 62

Section 11 Quantification of NERs ............................................................................ 63
Section 11.1 Determining Deductions for Uncertainty...................................................................... 63
Section 11.3 Ex-Ante Estimation of NERs .......................................................................................... 63

Section 13 Monitoring ................................................................................................. 65
Section 13.14 Monitoring of Carbon Stocks in the Project Area...................................................... 65
Summary of sampling procedures ...................................................................................................... 65
Field training ........................................................................................................................................ 66
Documentation of data quality assessment such as the results from a check cruise......................... 66
Map Showing Strata Boundaries and Plot Locations .......................................................................... 68
List of Plot Coordinates ....................................................................................................................... 70
Description of Plot size........................................................................................................................ 70
Documentation of Allometry ................................................................................................................ 70
Development of Allometry ................................................................................................................... 71
Estimated Total Carbon Stock, Standard Error and Sample Size for each Stratum and Pool ........... 83
Estimated Total Carbon Stock and Standard Error for Entire Project Area ........................................ 84
Monitoring of Deforestation in the Project Area .................................................................................. 85© Wildlife Works Carbon, LLC 20104

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Section 4 Applicability Conditions
For the Kasigau Corridor REDD Project the following conditions apply;The primary driver of deforestation is conversion of forest to cropland for annual crops, typicallymaize, as evidenced by the substantial conversion to maize in the Reference Area during theReference Period. The primary agents of deforestation are a growing population of local Taita andKamba people living in the Reference Area. Agriculture in the reference and leakage areas ispermanent and cultivation activities do not shift.

The land within the project area has been tropical dryland forest1 for at least 20 years and has been a
primary forest in its current state since recorded times2. The Project Area forest has an average
canopy of 39% and mature tree height of 5-10m, and therefore has qualified as forest as defined by
FAO 2010, or that of the definition of forest set by the residing designated national authority (DNA)(10% canopy, 4m height) for the project country for a minimum of 10 years prior to the project startdate (VCS, 2008)



No biomass is harvested for use in long-lived wood products in the project area under the with-project
scenario. Therefore, carbon sequestered in long-lived wood products under the project during any
monitoring period may be accounted for as zero.

The project is located in a semi-arid tropical region.

The primary agents of deforestation are local Taita and Kamba peoples, with a small minority of other
tribes who moved in during the El Niño rains of the mid 1990s, when the land was still sparsely
populated, or to work as herders for the former cattle operations. Tribal mobility for farm land in
Kenya is very low, as Kenya’s population is relatively high everywhere that leakage could potentiallyshift, and the population in the Reference Area outside of the Project Area, and the proposed Phase II
Project Area (see map in Section 6.3) is high. There exists no opportunity for the agents ofdeforestation to shift their activities outside the leakage area.

The project is not mandated by any enforced law, statute, or other regulatory framework.

The project area does not contain organic or peat soils. (see soils Map in section 6.5 below).



A reference area has been delineated meeting the requirements described in sections 6.3.1 and 6.3.2of the methodology VM0009, 'Methodology for Avoided Mosaic Deforestation of Tropical Forests'(MED), including the minimum size requirement.



As of the project start date, historic imagery in the reference region exists with sufficient coverage tomeet the requirements of section 6.4.2 of the MED.



A wide range of project activities have been implemented to mitigate deforestation by addressing theagents and drivers of deforestation as described in section 10.1 of the MED.

The project start date and end date and crediting period are clearly defined (see Section 6.3).

1

UN IPCC, Good Practice Guidance for LULUCF, Table 3A.1.8;
Earliest record that has been located is dated 1895 which identifies the area as forested [Hobley 1895 – Upon aVisit to Tsavo and the Taita Highlands – The Geographical Journal 1895 Vol 5 No 6 pp 545-561]
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Kasigau Corridor REDD Project Phase I – VCS PD Requirements
Wildlife Works (the Project Proponent) has access to the leakage area to sample forest degradation,
as evidenced by implementation of the leakage plots used to create the leakage model.

The lag period for the cumulative leakage model was estimated after the project start date but before
the end of the first monitoring period, and initial leakage plot measurements showed that no activityshifting leakage had occurred prior to the estimation of the lag period.

The project area does not include lands designated for legally sanctioned logging activities.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Section 5 Project Boundaries
Section 5.1 Spatial Boundaries
Kasigau Corridor Phase I - Rukinga Ranch
This Phase I Project Document covers 100% of the land known as Rukinga Sanctuary (see map below)
which is all that 74,516 acres (30,168.66 ha) of land originally known as Rukinga Ranch, LR 12263,
historically reduced by subdivisions 12263/1 and 12263/2 at dates prior to the start date of this project.
Project lands conform to the latest VCS definition of forest, with an average canopy cover of 39%, andmature tree height at 5-10m, and have been primary forest since historic times. A GIS database with
canopy measurements for Rukinga Ranch is available upon request.

Figure 1. Rukinga Ranch REDD Project and Reference Region Spatial Boundaries© Wildlife Works Carbon, LLC 2010

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Figure 2. Rukinga Landcover Map, Classified from Landsat 7 ETM+ Acquired February , 2003

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The following table shows the landcover strata for Rukinga Ranch and their respective areas. Strata sum
to the total area for the Ranch, 30,168.66 ha.
Stratum

Area (ha)ag active
713.7
dryland forest strata 1+2
6,883.6
dryland forest strata 3
5,651.1
dryland forest strata 42,773.4
dryland forest strata 58,133.4
dryland forest strata 6
4,345.5
Grassland
1,610.9
montane forest
570.6
Total area:
30,168.7
Table 1. Landcover Strata area for Rukinga Ranch, Febrary 2003
Using these values, forested area for the Sanctuary at project start date is calculated as:
27,844 / 30,168.7 = 93% forested 10 years prior to project start date
Land Ownership
Rukinga Sanctuary is privately owned by Rukinga Ranching Company Ltd., the majority shareholder
being Mike Korchinsky, Founder & CEO of Wildlife Works. The leasehold on the title will be due forrenewal in 2038, and can then be renewed for either 33, 66 or 99 years under Kenyan law, at theleaseholder’s option. Wildlife Works has had a wildlife conservation and land management operating
agreement with Rukinga Ranching Company Ltd. since 2005, and more recently acquired the carbon
rights from the landowner, Rukinga Ranching Company Ltd. after a process of Free Prior and Informed
Consent (FPIC), through a Carbon Rights Agreement/Conservation Easement that was approved by a fullvote at an AGM of the Shareholders of Rukinga Ranching Company Ltd. on February 13th 2009. At that
AGM the shareholders were given a presentation - explaining in lay terms - the potential of the REDD
project, a copy of which has been provided to the validator. Following the presentation, the shareholders
unanimously approved the pursuit of this opportunity by the Managing Director and majority shareholderof the land. This decision was ratified again unanimously by an extraordinary general shareholder
meeting of Rukinga Ranching Company Ltd. on December 9th, 2009, at the request of the CCB
Validators, Scientific Certification Systems, Inc (SCS).

Section 5.2 Temporal Boundaries
The project was commenced on January 1, 2005. Since this time, Wildlife Works has been successfully
protecting Rukinga Ranch from agricultural encroachment (deforestation), wildlife poaching and forest
degradation. The Phase I Project is 30 years in length. The project will therefore end on December 31,
2035.
Wildlife Works took financial responsibility for all conservation activities within the Project Area as ofJanuary 1st 2005, as a result of the agreement between Wildlife Works and Rukinga Ranching Company,
Ltd., the landowner, a copy of which was provided to the Validators.

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Wildlife Works began conservation activities centered around our ecofactory prior to 2005, but all
activities were located outside the Project Area. The figure below illustrates the relationship between thecompanies involved with Rukinga Sanctuary.
The VCS rule for AFOLU projects starting after Jan 1 2002 states that there is no specific timerequirement for validation and verification. Language exists in the MED to clarify the type of project
activities that qualify a project for a historical project start date, and Wildlife Works fully conforms to these
MED requirements.
Wildlife Works will monitor the project every year, producing accurate and credible documentation for all
VCS required project accounting. Wildlife Works will validate the project once every 5 years throughout
the life of the project until the project end date.
Per VCS minimum requirements, a baseline revision will be performed once every 10 years, on January
1, 2016 and January 1, 2026. If the VCS standard regarding baseline revision periodicity changes in the
future, Wildlife Works will commit to performing baseline revisions whenever specified by the VCSA.

historical reference period

project period

2034
2033
2032
2031
2030
2029
2028
2027
2026
2025
2024
2023
2022
2021
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
1989
1988
1987
Ve

Ve BR

Ve

Ve BR

Ve

Figure 3. Project Timeline (Ve = verification year, BR = baseline re-evaluation year)
Rukinga Sanctuary is comprised of primary Acacia-Commiphora dryland forest, and therefore conforms to
the minimum requirement for project land to have qualified as "forest" (10 years per VCS 2007.1). The
landcover classification shown above in figure 2 was performed on ETM+ imagery acquired from theLandsat 7 satellite on February 6, 2003. As both dominant tree species in this ecosystem (Acacia andCommiphora) grow very slowly (some trees on Rukinga Ranch are estimated to be over 300 years old),
we make the assumption that Rukinga ranch was in virtually the same forest state in 1996 as it was in
2003.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Section 5.3 Greenhouse Gases
The dominant method of deforestation in the Kasigau corridor is conversion to subsistence agriculture byslash and burn techniques. As such, only Carbon Dioxide (CO2) was selected as a source for greenhousegas emissions in the project. Although Methane (CH4) and Nitrous Oxide (N2O) are also greenhousegases, they are conservatively excluded from this project, as neither of which are present to a significant
degree in the Kasigau corridor region.

Section 5.4 Carbon Pools
The following table indicates carbon pools required for consideration under the MED, including thosepools that are mandatory, optional and respective justification for selection under this project:

Required

Included in
Project?

Yes

Yes

Major pool considered

Yes

Yes

Major pool considered

Above-ground non-treebiomass

Optional

Yes

Major pool considered

Below-ground large
tree biomass

Optional

Yes

Major pool considered

Below-ground small
tree biomass

Optional

Yes

Major pool considered

Below-ground non-treebiomass

Optional

Yes

Major pool considered

No

No

Conservatively excluded

Standing dead wood

Optional

Yes

Major pool considered

Lying dead wood

Optional

No

Conservatively excluded

Soil

Optional

Yes

Major pool considered

Yes

Yes

May be a significant reservoir under thebaseline scenario

Pool
Above-ground large
tree biomass
Above-ground small
tree biomass

LitterLong-lived wood
products

Justification

Table 2: Carbon pools selected for inclusion in the project and respective justification
Size Class Diameter Selection and Justification
Expert knowledge of the agents of deforestation and cultural practices in the Kasigau corridor ecosystem
indicate that farmers invariably burn all stumps in the process of clearing land for agriculture, We
therefore do not differentiate large trees from small trees for this project, and assume that all stumps(below-ground large tree biomass) are burned during agricultural conversion. Credible evidence can beproduced through farmer polling and or interviews with Wildlife Works resident community liaison, Laurian
Lenjo, who has intimate knowledge of farming practices throughout the corridor, knows many farmers
personally, and advises Wildlife Works regarding issues such as this.

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Section 5.5 Project Grouping
The Kasigau Corridor Phase I project is not a grouped project. Therefore, no supporting evidence is
supplied.

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Section 6 Baseline Scenario
Section 6.1 Obvious Agents and Drivers of Deforestation
Wildlife Works staff and employees possesses an incredible depth of local knowledge regarding both theReference and Project Areas, as a result of direct involvement and integration with this community since1997. As such, it was considered unnecessary for us to conduct a participatory rural appraisal (PRA) todemonstrate a clear understanding of the principle driver of deforestation in the reference region. This isobserved as conversion of dryland forest to annual subsistence cropland by two main groups of local
agents during the historic Reference Period.
List of Obvious Agents and drivers of deforestation


Local farmers from the Taita Tribe (approximately 95% of local population according to the 1999
Kenyan census) deforesting for cropland.



Farmers from the Duruma Tribe (approximately 5% of local farmers – from 1999 Kenyan census)
deforesting for cropland.

Both of the aforementioned populations began aggressively converting land in the 1990s prior to Wildlife
Works' arrival in the area in 1997. After rendering it impossible to illegally farm private group ranch land,
immigration to the area virtually ceased, and in fact many Duruma families returned to their primary farms
at the Coast, while most Taita farmers remained, establishing themselves as the dominant projectcommunity.


Illegal charcoal trade – typically first element of degradation as it generates cash to fund theclearing of the land for subsistence farming.

Large scale Tribal mobility in Kenya today for access to cropland is very restricted, as Kenya is fairly
highly populated, certainly in areas of adequate rainfall for farming, and the traditional tribes in any given
area typically prevent the incursion of immigrants from outside.
Narrative describing why the agents of deforestation are evident
Wildlife Works contends that the reasons for the presence of the agents of deforestation is obvious.
Agricultural conversion has occurred adjacent to - and even into - the Project Area during the historicalreference period just prior to Wildlife Works’ arrival in the area in 1997, and continues in a heavy andvisible manner in the reference region today. Standing on the boundary of the Project Area, one can see
the stark contrast to the converted land outside the Project Area without effort. This makes thedeforestation process extremely evident. Forest degradation is in turn conspicuous judging by the amountof charcoal sale depots alongside the main Highway (A109) that leads from the Reference Region to theclosest major coastal city, Mombasa.

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Looking back towards Rukinga Sanctuary from deforested area in the Reference Area.
Descriptions of agents and drivers including any useful statistics and their sources
Local Taita Farmers have traditionally farmed the fertile cloud forested hills of the Eastern Arc Mountains,
Kasigau, and Taita and Sagalla Hills. As their population exceeded the carrying capacity of the montane
land they relocated to the dryland Acacia-Commiphora forest that dominates the lower elevations of the
district. However, their traditional farming practices did not sustain, due to extremely low average rainfall.
After colonizing all available land with permanent water sources, they began to clear any available
unprotected land, hoping that the unpredictable rainfall would bless them with a crop. The larger blocks ofremaining land in the area outside of communally owned land protected by local administrations wereprivately held group ranches - designated as cattle carrying areas - for the communities of the hills in the
1970s. However, due to the remoteness of these areas and a lack of permanent water sources, theseareas were never been developed as cattle ranches, and remained as natural forest over the years until
the mid 1990s, when rainfall patterns initiated a population boom in the area. This boom was also
facilitated by the improvement of the main Mombasa highway (A109) and a local arterial road that runs
along the edge of the Rukinga project area.
Duruma farmers, originally from the Kenyan Coast, came to the area in the mid 1990s due to anomalous
El Niño rains, when there was still a very small Taita population living in the Dryland forested areas thatnow comprise the reference region. In many cases these Duruma families were lead by second wives ofa man whose primary family was at the Coast, and who farmed this area on squatter land, sending the
produce home to the primary family at the Coast. Because both of these agents of deforestation did notpossess legal land tenure, they never invested in the land, and chose to simply farm with no inputs until
the soil was depleted. They subsequently cleared more forest and began engaging in an annual depletion© Wildlife Works Carbon, LLC 201015

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cycle. Wildlife Works addressed this issue by creating a land cooperative, providing farmland for thoselandless farmers who were deforesting the area3.
List of Project Activities designed to mitigate deforestation
The Project Activities designed to mitigate deforestation include (in order of importance);
Wildlife Works Sustainable Development Initiatives
Wildlife Works has implemented a wide range of sustainable development initiatives at Rukinga over the
past ten years, and is committing to continue with a new range of innovative co-benefits for thecommunities that are in the Project Zone once the funding for the REDD project begins. These initiatives
collectively form the basis of Wildlife Works' deforestation mitigation strategy. An implementation
schedule for these Project Activities, complete with timelines and budgets, was shared with the Project
Validator.
Organic clothing factory3

Local history obtained through multiple conversations with community members over a period of 12 years.

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Wildlife Works' core project has been the construction of an Ecofactory. We employed over 150 people
from the local community during construction, and now trained and employ young women from thecommunity to sew organic cotton clothing, which we export to the US and Europe for sale on the internet
and in fashion boutiques. First and foremost, we plan to continue the level of investment we have been
making for the past ten years in this Ecofactory.
In addition, going forward we have several new Project Activities in this area;Adding capacity – we plan to immediately rehire ten women previously trained by Wildlife Works
but let go due to lack of funding

Factory Expansion – we plan to complete a second production cell, capable of dyeing and screen
printing fabric so that we can manufacture finished goods completely within our complex without
having to send out for dye and print. We believe this will make our production capability much
more attractive to a wider range of customers, and reduce our production costs. The walls for thisproduction cell were built back when the first sewing cell was built, but it needs roofing, flooring,
electrification and importation of the dye and screen print equipment acquired by Wildlife Works in
the US. A full budget for this factory expansion was provided to the Validator.



Increase Fabric Inventory and Produce 2010 Collection – we have been unable to produce a new
fashion collection from Rukinga for the past two years due to lack of funding, so we plan to initiate
a new Collection immediately in 2010, once carbon funding is received. This new collection will
be sold online and will relaunch our brand into the international marketplace, now with 100% of
production being done in Rukinga. This is critical to our long term strategy to wean local people
away from agricultural employment that conflicts with wildlife, and to introduce elements ofsustainability to our model for post carbon finance in 20 years.

Organic Greenhouse
Widlife Works established an organic greenhouse to grow citrus trees, which we sell at a discount to local
farmers so that they may plant a tree for shade that has the added benefit of earning them income. We
use the funds from the citrus sales to fund the growth and distribution of free agroforestry species such as
Neem and Moringa Oliefera to local farmers, to meet their medicinal, nutrition and fuelwood needs. With
the financing from the Carbon project, we plan to initiate a number of new Project Activities in this area;

Wildlife Works Organic Greenhouse© Wildlife Works Carbon, LLC 201017

Kasigau Corridor REDD Project Phase I – VCS PD Requirements
Expansion of our core greenhouse at Rukinga HQ to add a second shade house, doubling ourcapacity by adding two additional greenhouse workers from the local community. A full budget forthis activity has been provided to the Validator.

Establishment of 5 nurseries in the villages surrounding the Eastern and Southern boundary of
the Project Area and Kasigau Wildlife Corridor: Maungu, Itinyi, Sasenyi, Buguta, Makwasinyi.
Each nursery will utilize the same template and budget as for our own shade house (see above),and each nursery will employ an additional 2 members of the local community, totaling 10 new
employees. Each nursery will be responsible for working with their immediate community to planand implement a cash crop and implement fuelwood and construction pole strategy for that
community. They will plant the same combination of tree species currently being grown in ourown greenhouse. Once again, for the foreseeable future, the nurseries will provide agroforestry
species and native hardwood seedlings for free, while the sale of cash crop trees will contribute to
the budget. We will provide training in organic agroforestry and our organic Project Team Leader,
Joseph Mwanganda, will manage these new nurseries.



We will continue a project activity through which we provide relatively small amounts of elephantdung from the Rukinga Sanctuary to a local women’s group called the Imani Women’s Group.
Periodically and at their request, they can use the dung as a growing medium for theircommercial mushroom farm, which is housed in a small shed within the women’s groupcompound and provides a good income to the group, with little to no negative impact on
biodiversity or land use.



We will restart a 3 year reforestation project on the slopes of Mt. Kasigau, working closely with
the Kasigau Conservation Trust (KCT) to plant 20,000 indigenous hardwood trees over the next
three years in one of the Project Zone’s High Conservation Value (HCV) areas. This project aims
to replace trees taken out for charcoal or construction over the past years. We will be using thenursery built at Makwasinyi and Sasenyi (see above) as the base for propagating the seedlings ofthe indigenous trees in the first year, until Phase II of this project, at which time we plan to add an
additional 4 nurseries on the South and East sides of Mt. Kasigau. We will be providing financial
rewards to community members who plant those trees and protect them through two full years.
We are confident that this project will go a long way in restoring the habitat and conserving theendemic species in this region. Its model might hopefully be emulated in other parts of thecountry so as to stop the loss of forests in Kenya. We have involved the community in all facets ofthe project, from the formulation of this proposal, the monitoring and as indicated in its
implementation. This has ensured that the community has taken it up as its own initiative and will
see it through even in the absence of Wildlife Works, thus ensuring sustainability.

Dryland Farming scheme
Our most recent project involves working with the Kenyan Agricultural Research Institute (KARI) to
cultivate a climate appropriate plant called Jojoba (Simmondsia chinensis) that provides a cash crop
through its seeds and is also extremely drought tolerant, non invasive and has the added criticallyimportant benefit that it is not eaten by any wildlife, birds or even insects. It is therefore viewed as theultimate non-conflict crop. Wildlife Works is currently studying the impact of various levels of plantmaintenance and irrigation on plant seed and oil productivity, with the idea that we can provide local© Wildlife Works Carbon, LLC 201018

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

farmers root stock to establish their own plants. They can then determine how much they can likely makeif they are willing to put a certain level of effort into the plant maintenance. There are three specific Project
Activities associated with this scheme;Complete our involvement in Phase I of the joint Research Project, taking place on the Jojoba
fields at our HQ which will end in 2010

Develop a full business plan on how to create a self sustaining venture to outplant jojoba in thesurrounding community farmland, providing the local farmers with a drought tolerant and nonconflict crop.



Source private funding to implement the Jojoba outplanting business plan, either from donors,
private investors, Government of Kenya, or some combination thereof.

Wildlife Works REDD Forest and Biodiversity monitoring
There are a number of specific Project Activities in the Kasigau area that Wildlife Works will complete
throughout the project lifetime;Continue daily ranger patrols to monitor of the health and vitality of the Project area – we havebeen performing daily patrols for almost fourteen years, and our rangers are very skilled at
identifying potential threats to the forest and biodiversity of the Rukinga Sanctuary.

Using Carbon finance, we have added a new permanent Ranger Station at the SoutEast end of
the Project area, furthest from our headquarters. This supports the addition of a full new sectionof 8 Wildlife Works Rangers, recruited and trained from the local community, along with a new
Team Leader promoted from within our existing force. This is primarily to prevent incursions ofillegal cattle from that direction, to make patrolling the far boundary easier, and to develop closer
working relationships with the Makwasinyi community.



We have made a significant investment in modernizing our patrol fleet, by purchasing three new
Toyota Land Cruisers, to reduce the carbon emissions from our patrol vehicles, and to reduce thecost of operating and maintaining them. Perhaps most importantly, we wish to ensure that wehave a reliable fleet to support constant patrol activities. We have thus retired our oldest patrol
vehicle, a 1980 Toyota Land Cruiser – HJ45 Diesel.



We will improve our ability to monitor the HCV species in Rukinga by adding a dedicated Ranger
Patrol, the HCV Ranger Team, which will be responsible for constant tracking and monitoring of
the HCV species. Unlike the general ranger teams that are patrolling geographic sectors of the
Project area, this dedicated team will be recruited from the existing ranger Patrols based on
tracking ability and biodiversity knowledge, and the 4 members of the patrol will be backfilled in
the geographic Ranger teams by hiring new rangers from within the community.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Wildlife Works ranger force team members
Additionally, to improve our monitoring of HCV species, we plan to establish a GIS center of
excellence at Rukinga HQ, for which we have hired one full time Kenyan GIS expert, and set up astate of the art GIS computer station. This individual is responsible for liaising closely with the
HCV Ranger Team, with the Ecotourism partner in the Project Zone, and with all Wildlife Works
ranger patrols to maintain daily sighting logs of the HCV species. They will also be responsible formonitoring those other species of ecotourism value, such as Elephant, Buffalo, Giraffe andCommon Zebra. A biodiversity database is being collected with recordings made from standard
daily ranger patrol sheets.



We will begin annual monitoring of our carbon inventory by revisiting 20% of our permanent fixedplots each year to resample the trees, shrubs and grasses, looking for degradation or
improvement in existing stocks. In addition we plan to acquire remote sensed imagery to prove
the absence of large scale deforestation or boundary incursion. Wildlife Works subsidized thepurchase of a gyrocopter by our VP African Operations, Rob Dodson, which he will use toperform periodic aerial monitoring of the project area and reference region.

We will investing in third auditors to verify project carbon inventories and project progress every
five years.

Ecotourism
Wildlife Works has located an ecotourism provider who now operates a safari camp in the center of the
Rukinga Sanctuary. This provides employment for safari guides and other service jobs, as well a market
for local produce. In the absence of REDD funding, and our continued protection of the biodiversity in the
project area, this business would likely lose its support.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Ecotourism Center at Rukinga - "Camp Tsavo"
The primary ecotourism tenant, called Camp Kenya, brings groups of young people to the camp from theUK who stay at the camp, but spend their days in the communities of the project area implementing
community projects, providing a significant benefit to the communities. Wildlife Works has negotiated for asecond partner, called EcoTraining, to come to Rukinga. They are a South African safari guide training
company, and have agreed to support the placement of local youth into their program, on a space
available basis, to be trained as Safari Guides at a very high quality level. As a project activity, we plan toprovide funding for two local youth per year to go through their program and be trained as safari guides.
Finally we plan to explore a second high end ecotourism retreat on Rukinga, to bring more jobs andincome to the Project. This effort will be self funded by outside investment partners, and represents asignificant capital expenditure.
School Construction and Bursary Scheme
When Wildlife Works arrived in the area, there were almost no schoolrooms, no books and no desks.
None of the necessary infrastructure for children to have a hope of a decent education exisited. We
began with a school building program, and over the years we have partnered with the community andvarious identified donors to build 18 classrooms throughout the district. We also build desks, and ouroriginal Kenyan manager Alice Ndiga launched a school bursary program, which she administers, called
the Kelimu Trust, that has sent over 65 local children through private high school, and several on tocollege.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements.
Old Kale School – no floor, no desks, one mud room New School block built by Wildlife Works
Once the Project Carbon funds start to come in we plan on two specific Project Activities in this scheme,
as outlined in the Project Implementation Schedule provided to the Validator;
Provide Wildlife Works direct funding to send 5 new students through four year secondary
schools program and on to three or four year College/University should they qualify – this is anannual commitment of $2000 in the first four years of Secondary School fees and between $5000
and $10000 a year in college/university fees.

Establish a Wildlife Works School Construction and Maintenance fund, by hiring a dedicated staff
person to manage the fund with project management skills and ability to write grant proposals,
and provide $10,000 per year in Wildlife Works funding above and beyond the compensation of
the fund manager to seed school construction and maintenance projects in the Project Zone. The
Fund Manager will work closely with the local District Education Officer, and the existing schoolboards in the area to determine which projects should receive funding each year.

Please note that this document outlines minimum levels of financial commitment to project activities, andfunding levels will be revisited as project financing becomes more clear based on carbon credit sales
each year.
A List of External Drivers of Deforestation (Covariates) Used in the Deforestation Model
We explored the most obvious covariate - population - and found that it did not significantly affect the
deforestation baseline rate. We ultimately decided to not use any covariates, basing deforestation on
historical information alone.

Section 6.2 Participatory Rural Appraisal
As a result of Wildlife Works extensive knowledge of the Reference Region and Project Area, we are
intimately familiar with the agents and drivers of deforestation and therefore we found it unnecessary to
perform a Participatory Rural Appraisal.
Analysis of Agents of Deforestation
This section is Not Applicable.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Analysis of Drivers of Deforestation
This section is Not Applicable.

Section 6.3 The Reference Region
Delineation of the Reference Area
The Reference Region for the Kasigau Corridor Phase I project was chosen to specifically address the
behavior of the local agents of deforestation as well as the drivers of deforestation for the ecoysystem.
Specifically, the area is comprised almost entirely of local inhabitants engaging in subsistence farming
practices. In the area that are not zoned for group ranch ownership, local agents practice slash and burnagriculture. This type of deforestation is prevalent and exclusive, as the dominant species (Acacia /Commiphora) are not commercially viable. For this reason, the main agents of deforestation, as describedin section 6.2.1 consist of local community members, and the primary driver, as will be tested in section
6.4, the Cumulative Deforestation Model, is population.
Narrative describing the rationale for selection of the reference region boundaries
The Reference region boundaries were chosen to address the behavior of the agents of drivers ofdeforestation in the Kasigau Corridor. The reference area is bounded by Tsavo West national park to thewest, Tsavo East national park to the Northeast, and group-owned ranches on all other boundaries. The
area is therefore bound on all sides by either protected areas or tracts owned by groups under agreementwith Wildlife Works for Kasigau Corridor Phase II Project. As such, unplanned deforestation will
necessarily occur within the delineated reference area.
The region was specifically chosen to embody a region that has seen deforestation of a nature typical forthis ecosystem. In fact, the area forms a corridor between the two aforementioned national parks, with
virtually no extraneous space. As such, Wildlife Works is confident that by studying the area delineated as
the reference region for this project, the culture and behavior of the agents and drivers of deforestation
will be completely captured.
Additionally the geographic qualities of the reference region are similar to those of Rukinga Ranch. Forest
type, soils, river density, and infrastructure are similar. The reference region does encompass the Taita
Hills area; Wildlife Works feels that it is not only appropriate, but necessary to include these hills in thereference area, as they have been subject to subsistence conversion to agriculture as much, if not to agreater extent, than the surrounding lowlands. It would be inappropriate to omit the hills simply due totheir elevation. The reference region was also chosen such that the agents of deforestation would, andare perfectly able, to act within its boundaries as an alternative to deforesting within Rukinga Ranch itself.
The following maps demonstrate the geographic features of the reference area that render it appropriate
for evaluating the baseline scenario for this project.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Delineated boundaries

Reference Area and Land Tenure Boundaries, Roads and Major Markets

Reference Area Slope© Wildlife Works Carbon, LLC 2010

Reference Area Aspect24Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Reference Area Elevation

Reference Area Soil Classes

Reference Area Thematic Landcover
Infrastructure (roads, major markets, land tenure)
These characteristics are shown on the main maps of the reference region.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Defining the Reference Period
The reference period is defined by the following historic events;


Population in the Taita Hills began to exceed the carrying capacity of the fertile hill top lands inthe late 1980s, and families began to move down into the dryland forested areas.



Local lore has it that the Coastal Duruma first came to the Reference Area adjacent to the Project
Area in the early 1990s when they were promised land by a local Taita politician who had taken a
Duruma wife in return for their votes in local elections. The only problem was he promised themland he did not own that falls within the Reference Area for this project. The Duruma arepolygamists, and therefore the common practice was for a husband to bring his second or third
wives to the Project area to establish agricultural plots. The husband would leave them in thebush with their small children and return to the Coast where they would spend most of the timewith the family of the first wife. The husband would then return at harvest and claim a large
portion of the crop should there actually have been a crop, and would take it back to the Coastfamily. These single parent families were rarely successful at agriculture, but continued to clear
land aggressively hoping they would find the perfect location where the tragically localized rainfall
patterns would find their land. In the interim, the teenage males would snare animals for food, theDuruma being much more comfortable in the bush than Taita farmers.



El Niño Rains in the mid 1990s caused more landless families from both Taita and Coastal
Duruma communities to move to the area, as they could get successful maize harvests, and theland was still relatively under populated.



The main Nairobi - Mombasa highway that passes through the Reference Area (A109) fell into
horrible disrepair in the late 1990s, so the high volume of trucks that travel up and down the
highway from the main port of Mombasa to the interior of Kenya and beyond (as far as Zambia)
was forced to make frequent maintenance stops. As a result, small towns such as Maungu, whichis the town directly adjacent to Rukinga, sprang up along the highway.



There are no significant economic factors involved in selection of the Reference Period, as the
local population consists primarily of subsistence farmers, producing for their own consumption.



These factors lead to a reference period beginning in February, 1987, before which there wasvery little population and very low deforestation, and extending to the Project sart date, January
1st, 2005. Wildlife Works then located historical imagery covering as much of the reference area
as possible, both on a spatial and temporal basis. The following were found and used in building
the cumulative deforestation model (CDM).

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Figure 4. Historical imagery used for the Cumulative Deforestation Model (CDM).
It should be noted that the MED makes use of the post 2003 Landsat SLC-OFF imagery, that was in turnaccessible and useful in the deforestation analysis.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Section 6.4 The Cumulative Deforestation Model
Historic Imagery Used to Build the Cumulative Deforestation Model
The imagery located for the reference period provided 100% “double coverage” over the reference area.
Upon request, the valdiator will be shown a double coverage map to demonstrate this point. All images
were registered to within 10% RMSE. The line plot of the historic images confirms stationarity.

Figure 5. Line plot of historic images demonstrating stationarity.
Sampling Deforestation to Build the Cumulative Deforestation Model
Variance from the pilot sample (100 points) was collected and input to equation 6 to determine total
sample size for the CDM:
0.3126
1
2
1
2

1.96
0.01

0.3126 1.96
0.01

We chose to use an even 2000 samples, as it is conservatively greater than 1877. To support thecollection of data for the CDM, Wildlife Works developed an image classification protocol, and a gridclassification tool, which generates the dot grid overlaid on the historic imagery, and supports the analyst
in performing the deforestation analysis of each of the grid values over time. An excerpt of the image
classification protocol is provided below, and the full document was provided to the Validator.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements________________
Evaluating points
When classifying the points in the grids it is very important to evaluate the area around each point
to get a clear understanding of the land cover features and classification type, not just the area directly
under the point. Points will often land in transition areas so a thorough review must be done to evaluate
the relative proximity to the various land covers. The follow examples examine a range of land covers andfeatures in the images and how to classify them correctly.
Example 1: Forests
A. High density – This point is in the center of a forest. This forest is consistently deep green
and very little to no soil is visible.

B. Low density – This point is on a low density forest where a lot of soil can be seen. The dark
spots in the image are trees and the red area is soil visible between the trees.

C. Low density – This is another example of a low density forest.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

D. Shrub/grass land/naturally low vegetation – This point is on a non-forest area; however
this area has not been deforested. This is a very important distinction to be aware of; eventhough this area may not be forested it should still be classified as forest because the lack offorest was not caused by anthropogenic activities.

Example 2: Anthropogenic deforestation
The key to identifying anthropogenic deforestation versus land that is naturally non-forested or
low density forest is the identification of unnatural patterns in the landscape. These patterns look very
unnatural and include agricultural fields, mosaic deforestation and clear-cut area.
A.

Agricultural fields – This point is in an agricultural field. The distinct lines and structure of the
fields are common landscape characteristics of land that is used for agricultural activities.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

B. Mosaic Deforestation – This point is on a mosaic patch of deforestation. A common
characteristic of mosaic deforestation is random patches of cleared areas that usually start in adense area and become less dense and scattered as it spreads out.

The points in the grid can be classified to the appropriate land
cover type using the Grid Classification tool. For more
information about the Grid Classification tool see: Grid
Classification Tool User Manual.

__________
Excerpt from image classification protocol
The grid data was collected according to the procedure described in the MED and using the Grid
Classification Tool (shown above). The result of this data collection analysis for the Reference Area for alltime periods follows;© Wildlife Works Carbon, LLC 201031

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

1987

1994

1999

2001

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

2003

2004

2005

Classification Legend

Figure 6. Data collected over the historical reference period used to fit the CDM© Wildlife Works Carbon, LLC 201033

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Minimizing Uncertainty in the Cumulative Deforestation Model
To minimize interpretation errors while evaluating forest state in the images used to develop the CDM, animage interpretation protocol was developed and followed by all interpreters. This protocol includes thefollowing information;


Instructions in how to interpret images using a grid of points overlaid on each image.



A description of the set of thematic landcover classes used to interpret the points.

Common (typically encountered) types of land cover patterns and features, and instructions as to
how to recognize thematic classes using context.

How to interpret the forest state of an image, including potential pitfalls to be cognizant of.

After forest state interpretation was completed for all the images within in the historical reference period,
the data was independently checked for inconsistencies and systematic misinterpretation. This was
accomplished by using an algorithm that flagged any points that had an unlikely forest state transition
over the reference period (an example being a transition from non-forest to forest in less than 5 years).
These points were then re-evaluated by examining all images at each point (the temporal span) in orderto accurately identify and rectify any misinterpretations.
A total of 164 points out of 2000 were flagged for inconsistencies. A spreadsheet was used to evaluate
and track the forest state change over the reference period. The images were then re-interpreted for eachpoint and the errors were documented. After the points were reclassified, the check algorithm was run
again to ensure that all flagged forest state transitions had been corrected.
The following documents were made available to the validator:
Image Classification Protocol: Image Evaluation Protocol, 01/12/2011
List of flagged and rectified forest state transition: Grid Data RefArea flaggedPointsv2, 01/12/2011
Fitting the Cumulative Deforestation Model
Observations of forest state from the reference region and applicable covariate data sets were used to fit
the cumulative deforestation model using the free statistical program R. Population census data wereconsidered as covariates to deforestation throughout time, and these data were obtained for two census
districts near the project area – Sagalla and Kasigau – from the Kenya Census for 1989, 1999 and 2009.
A linear interpolation was used to estimate population between 10-year census dates. However, thesecovariates did not inform the model when compared to the model evaluated using only historicalobservations of deforestation. Four models were evaluated using AIC and their linear predictors, and arepresented in the table below.
Model
Forest State = Alpha + Time
Forest State = Alpha + Time + Sagalla
Forest State = Alpha + Time + Kasigau
Forest State = Aplha + Time + (Sagalla + Kasigau)

AIC
4
6
6
12

Table 3. Linear predictors considered and AICs.
The selected linear predictor, per equation 7 is

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

̂

1.0804558

0.0003792

where is the number of days since the project start date. This predictor was selected because it gave
the model with the lowest AIC. A graph of the selected model based on this linear predictor is given
below.

Figure 7. A plot of the selected logistical cumulative deforestation model.
Linear Prediction of Deforestation
A linear rate was selected to predict the cumulative deforestation for project accounting purposes.
According to the notation of equation 7, the selected rate is
0.031649
where is the number of days since the project start date, and y is proportion of area deforested. This
linear rate is conservative because it predicts less baseline deforestation than the cumulative
deforestation model, does not cross the CDM, and is at least 20 years in length. For the end date of thismonitoring period, the projected proportion of cumulative deforestation by the cumulative deforestation
model is 0.404, while the linear model is 0.1898, less than that predicted by the logistical cumulative
deforestation model.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

The following lists the proportion of cumulative deforestation for all monitoring periods to-date based on
this selected linear rate.
Monitoring Period
1Year Ending
2010

Cumulative Deforestation
0.1898

Table 4. List of cumulative deforestation by monitoring period.
A graph of the selected linear rate compared to the cumulative deforestation model from the project startdate to end date is presented below to illustrate that the linear rate is conservative.

Figure 8. A plot of the logistical cumulative deforestation model (a) and the selected linear rate (b).

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Estimating Uncertainty in the Cumulative Deforestation Model
Uncertainty in the cumulative deforestation model was quantified using equation 15 and 17. Equation 17
is calculated as1∈

0.3126732
where 0.1098263 is equal to



iJ

∈

0.1098263 1

0.1098263

wi oi .

Equation 15, the uncertainty in the deforestation model, is then calculated as
1.96
∑∈
√
1.96

0.3126732√8821

0.1098263

where 8821 is the number of state observations made to fit the cumulative deforestation model. The
uncertainty in the deforestation model is.

Section 6.5 Soil Carbon Loss Model
Sampling Soil Carbon Loss
Soil carbon was determined to be an important pool for this project and was measured using purposive
samples of farms in the reference area, most closely correlated to the original dryland forest conditions on
Rukinga Ranch. This was possible because Wildlife Works primary shareholders, and of course all
employees were in the region prior to the Project start date, so we were able to determine which farms
were converted from dryland forest conditions most similarly matching those inside the Project area, as
well as when they were converted.
We selected 25 soil sample locations outside of Rukinga’s boundary in farms(shambas), all at least 10
years since conversion to farm land with conversion as recently as 10 years and as distant as 40 years
ago. We also randomly selected 25 locations inside Rukinga in intact dryland forest.
This following is a table of the shambas that were sampled:
Name

Location

Plot Description

Sample Depth (cm)MzunguSasenyi100

Nemu

MarunguNzangi

Kulikila

Farm cleared 28 yrs ago. Crops grown are
maize and green peas
Farm cleared 10 yrs ago. Crops grown are
maize & green peas
Farm cleared 17 yrs ago. Crops grown are
maize & green peas

© Wildlife Works Carbon, LLC 2010

100
100

37

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Ndaro

Sasenyi

Ngome

SasenyiMazikoSasenyiJira M

SasenyiKazungu

Sasenyi

Kamau

Itinyi

WalterMarungu

KivuvaItinyiMwanjila

ItinyiMarungu
primary
J. Mkala

Marungu

Alima

Marungu

MwikaliLokichigio

Nicholus

Lokichigio

M. Ngele

Itinyi

Kibarangoma

Marungu

F. Kamau

ItinyiMwanyuma

Marungu

Chimanga

Mwagwede

MwaduleMwagwede

Lomitir

Lokichogio

M. Mtima

Marungu

Sasenyi

Farm cleared 35 yrs ago. Crops grown are
maize & green peas
Farm cleared 37 yrs ago. Crops grown are
maize & green peas
Farm cleared 26 yrs ago. Crops grown are
maize & green peas
Farm cleared 40 yrs ago. Crops grown are
maize & green peas
Farm cleared 30 yrs ago. Crops grown are
maize & green peas
Farm cleared 12 yrs ago. Crops grown are
maize & green peas
Farm cleared 10 yrs ago. Crops grown are
maize & green peas
Farm cleared 20 yrs ago. Crops grown are
maize & green peas
Farm cleared 10 yrs ago. Crops grown are
maize & green peas
Farm cleared 40 yrs ago. Crops grown are
maize & green peas
Farm cleared 40 yrs ago. Crops grown are
maize & green peas
Farm cleared 10 yrs ago. Crops grown are
maize & green peas
Farm cleared 20 yrs ago. Crops grown are
maize & green peas
Farm cleared 10 yrs ago. Crops grown are
maize & green peas
Farm cleared 13 yrs ago. Crops grown are
maize & green peas
Farm cleared 13 yrs ago. Crops grown are
maize & green peas
Farm cleared 16 yrs ago. Crops grown are
maize & green peas
Farm cleared 14 yrs ago. Crops grown are
maize & green peas
Farm cleared 17 yrs ago. Crops grown are
maize & green peas
Farm cleared 17 yrs ago. Crops grown are
maize & green peas
Farm cleared 18 yrs ago. Crops grown are
maize & green peas
Farm cleared 17 yrs ago. Crops grown are
maize & green peas

100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100

Table 4. List of soil samples in the reference region.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

The location of all the soil samples taken is shown below in a map of Rukinga Sanctuary and theimmediately surrounding reference area.

Figure 9. Soil samples in Rukinga and shambas in the reference region© Wildlife Works Carbon, LLC 201039

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

For each plot location, soil was sampled to a consistent depth of 1m. We selected this depth due to theresults of a pilot study using a few test pits. Analysis showed that soil carbon loss was still significant
down to 1m. Farmers typically disturb the top 30cms with their ploughs, or with any farming practices theymight use to improve or deteriorate soil condition, but we had surmised that the deep root systems of thedryland forest would lead to high soil carbon at lower depths over time, and we thus chose to sample to a
1m depth.
Each sample was performed in two “lifts”, the first representing the top 30cm (Top Soil), the second from31-100cm (Sub Soil), by digging a 1m square pit and thoroughly mixing the soil removed from the pit ineach “lift” before extracting a sample in a bag for sending of to the independent Soil Laboratory in Nairobi.
Wildlife Works has been using the same soil sampling laboratory - in fact using the same analyst - forseveral years. The laboratory analyst / manager has agreed to speak with the Validator should theyrequire any/all of the following:

calibration recordscertification documentsa description as to how soil carbon is analyzed

All laboratory reports, depicting bulk density and soil carbon, have been provided to the Validator. The
process for soil sampling is illustrated in a soil sampling protocol standard operating procedure , which
serves as a training guide for the field sampling teams, and has also been provided to the Validator.
The following tables list soil data collected inside the project area and in the immediately surroundingreference area:
Reference area samples
SampleFarm

Soil Depth

Comments

CW019SA0290

MzunguTop Soil

CW019SA0291

MzunguSub Soil

CW019SA0292

Nemu

Top Soil

CW019SA0293

Nemu

Sub Soil

CW019SA0294

NzangiTop Soil

CW019SA0295

NzangiSub Soil

CW019SA0296

Ndaro

Top Soil

CW019SA0297

Ndaro

Sub Soil

CW019SA0298

Ngome

Top Soil

CW019SA0299

Ngome

Sub Soil

Sasenyi X0468880,Y9596995
Sasenyi X0468880,Y9596995
MarunguX0468956,Y9598235
MarunguX0468956,Y9598235
KulikilaX0465708,Y9590061
KulikilaX0465708,Y9590061
SasenyiX0469081,Y9595739
SasenyiX0469081,Y9595739
SasenyiX0469081,Y9595739
SasenyiX0469081,Y9595739© Wildlife Works Carbon, LLC 2010

Bulk Density(g/cm3)
1.57

Carbon
(%)
0.64

1.42

0.52

1.43

0.80

1.36

0.55

1.31

1.34

1.29

0.64

1.53

0.51

1.38

0.17

1.57

0.32

1.36

0.27

40

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

CW019SA0300

MazikoTop Soil

CW019SA0301

MazikoSub Soil

CW019SA0302

Jira MTop Soil

CW019SA0303

Jira M

Sub Soil

CW019SA0304

Kazungu

Top Soil

CW019SA0305

Kazungu

Sub Soil

CW019SA0306

Kamau

Top Soil

CW019SA0307

Kamau

Sub Soil

CW019SA0308

WalterTop Soil

CW019SA0309

WalterSub Soil

CW019SA0310

KivuvaTop Soil

CW019SA0311

KivuvaSub Soil

CW019SA0312

Mwanjila

Top Soil

CW019SA0313

Mwanjila

Sub Soil

CW019SA0314

MarunguPrimary

Top Soil

CW019SA0315

MarunguPrimary

Sub Soil

CW019SA0316

J MkalaTop Soil

CW019SA0317

J MkalaSub Soil

CW019SA0318

Alima

Top Soil

CW019SA0319

Alima

Sub Soil

CW019SA0320

Mwikali

Top Soil

CW019SA0321

Mwikali

Sub Soil

CW019SA0322

Nicholus

Top Soil

CW019SA0323

Nicholus

Sub Soil© Wildlife Works Carbon, LLC 2010

SasenyiX0468799,Y9595759
SasenyiX0468799,Y9595759
SasenyiX0468945,Y9595976
SasenyiX0468945,Y9595976
SasenyiX0468945,Y9596807
SasenyiX0468945,Y9596807
ItinyiX0469173,Y9598109
ItinyiX0469173,Y9598109
MarunguX0469162,Y9598058
MarunguX0469162,Y9598058
ItinyiX04770177,Y960141
5
ItinyiX04770177,Y960141
5
ItinyiX0470931,Y9602306
ItinyiX0470931,Y9602306
MarunguX0469404,Y9598889
1
MarunguX0469404,Y9598889
1
SasenyiX0469046,Y9597012
SasenyiX0469046,Y9597012
MarunguX0469173,Y9598113
MarunguX0469173,Y9598113
LokichiqioX0472927,Y9606132
LokichiqioX0472927,Y9606132
LokichiqioX0473454,Y9605990
Lokichiqio-

1.45

0.36

1.41

0.22

1.43

0.62

1.38

0.19

1.43

0.81

1.31

0.62

1.69

0.20

1.52

0.34

1.5

0.41

1.47

0.37

1.51

0.40

1.37

0.25

1.5

0.78

1.43

0.30

1.52

0.26

1.42

0.19

1.58

0.24

1.46

0.35

1.48

0.64

1.42

0.51

1.53

0.69

1.39

0.34

1.56

0.50

1.41

0.38

41

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

X0473454,Y9605990
CW019SA0324

M NgeleTop Soil

CW019SA0325

M NgeleSub Soil

CW019SA0326

Top Soil

CW019SA0328

Kibarangoma
Kibarangoma
F Kamau

CW019SA0329

F Kamau

Sub Soil

CW019SA0330

Top Soil

CW019SA0332

Mwanyuma
Mwanyuma
Chimanga

CW019SA0333

Chimanga

Sub Soil

CW019SA0334

MwaduleTop Soil

CW019SA0335

MwaduleSub Soil

CW019SA0336

Lomitir

Top Soil

CW019SA0337

Lomitir

Sub Soil

CW019SA0338

M Mtima

Top Soil

CW019SA0339

M Mtima

Sub Soil

CW019SA0327

CW019SA0331

Sub Soil
Top Soil

Sub Soil
Top Soil© Wildlife Works Carbon, LLC 2010

ItinyiX0471145,Y9601747
ItinyiX0471145,Y9601747
MarunguX0469975,Y9600174
MarunguX0469975,Y9600174
ItinyiX0470939,Y9603447
ItinyiX0470939,Y9603447
MarunguX0468862,Y9598289
MarunguX0468862,Y9598289
MwaqwedeX0465293,Y9589662
MwaqwedeX0465293,Y9589662
MwaqwedeX0465633,Y9589944
MwaqwedeX0465633,Y9589944
LokichogioX0473367,Y9605797
LokichogioX0473367,Y9605797
MarunguX0469238,Y9598850
MarunguX0469238,Y9598850

1.33

0.47

1.57

0.15

1.57

0.56

1.5

0.28

1.59

0.51

1.5

0.26

1.54

0.42

1.29

0.51

1.56

0.52

1.38

0.55

1.34

0.91

1.28

0.76

1.51

0.44

1.45

0.35

1.55

0.43

1.44

0.35

42

Kasigau Corridor REDD Project Phase I – VCS PD Requirements
Samples inside Rukinga Sanctuary
Sample Number

Farm

Field

Comments

Carbon0‐30cm‐X0477067, Y9578494

Bulk
Density
1.38

CW019SA0239

Rukinga Ranch

Rukinga 75

CW019SA0240

Rukinga Ranch

Rukinga 75

31‐100cm‐ X0477067, Y9578494

1.21

1.70

CW019SA0241

Rukinga Ranch

Rukinga 53

0‐30cm‐ X473061, Y9584563

1.23

1.47

CW019SA0242

Rukinga Ranch

Rukinga 53

31‐100cm‐ X0473061, Y9584563

1.33

0.74

CW019SA0243

Rukinga Ranch

Rukinga 40

0‐30cm‐ X0465557, Y9587046

1.34

1.09

CW019SA0244

Rukinga Ranch

Rukinga 40

31‐100cm‐ Xx0465557, Y9587046

1.13

1.90

CW019SA0245

Rukinga Ranch

Rukinga 45

0‐30cm‐ X0475045, Y9586570

1.22

1.49

CW019SA0246

Rukinga Ranch

Rukinga 45

31‐100cm‐ X0475045, Y9586570

1.35

0.69

CW019SA0247

Rukinga Ranch

Rukinga 54

0‐30cm‐ X0475063, Y9584564

1.3

0.59

CW019SA0248

Rukinga Ranch

Rukinga 54

31‐100cm‐ X0475063, Y9584564

1.33

0.83

CW019SA0249

Rukinga Ranch

Rukinga 81

0‐30cm‐ X0473772, Y9575089

1.39

0.61

CW019SA0250

Rukinga Ranch

Rukinga 81

31‐100cm‐ X0473772, Y9575089

1.38

1.10

CW019SA0251

Rukinga Ranch

Rukinga 63

0‐30cm‐ X0477066, Y9582559

1.39

0.38

CW019SA0252

Rukinga Ranch

Rukinga 63

31‐100cm‐ X0477066, Y9582559

1.25

0.72

CW019SA0253

Rukinga Ranch

Rukinga 71

0‐31cm‐ X0479067, Y9580518

1.2

0.52

CW019SA0254

Rukinga Ranch

Rukinga 71

31‐100cm‐ X0479067, Y9580518

1.36

0.60

CW019SA0255

Rukinga Ranch

Rukinga 19

0‐30cm‐ X0477062, Y9592623

1.38

0.44

CW019SA0256

Rukinga Ranch

Rukinga 19

31‐100cm‐ X0477062, Y9592623

1.4

0.80

CW019SA0257

Rukinga Ranch

Rukinga 3

0‐30cm‐X0475059, Y9599984

1.33

0.40

CW019SA0258

Rukinga Ranch

Rukinga 3

31‐100cm‐ X0475059, Y9599984

1.42

0.85

CW019SA0259

Rukinga Ranch

Rukinga 56

0‐30cm‐ X0479048, Y9584582

1.37

0.65

CW019SA0260

Rukinga Ranch

Rukinga 56

31‐100cm‐ X0479048, Y9584582

1.21

1.28

CW019SA0261

Rukinga Ranch

Rukinga 23

0‐30cm‐ X0471146, Y9590615

1.3

0.65

CW019SA0262

Rukinga Ranch

Rukinga 23

31‐100cm‐ X0471146, Y9590615

1.25

1.05

CW019SA0263

Rukinga Ranch

Rukinga 24

0‐30cm‐ X0472402, Y9590858

1.25

0.69

CW019SA0264

Rukinga Ranch

Rukinga 24

31‐100cm‐ X0472402, Y9590858

1.35

0.98

CW019SA0265

Rukinga Ranch

Rukinga 10

0‐30cm‐ X0475077, Y9596669

1.34

0.52

CW019SA0266

Rukinga Ranch

Rukinga 10

31‐100cm‐ X0475077, Y9596669

1.4

0.72

CW019SA0267

Rukinga Ranch

Rukinga 48

0‐30cm‐ X481050, Y9586554

1.31

0.87

© Wildlife Works Carbon, LLC 2010

0.59

43

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

CW019SA0268

Rukinga Ranch

Rukinga 48

31‐100cm‐ X481050, Y9586554

1.34

0.65

CW019SA0269

Rukinga Ranch

Rukinga 22

0‐30cm‐ X0469113, Y9590709

1.38

1.13

CW019SA0270

Rukinga Ranch

Rukinga 22

31‐100cm‐ X0469113, Y9590709

1.52

0.55

CW019SA0271

Rukinga Ranch

Rukinga 112

0‐30cm‐ X0471958, Y9600245

1.44

0.35

CW019SA0272

Rukinga Ranch

Rukinga 112

31‐100cm‐ X0471958, Y9600245

1.33

0.78

CW019SA0273

Rukinga Ranch

Rukinga 76

0‐30cm‐ X0479067, Y9578494

1.22

0.54

CW019SA0274

Rukinga Ranch

Rukinga 76

31‐100cm‐ X0479067, Y958494

1.26

1.39

CW019SA0275

Rukinga Ranch

Rukinga 35

0‐30cm‐X0477031, Y9588676

1.21

1.19

CW019SA0276

Rukinga Ranch

Rukinga 35

31‐100cm‐ X0477031, Y9588576

1.29

1.12

CW019SA0277

Rukinga Ranch

Rukinga 82

0‐30cm‐ X0475085, y9574499

1.34

0.54

CW019SA0278

Rukinga Ranch

Rukinga 82

31‐100cm‐ X0475085, Y9574499

1.45

0.73

CW019SA0279

Rukinga Ranch

Rukinga 66

0‐30cm‐ X0469494, Y9580862

1.3

0.67

CW019SA0280

Rukinga Ranch

Rukinga 66

31‐100cm‐ X0469494, Y9580862

1.59

0.59

CW019SA0281

Rukinga Ranch

Rukinga 59

0‐30cm‐ X046129, Y9582521

1.5

0.48

CW019SA0282

Rukinga Ranch

Rukinga 59

31‐100cm‐ X0469129, Y9582521

1.36

1.07

CW019SA0283

Rukinga Ranch

Rukinga 90‐30cm‐ X0473253, Y9596819

1.39

0.62

CW019SA0284

Rukinga Ranch

Rukinga 9

31‐100cm‐ X0473253, Y9596819

1.45

0.47

CW019SA0285

Rukinga Ranch

Rukinga 84

0‐30cm‐ X0472093. Y9600367

1.36

0.44

CW019SA0286

Rukinga Ranch

Rukinga 84

31‐100cm‐ X0472093, Y9600367

1.28

0.81

CW019SA0287

Rukinga Ranch

Rukinga 63

0‐30cm‐ X0476903, Y9586364

1.24

0.51

CW019SA0288

Rukinga Ranch

Rukinga 46

31‐100cm‐ X0476903, Y9586364

1.26

0.98

Description of Soil Types
The dominant soil type within the Project Area is Red Laterite typical of this region of Kenya. There aresmall bands of black cotton soil that occur randomly within the project area but account for a tiny - and webelieve insignificant - element from the standpoint of the Project soil carbon pool. There are also areas
within the Project Boundary where Gneiss Islands, or rocky outcrops penetrate the soils to form small
rocky hills. These outcrops also represent a tiny and we believe insignificant portion of the land andtherefore were ignored from the standpoint of the Project soil carbon pool. A soil classification map was
obtained for the whole of Kenya4 from which the soil classification map for the Reference Area, and the
supporting data below, was produced:

4

Sombroek, W.G., Braun, H.M.H. and van der Pouw, B.J.A. (1982). Exploratory Soil Map and Agro-Climatic Zone
Map of Kenya, 1980. Scale: 1:1,000,000. Exploratory Soil Survey Report No. E1. Kenya Soil Survey Ministry ofAgriculture - National Agricultural Laboratories, Nairobi, Kenya.

© Wildlife Works Carbon, LLC 2010

44

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Figure 10. Soil classes in the reference and project areas.

© Wildlife Works Carbon, LLC 201045

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Figure 11. Soil type comparison between Rukinga and the reference area
Minimizing Uncertainty
Wildlife Works has developed a field protocol for sampling soil carbon and that document “Standard
Operating Procedure – Soils” was provided to the Validator.
The same team has been collecting soil samples for over one year in the project area and has collected
well over 100 soil samples during that time. Our VP African Field Operations, Rob Dodson, trained theteams in the proper procedures and conducts periodic audits. Wildlife Works has the utmost confidence inour soil sampling team, and they have produce consistently accurate results. Ultimately, provided© Wildlife Works Carbon, LLC 201046

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

accuracy in field measurements, soil carbon uncertainty lies in the variance between plots and the qualityof the soil laboratory used to determine soil organic carbon levels. Wildlife Works has, and will continue touse, Crop Nutritional Services in Nairobi. "Cropnuts" is run by Jeremy Cordingley, who has extensive
training and experience in soil science and laboratory procedures. Jeremy conducts periodic calibration
exercises with his equipment, and has offered to speak to the Validators should the so desire.
Fitting the Soil Carbon Loss Model
The soil carbon loss model was fit by first estimating the asymptotic proportion of soil carbon loss. Per
equation 12 of the MED, the estimated asymptotic proportion is

ℓ

1

1

#
ℓ

1
ℓ

∈

224.01411.53.

where 224.01 is the estimated mean carbon stock (tonnes CO2e/ha) of shambas in the reference areaand 411.53 is the same for the project area. The default of 20% was selected for the mean rate of soil
carbon loss (based on a conservative value derived from Davidson and Ackerman, 1993). A mean rate of
20% decay is achieved by
0.55 , and the final model isℓ, , ,ℓ
ℓ
,

, ,

1
.

,
1

exp
.

,
exp
.

Predicting Soil Carbon Loss
The final soil model is displayed by equations 11 and 13 below. These equations show that upon
deforestation in the project area, soil carbon gradually decays from the stocks in the deforested areas.
Most soil carbon is lost in the 5 years after deforestation and the proportion of soil carbon lost asymptotes
at 0.456.

Figure 12. Equation 11 (general soil loss form) and Equation 13 (general carbon loss form applied at
Rukinga)© Wildlife Works Carbon, LLC 201047

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Estimating Uncertainty in the Soil Carbon Loss Model
Per equation 19, the total estimated uncertainty in the soil carbon loss model is
1

1.96

∈

1.96

√25

79.48

224.01.
where 79.48 is the estimated standard deviation of soil carbon stocks (tonnes CO2e/ha) from thesampled shambas, 25 is the sample size and 224.01 is the estimated sample mean (tonnes CO2e/ha).

Section 6.6 Baseline Scenario for Selected Carbon Pools
Selecting the Proportion of Below Ground Biomass Removed from Large Trees
The Kasigau Corridor is semi-arid, and due to very low average annual rain fall, the Dryland Forest on
Rukinga Ranch and in the surrounding reference region is characterized by small to medium sized trees,
mostly Acacia ssp and Commiphora ssp. When farmers clear the forest for agriculture, stumps are always
removed if the cleared land is to be used for growing crops such as maize. This is because the land isusually tilled by ox-plough and stumps can present an impediment. Commiphora stumps rot away quite
quickly after the tree has been cut down but the acacia are often too hard to be cut with an axe or panga,so the farmers fell them by making a fire around the base of the tree. This eventually topples the tree andthe fire smolders into the stump and burns it down to below the surface of the soil. Stumps arecorrespondingly not visible from the cleared farm.
Our site management team and the majority shareholder of Rukinga Ranching Company Ltd, Mike
Korchinsky, have been in the area for almost 15 years and have not seen a single stump in a maize farm.
As a result we contend that it is common practice in this region to burn the stumps out, and therefore weselect 100% as the Proportion of below ground biomass removed from large trees.
Selecting the Proportion of Wood Products
There is no harvest of commercial timber from the project area in the Baseline, nor for wood carving,furniture etc. The only potential harvest of wood products under the baseline would be for building
materials for local village huts, e.g. the farmer might cut one or two trees for poles to build his home prior
to slash and burn of the remaining biomass for cropland preparation. There are approximately 200-300
trees per hectare in the dryland forest, and a typical small farm or “shamba” is 5 acres or 2.5 hectares,
representing 500-750 trees, so the one or two poles taken for hut construction per farm represent ademinimus amount of the above ground biomass of less than .5%. As not all farmers use locallyharvested poles for hut construction, and even for those that do, the poles represent a tiny amount ofbiomass as the huts are very small and grass thatched, we feel it is reasonable to ignore thesequestration of carbon in long lived wood products in the baseline scenario, and therefore suggest the
proportion of baseline emissions that are stored in long-lived wood products can be zero.

© Wildlife Works Carbon, LLC 201048

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Figure 13. Local farmers house - Rukinga boundary

Section 6.7 Baseline Reevaluation
This PD was written at the time of initial validation and first monitoring period at the beginning of the
project. This section is not yet applicable. Wildlife Works understands that under certain circumstances in
the future as specified in VCS 2008a there may be reason to perform a Baseline Reevaluation before themandatory time frame of 10 years.

© Wildlife Works Carbon, LLC 201049

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Section 7 Additionality
Within the Project Area, none of the proposed Project activities violate any law.
1. Identification of alternative land use scenarios
a. Continuation of the pre-project land use as private wildlife sanctuary:
Prior to the implementation of the REDD project on Rukinga, the Project proponents had
spent a significant and unsustainable amount of money over the last ten years financing
activities to attempt to protect the forest from destruction. Those activities provided nosignificant sources of income from the land to offset the land protection costs, andtherefore this project would eventually have failed financially if carbon funding were notmade available.
b. Uses a in the ten years prior to Project start date:
Cattle Ranching - When the current majority landowners acquired their interest in
Rukinga Sanctuary in 2000, the previous owners were operating a financiallyunsuccessful cattle ranching operation on the land. The area is too dry with nopermanent water for successful cattle ranching, and there was predation by lions on the
cattle at a rate that lead to the financial failure of the operation, and eventual sale of theland to the majority shareholder of Wildlife Works.
Ecotourism - The prior owners also had an ecotourism facility on the Project area, but asevidence that these activities were not financially viable on the land, the slash and burnclearing had reached within 200 meters of the ecotourism facility, causing it to fail andmove away.
c.

Slash and Burn Agriculture by subsistence farmers:
Prior to the Project Proponent taking over management of the land in 2005, local people
had begun to clear part of the Project area, and have systematically cleared the dryland
forest from a majority of the Reference area in order to provide land for annual crops.
This is evidently the most likely Baseline scenario, as it had been carried out routinely
throughout the Reference region, in clear violation of land laws.

2. Consistency of credible land uses with enforced mandatory laws and regulations:
All of the alternative land use scenarios above represent legal land uses, with the exception ofslash and burn agriculture, which essentially consists of squatting on privately owned land; illegal
under Kenyan law. However, there is overwhelming evidence that this law had beensystematically unenforced, as greater than 30% of the area of the administrative unit thatencompasses the project area had been deforested in the ten years prior to the Project start date.
Thus, all the land uses above are credible.
3. Investment Analysis – Simple Cost Analysis:
Physical protection of the Project area, and provision of deforestation mitigation activities, such as
school building, scholarships, ranger patrols, reforestation of deforested indigenous forests etc.
for the community cost the Project Proponent approximately $300-400,000 per year in the years© Wildlife Works Carbon, LLC 2010

50

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

prior to implementation of the VCS AFOLU project. There exists no significant income to offset
these costs. In the absence of active protection, both physical and that created by partnering withthe communities to create economic alternatives, it is clear the land in the Project area would becleared aggressively for subsistence agriculture, as that was in fact what was already happening
prior to our arrival. Slash and burn agriculture faces no economic barriers, and is therefore once
again the most likely Baseline scenario.
4. Common Practice Analysis
It is common practice to protect wilderness in Africa, and to provide sustainable development
support for rural African communities, but that common practice is typically funded bygovernments or donor agencies, and not by financial return from the project activities. It is NOT
common practice for private companies that are not donor funded, such as the Project proponent
to protect forested wilderness in Africa for financial return, in the absence of AFOLU revenues.
The Project proponent’s Rukinga Sanctuary project is the first AFOLU Project Activity of its typein Kenya, and one of the very first in Africa.
Summary of Additionality Test
In summary;

the Kasigau Corridor REDD project is not the only credible alternative land use consistent with
enforced mandatory applicable laws,

one of those alternative land uses, that of Slash and Burn Agriculture is by far the most likely
baseline land use,

the Kasigau Corridor project passes the Investment Analysis Test as it is not a financially viable
land use without the AFOLU VCS project revenuesand the project activities are NOT common practice.

therefore it is additional under the rules of VT0001 Tool for the Demonstration of Additionality in VCS
AFOLU Project Activities.

© Wildlife Works Carbon, LLC 2010

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Section 8 Baseline Emissions
Baseline emissions are calculated as the carbon pools measured in the project area, which are applied tothe cumulative deforestation model (determined by sampling historical imagery). The estimated emissions(tonnes CO2e) for each selected carbon pool in the project area for each year since the project start dateare shown in the following table. The total estimated baseline emissions for the first monitoring period are
1,450,329 tonnes CO2e. These emissions are based on the selected linear predictor of cumulative
deforestation. It should be noted that it is not mandatory to measure ex-ante carbon stocks in the project
area according to VCS standards. However, Wildlife Works chose to verify the project at the same time as
project validation, and therefore performed a full ex-ante carbon inventory. The spreadsheet 'NER
Analysis v4, 01/25/2011' provides complete GHG emission analysis for the entire project crediting period,
and was provided to the Validator.

Linear Model (%)
AGLT
BGLT
AGST
BGST
AGNT
BGNT
SDW
LDW
WP
SOIL
Total Emissions

2005
3.16%
50,776
20,310
0
0
8,556
3,422
0
0
0
119,709
202,774

2006
6.33%
50,776
20,310
0
0
8,556
3,422
0
0
0
155,515
238,580

2007
9.49%
50,776
20,310
0
0
8,556
3,422
0
0
0
166,225
249,2902008
12.66%
50,776
20,310
0
0
8,556
3,422
0
0
0
169,429
252,4942009
15.82%
50,776
20,310
0
0
8,556
3,422
0
0
0
170,387
253,452

2010
18.99%
50,776
20,310
0
0
8,556
3,422
0
0
0
170,674
253,739

Table 5. Baseline emissions by carbon pool and year.

8.1 Estimating Emissions from Above Ground Large Tree Biomass
See above summary table.

8.2 Estimating Emissions from Above Ground Small Tree Biomass
See above summary table – no distinction is made in this project between large and small trees; small
tree biomass is therefore included in the large tree pool.

8.3 Estimating Emissions from Above Ground Non-Tree Biomass
See above summary table – non-tree includes shrubs and grasses.

8.4 Estimating Emissions from Below Ground Large Tree Biomass
See above summary table.

© Wildlife Works Carbon, LLC 2010

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

8.5 Estimating Emissions from Below Ground Small Tree Biomass
See above summary table – no distinction is made in this project between large and small trees; small
tree biomass is therefore included in the large tree pool.

8.6 Estimating Emissions from Below Ground Non-Tree Biomass
See above summary table – non-tree includes shrubs and grasses.

8.7 Estimating Emissions from Standing Dead WoodSee above summary table – standing dead wood was included in the large tree numbers. Lying deadwood was conservatively ignored (see below)

8.8 Estimating Emissions from Lying Dead Wood
While there are many lying dead trees in the ecosystem, termites are very active in this ecosystem. To
provide a conservative estimate of total aboveground biomass from trees, we have excluded this pool,
although in some plots the weight of lying dead wood is significant as a result of elephant damage.

8.9 Estimating Emissions from Soil
See above summary table

8.10 Estimating Emissions from Wood Products
The proportion of long lived wood products defined in section 6.6.10 was zero. Therefore, there are nomeasured negative emissions (sequestration) from this pool.

© Wildlife Works Carbon, LLC 2010

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Section 9 Project Emissions
9.0 Forest Fires
There have been no significant forest fires in the Project area during the first monitoring period. The
Project proponent understands that should significant forest fires occur in the future during the Projectcrediting period, that we would be required to produce a map of the boundaries of the fire prior to thesubsequent monitoring period.

9.1 Emissions from Burning
There have been no events of woody biomass burning within the Project area. Wildlife Works' sustainablecharcoal project activity uses fingerling wood, sustainably harvested from indigenous trees outside theProject Area.

© Wildlife Works Carbon, LLC 2010

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Section 10 Leakage
Section 10.1 Leakage Mitigation Strategies
Providing economic alternatives to the slash and burn agricultural practices that have devastatedso much of sub-saharan Africa:a) we built a factory on the edge of our project area where we train the local women how to
sew. We have employed many local people over the years, producing organic cotton
fashion which we sell locally and internationally. A pact with the community exists: if theyvalue the jobs, they agree to stop clearing the forest and damaging biodiversity, or wewill not be able to sell products, and they will lose their jobs. Our factory uses a small
amount of electricity generated from the National Grid, which in Kenya is 40%hydroelectric. We believe the emissions created by this power use are more than offset
by the reduction in emissions gained from our greenhouse and tree nurseries andreplanting schemes discussed below.
b) we established an organic greenhouse and nursery program to grow a variety of trees,
providing fuelwood, cash crops and medicinal/agroforestry species to the community.
Increasing agricultural productivity on existing farmland is viewed as the best way to stop
additional conversion. We plan to expand this activity to sponsor nurseries in each of themain villages surrounding our project upon receipt of carbon revenue from this project.
We havfe already initiated a reforestation activity with native hardwoods grown in our
nursery, and outplanted into previously deforested areas on community lands. We areclaiming no additional carbon emissions credits for this activity; it is simply an element of
our leakage mitigation strategy.
c) we have been working with the Kenyan Agricultural Research Institute (KARI) to explore
the potential of growing jojoba as a dryland cash crop that can withstand drought andpoor agricultural practices and still generate a cash crop on a high value per hectare
basis, again to improve food security by increasing agricultural productivity on existing ag
lands to reduce conversion pressure. We have completed a two year study and are readyto roll out a farmer outreach model.

Providing planned Farm land
The local population’s need for additional farm land was addressed by the establishment of a landcooperative on 5000 acres of what was still at that time Rukinga Ranch. This Sasenyi Valley landcooperative on land that had been cleared of forest prior to our arrival gave the community area
to expand into without needing to clear more forest. They were able to receive legal title for theirfarms, a first in this area of Kenya. This program has been fully implemented.

Expansion of our ranger patrols and implementation of community ranger groups to patrol the
leakage area
Unlike most REDD projects, Wildlife Works directly employs its own rangers to protect the forestfrom illegal incursion, deforestation and even damage to biodiversity. We have a 10 year trackrecord of physically protecting the land from all potential deforestation agents. Our success,where many other projects have failed in this regard, is due to our providing economic© Wildlife Works Carbon, LLC 2010

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

alternatives to the community, preventing the requirement to clear more forest for agriculture.
This has created a partnering relationship with the community, and increased the effectiveness of
our rangers, even though they are not armed. They can draw heavily on support from theinfluential members of the local community. We believe that our presence in daily protection of
the forest has significantly reduced, if not completely eliminated, the threat of immigrant
populations from non forested areas of the Coast province in Kenya coming to the area in searchof unprotected land for slash and burn agriculture. Therefore, in addition to stopping the specificdeforestation of the project area, the project activities have reduced the population pressure thatwould have been seen under the baseline / without project scenario. We have more than doubled
our ranger force since the beginning of the REDD project.
Phase II:
We plan to implement a second phase of the Kasigau Corridor project, in which we will extend
our monitoring and protection to ALL of the remaining dryland forest in this region of Kenya,nearly 500,000 acres, to prevent slash and burn agriculture from moving into any of the adjacentforested lands privately owned by members of the community. We have entered into Carbon
Rights Agreements / Easements with the neighboring community land owners to execute thiscomponent of the strategy, and have already begun protection of their forests with additional
rangers and ranger posts. This program has been fully implemented.



Fuelwood and sustainable charcoal:a) We are establishing 5 organic greenhouse extensions within the Project area to produce
fuelwood and other agriforestry species for the local community. We aim to assist them inbecoming self-sufficient in fuelwood, without having to extract from any of the Project
area or other private dryland forest in the region. This activity is currently being
established.
b) A study carried out by Matthew Owen of the University of North Carolina, “Adaptation to
Rural Domestic Fuelwood Scarcity in Embu District, Kenya" showed that when fuelwood
is an abundant and free resource, it is used at a level far above necessity, and that when
it becomes a constrained resource, consumption can drop by as much as 50% without
loss of function to the community. This indicates that the amount of wood being
harvested for fuelwood from Rukinga can probably be replaced with far less fuelwood
grown in woodlots and community farms.
c) We have been developing a sustainable charcoal alternative to destructive bushcharcoal. We currently employ 12 people in the production of charcoal briquettes fromfingerling charcoal harvested from indigenous trees and shrubs, and using a cassava
flour binder. We believe we can substitute this carbon neutral charcoal into the local
economy with minimal subsidy to provide for the community’s fuel needs, with zeroleakage. Production testing has been completed for this activity. Sales tests are ongoing.
d) Our baseline analysis shows that the without project scenario would have seen the
Project area eventually cleared completely for farm land. As such, wood resources thecommunity may have extracted from the Project area would have been transient at best.

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Section 10.2 Delineation of the Leakage Area
The leakage area, depicted in the map in section 10.3.2 below, was selected from forested areas as close
as possible to the Project area which are subject to the same agents and drivers of deforestation as the
project area, and that exhibit similar geographic characteristics (such as elevation, proximity to villages or
towns, forest type etc.) The MED requires that the leakage area be forested at the project start date.
Tsavo National Parks were excluded, as they fall under a different legal protection status. The mostobvious area with a high potential for leakage are the group-owned ranches with identical land ownershipsystem to the Project area. They are immediately adjacent to the project area, but were not selected forinclusion in the leakage area, as they are now being protected by Wildlife Works under Phase II of the
Kasigau Corridor REDD. The second criteria was accessibility by the agents of deforestation, as some of
the remaining forested land in the reference region is very remote and unlikely to suffer leakage. Soil
fertility or rainfall were not considered, as they are fairly constant across the Reference area.

Section 10.3 The Leakage Model
Sampling Deforestation and Degradation to Build the Leakage Model
Per the requirements of the MED, the leakage area was sampled prior to the first monitoring period, toestimate the lag period for the leakage model.
Equation [10], dependent on the standard deviation of the forested state observations, was used to
calculate the number of sample point locations required, and yielded a result of 38 locations within theleakage area
1.96
0.1
. 3126 1.96
0.1

38 equal sized 2 hectare square plots were then randomly located within the Leakage area, andcoordinates of the NE corner of each Leakage plot was given to the leakage plot sampling team. A
number of extra plots were generated to allow for inaccessibility in the field of certain plots. The rationale
behind the extra plots is that in this ecosystem, inaccessibility is limited to thick bush, where vehicles
cannot approach to a safe distance for the sampling teams to reach the location on foot to perform thesampling exercise. At Wildlife Works, safety for our employees is of primary concern, and if samplingteams walk too far in thick bush, they run the risk of encountering elephant or buffalo. As inaccessibility
always corresponds with thick primary vegetation, it can be assumed that the exclusion of theinaccessible points is a conservative measure of leakage, as they would undoubtedly have a factor of 0.
Note that the field leakage sampling done by Wildlife Works personnel was done prior to the MED being
finally validated, and at the time the Leakage Plot samples were taken, a 0% leakage factor was notencountered; the lowest factor was 0-20%. Again, we believe this leads to a conservative measure of
average leakage factor, and a conservative leakage lag period. Maps of the leakage area, showing thepermanent Leakage plots are shown below.

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Figure 14. Leakage plots overlaid on a forest/non-forest map© Wildlife Works Carbon, LLC 2010

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Figure 15. Leakage plots and corresponding coordinates© Wildlife Works Carbon, LLC 2010

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The Leakage Sampling team performed estimates of deforestation and degradation according to'Standard Operating Procedure Leakage', a copy of which was provided for the Validator. They made no
permanent marking of plots, and will simply return to the same NE corner coordinate each monitoring
period, and repeat the procedure in each subsequent period. Sampling results are summarized in thetable below. Leakage sampling was lead by Operations Manager Jamie Hendriksen, and supervised by
Rob Dodson, VP African Field Operations, our two most experienced staff members, as this was our firstever leakage area plot sampling effort. They will now be responsible for training other members of ourfield plot sampling team to perform this activity each required monitoring period, and for performing QA on
a selected sample of the Leakage Plots each monitoring period to ensure consistency in their evaluationof degradation for this first monitoring period.
Leakage Plot Coordinates
Plots Point X
Point Y
L01
462290.6 9566407.4
L02459730.6 9572807.4
L03458450.6 9583047.4
L04464850.6 9584327.4
L05405970.6 9589447.4
L06
407250.6 9589447.4
L07404690.6 9590727.4
L08405970.6 9592007.4
L09
404690.6 9594567.4
L10472530.6 9602247.4
L11457170.6 9603527.4
L12472530.6 9603527.4
L13486859.7 9593065.8
L14482453.6 9592769.8
L15485034.1 9591786.5
L16461010.6 9611207.4
L17448210.6 9612487.4
L18479344.8 9595139.2
L19457170.6 9618887.4
L20443090.6 9640647.4
L21441810.6 9648327.4
L22481307.2 9593867.3
L23481598.5 9594812.8
L24484344.9 9592969.2
L25484231.9 9593633.8
L26487579.8 9593521.9
L27486771.0 9594064.8
L28448401.6 9613947.0
L29446810.4 9609307.4
L30446012.1 9612379.7
L31449457.4 9613888.1
L32470359.3 9573125.2
L33471130.8 9572882.0
L34465491.1 9583961.7
L35465407.2 9584618.5
L36464821.6 9585006.8
L37470989.6 9573694.4
L38470380.8 9573910.4

Degradation % Dec, 2010
0‐20
41‐60
21‐40
0
0‐20
0‐20
0‐20
21‐40
21‐40
0‐20
61‐80
0‐20
0‐20
21‐40
41‐60
41‐60
21‐40
20‐40
0‐20
0
0
0‐20
21‐40
0‐20
21‐40
0‐20
0‐20
61‐80
61‐80
41‐60
21‐40
61‐80
41‐60
21‐40
21‐40
0‐20
41‐60
21‐40

value
1
3
2
0
1
1
1
2
2
1
4
1
1
2
3
3
2
2
1
0
0
1
2
1
2
1
1
4
4
3
2
4
3
2
2
1
3
2

Leakage Area Polygons
perimeter (m)10394.51227
2046.461795
4547.836483
7837.745452
17675.17905
8279.920827
10307.97126
9726.982167
18021.21369
7857.944337
8822.613995
68042.63615
32587.05298
38283.42394
8619.378165
9174.509654
Total Leakage area (ha)Rukinga forested area (ha)area (m2)2622654.416
221164.6479
522040.3724
2487550.251
3713644.272
1626170.571
3942253.055
2795240.98
13467407.77
2567239.308
4934017.422
255753249.1
29928113.67
56169524.54
3420972.65
4721327.05
38,88927,844

deg

value
0
0‐20
21‐40
41‐60
61‐80
81‐100

0
1
2
3
4
5

Table 6. Leakage plot evaluation results© Wildlife Works Carbon, LLC 2010

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Fitting the Leakage Model
The leakage model was fit by first computing the proportion of cumulative deforestation and degradationin the leakage area as the average of observed factors. This proportion
is 0.3789, applied to equation
9 to compute the lag period aslog
log 0.3737

log 1
log 1

0.37371.08804558.
And the final leakage model per equation 8 is then
1

, ̂,

1

exp

1.08804558

0.0003792

0.4498The following is a plot of the leakage model for the leakage area compared to the cumulative
deforestation model.
1.2

1

0.8

0.6

cdm model
leakage model

0.4

0.2

0
0

5

10

15

20

25

30

Figure 16. Plot of the leakage model compared to the cumulative deforestation model over time (years).

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Section 10.4 Estimating the Leakage Factor and Emissions from Leakage
The estimated cumulative degradation and deforestation predicted by the leakage model is 0.343 which
necessarily matches that observed in the leakage area for the first monitoring period. Since this is thefirst monitoring period and the leakage model was parameterized after the project start date, the leakage
factor is zero. Likewise, for this monitoring period, the estimated emissions from leakage are zero.
During subsequent monitoring periods, the Leakage Plot Sampling teams will revisit the 38 two-squarehectare plots and perform the same SOP to determine the Leakage Factor evident at that time, and thatwill be used to determine whether or not Leakage has occurred during that monitoring period, per therequirements of the MED.Leakage measured for each monitoring period will be applied to net emission
reduction figures for that same period (i.e. adjustment for leakage is applied at the point of eachverification event following the first, which is used to only determine the leakage lag factor).

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Section 11 Quantification of NERs
Net Emissions Reductions (NERs) to date are quantified from the following components (tonnes CO2e)
with 290,066 and 1,160,263 tonnes CO2e to buffer pool and issuance, respectively.
Component
Estimated Baseline Emissions
Uncertainty Deduction
Project Emissions
Emissions from Leakage
Gross Total NERs
NERs to Buffer Pool (20%)
Net Total NERs

Value
1,450,3290001,450,329
290,066
1,160,263

Table 7. Components of NER calculations, allocation to buffer pool and total NERs to date.

Section 11.1 Determining Deductions for Uncertainty
Given the calculated, weighted quadratic average using equation 36, no confidence deduction is applied,
as total uncertainty falls below 0.15. The weighted quadratic average of quantified uncertainty, perequation 36, is22624568.9 ∙ 0.05941298
2624568.9 ∙ 0.0851
11842347.78 ∙ 0.1391
2 ∙ 2624568.9
11842347.78.
where the inputs are presented below.
Variable

Description
Total forest carbon stock at monitoring period [1]Value
2,624,568.9

Soil carbon stock within the project area at monitoring period [1]1,1842,347.8

Estimated uncertainty in the CDM at monitoring period [1]0.05941298

Estimated uncertainty of total carbon stocks at monitoring period [1]0.0851

Estimated uncertainty in the soil carbon model at monitoring period [1]0.1391

Table 8. Variables and values used to calculate the weighted quadratic average of uncertainty.

Section 11.3 Ex-Ante Estimation of NERs
Baseline emissions were projected over the life of the project to estimate net carbon benefit. An ex-anteestimate of the total gross NERs generated by the project is 7,542,945 tonnes CO2e.
The project activities described in detail in Section 10 Leakage and Section 6.1 Baseline Scenario
Overview, were specifically designed to mitigate deforestation and human-wildlife conflict, and therefore© Wildlife Works Carbon, LLC 2010

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

by default serve to mitigate leakage and uphold project permanence. Wildlife Works is of the opinion thatthe project will suffer little to no leakage, due to our exceptional attention to leakage mitigation. However,in the absence of precedent for estimating ex-ante leakage emissions, Wildlife Works chose to use aconservative value of 20%. Applying this factor to gross NERs yields an estimate of total net NERs overthe project lifetime of:Ex

Ante NERs
Ex

7,542,945

Ante NERs

7,542,945 ∗ 0.20,,

This analysis is available as a spreadsheet and accounts for an estimate of 20% leakage from 2011
onwards, according to the MED. It includes project emissions and a total confidence deduction. A chartof the projected NERs over the life of the project is presented below. Actual leakage values will bemeasured empirically at each monitoring period, and will vary from these conservative ex-ante estimates.

Ex‐Ante NERs
300,000

250,000

200,000

150,000

100,000

50,0000

Figure 17. Ex-Ante Calculation of NERs for the Project lifetime.

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Section 13 Monitoring
Please also refer to the document entitled 'Section 13 Monitoring' (01/14/2011)

Section 13.14 Monitoring of Carbon Stocks in the Project Area
Summary of sampling procedures(See Standard Operating Procedure Biomass, 1/10/2011 and Standard Operating Procedure Soils,
1/1/2011 provided to the Validator for detailed procedures)
Rukinga Sanctuary is 30,169 hectares of varying density Acacia-Commiphora woodland/forest located in
the SE of Kenya. Altitudes on the sanctuary range from approximately 450m to 1,000m and theecosystem encompasses montane forest on the slopes of the higher elevations, through AcaciaCommiphora dryland forest at mid elevations and down to grassland dominated savannah at the lowest
elevations. In order to most accurately estimate the biomass of the sanctuary, with reasonable time andexpense, we divided the sanctuary into three major strata based on ecosystem type, as there is a highperceived variation in average biomass across the three strata pools, with larger trees in high density inthe montane forest strata, medium to large trees and lots of shrubs in the dryland forest strata andscattered trees, very few shrubs and heavy grass cover in the savannah grassland strata. Overall, we
used 9 strata, summing to the total land area, to depict landcover in Rukinga.
In order to most accurately estimate biomass in the sanctuary, with reasonable time and expense, we
divided the sanctuary into three major ecosystem types, as there is a high perceived variation in averagebiomass across these pools, with larger trees in high density in the montane forest strata, medium tolarge trees and lots of shrubs in the dryland forest strata and scattered trees, very few shrubs and heavy
grass cover in the savannah grassland areas. We ultimately used 9 strata, summing to the total landarea, to depict homogeneous patches of landcover in Rukinga.
It should be noted that our ex-ante monitoring was conducted in February and March 2009, the dry
season in this area. We believe this will yield an extremely conservative biomass estimates, as thedominant tree species enter into estervation to preserve moisture. During this season, the trees lose allleaf mass, and the perennial grasses senesce. Wildlife Works executive management supervised thedata collection teams at the initial plots, to ensure proper adherence to procedure.
It was determined that a systematic random plot sampling technique would best capture variability inlandcover, due to the high degree of perceived variation of type and density of trees and shrubs. A
systematic sampling method was used to overlay a 2km x 2km grid over the sanctuary and select sample
plot centers at the center point of each square (see figure 18 below). The upper left corner of the grid was
randomly positioned within its UTM 1km x 1km grid.
To sample soil, coordinates were provided to the soil plot sampling teams by our GIS team, at random
forest plot locations, and they sampled using the method illustrated in the 'Standard Operating Procedure
Soils' document provided to the validator. The following is an excerpt from the soil sampling procedure:
Step1 For a plot inside Rukinga, coordinates are provided to the soil plot sampling teams by our GIS
team, at random plot locations. The plot teams use their GPS to find the plot center.
Step 2. A one meter square is marked out on the ground, and digging commences.

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Step 3. The soil from the top 30cms is piled together and the larger lumps are smashed with the back of ahoe.
Step 4. Whilst the soil is being dug from the sample pit, the tailings are thoroughly mixed so that thevarious layers are interspersed.
Step 5. The lower layer taken from 31cm-100cm is then piled on the other side of the pit and it too ismixed thoroughly.
Step 6. A sample is then taken from each of the mixed piles, bagged, and sent to the independent testinglab – CROP NUTRITION SERVICES, Nairobi Kenya.
If outside Rukinga, the location and name of the farm and any comments are recorded on the bag and in
the sampling notes, and Top Soil(0-30) and Sub Soil(31-100) are recorded for the respective samples.
Care should be taken not to include any large rocks or roots or other obvious organic matter in thesamples; mineral soil only.
Crop Nutrition Services performs standard bulk density and organic matter analysis of the soil samples
and returns the results in excel spreadsheets. The Bulk Density method used by the outside laboratory(Crop Nutrition Services) that performed the soil testing for the PD is an official FAO methodology formeasuring Bulk Density of disturbed soil samples. A copy of the FAO approved protocol was provided to
the Validators.
Field training
Field training was conducted in February, 2009 for the first tree plot sampling team. This team consistedof;

a local tree expert who was able to identify all the different acacia and commiphora species
encountered in the sampling - Joel MwandigaMike Korchinsky – CEO Wildlife Works

Rob Dodson – VP African Field Operations



Mwololo Muasa a Wildlife Works employee who would be the permanent team lead and data
recorder

Three casuals to assist with carrying equipment into the field and marking the plots

A driver

A ranger for security

The Standard Operating Procedures for Biomass and Soils were produced following refinement of the
field techniques by this initial team and two other teams have been trained using the procedure and byaccompanying our permanent team on their work, to ensure consistency in method.

Documentation of data quality assessment such as the results from a check cruise
Quality Control (QC) for Biomass plots was conducting using the following protocol;© Wildlife Works Carbon, LLC 2010

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1. An independent QC team not involved in the original plot sampling of each plot is given
coordinates for the plot centers for 5% of the original plots. The Independent QC team is also
given blank plot data recording sheets, plot radius for each carbon pool, a copy of the plot
sampling “Standard Operating Procedure – Biomass”, dbh tape, compass and long tape, andsent out to measure the plots as though they were doing it for the first time.
2. The QC team returns to headquarters with data sheets which are given to a third party
analyst, who are neither on the original nor the QC plot team, for comparison against theoriginal plot data sheets.
3. Any discrepancies are noted, and when all sheets have been compared, the two plot teams
are brought together with the VP African Field Operations or his deputy the Operations
Manager to discuss and explain any significant variances (±15%)4. The monitoring team lead is informed if more than 1 QC plot contains significant
discrepancies from the original data sheets, and further QC plots may be required toestablish the extent of the quality errors.
5. The Monitoring Team Lead and/or senior carbon staff makes a determination as to whether aplot needs to be revisited:
For a given plot, the number of trees that fall outside the ±15% threshold for change sinceoriginal measurement is counted. If greater than 10% of trees in that plot fall outside thethreshold, and QC has been performed on the plot within 1 year from original measurement,
the plot must be re-measured. If QC has been performed on a plot greater than 1 year after
original measurement, the threshold described above shall be relaxed to 15%.

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Map Showing Strata Boundaries and Plot Locations

Figure 18. Stratification of the project area and carbon inventory plots© Wildlife Works Carbon, LLC 2010

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Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Figure 18. Stratification of the project area and carbon inventory plots© Wildlife Works Carbon, LLC 201069

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

List of Plot Coordinates
A list of plots and corresponding coordinates was provided to Validator, as it was determined to beinappropriately large for this document.
Description of Plot size
The following describe the biomass plots on Rukinga; 25m radius circle for large and small trees in Dryland Forest 8m radius circle for large and small trees in Montane Forest 15m radius circle for shrubs in Dryland forest 4m radius circle for shrubs in Montane Forest 1m x 1m x 4 square plots at each tree plot location for grasses
Documentation of Allometry
Living TreesAs this is the first project we have encountered calculating aboveground biomass for the species of tree
found in Acacia-Commiphora woodland, there exist no allometric equations available for calculating ABB
from DBH. As a result we were forced to develop our own method to determine appropriate allometry.
Select trees from dominant species found in repeated plots were harvested from test areas outside of the
Project area, and cut into pieces and weighed, for a range of dbh equating to the dominant ranges of dbh
found within the project area. This provided a wet weight total aboveground biomass for a range of tree
sizes from 10cm to 50cm dbh. A green to dry weight ratio was used to convert to dry weights.
A graph of dbh vs. wet weight was then plotted, as described in the spreadsheet'AllometricFormulasPower, 01/14/2011 ' provided to the validator.
ShrubsFor dominant shrub species a test plot was created from which two separate methods were produced;
For shrubs/small trees that can become very large, e.g. Cordia, Acacia ruficiens where the shrub is multistemmed from the ground, with between 2 and 15 stems, average stem diameter was calculated for arange of shrub sizes, by measuring all the stem diameters on the shrub and dividing by number of stems,
and then harvesting, bundling and weighing one representative stem of the average diameter from eachsize class. These classes are small, medium, and large, providing a standard stem weight by shrub sizeclass. The number of stems and size class for each shrub in the sample plot were then recorded, and a
shrub total aboveground biomass determined from multiplying the number of stems by the stem weight
for that class.
For Grewia, and others where the shrub has many stems, and is non-uniform in distribution of biomass
per stem, conservative weight averages were obtained for each size class through destructive harvesting,which was then applied to live sample plots without destructive harvesting requirements. A green to dry
weight ratio was then used to convert to dry weights.

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Shrub Species

Size
Class
(S/M/L)

Cordia sinensis
Cordia sinensis
Cordia sinensis
Grewia sp.
Grewia sp.
Grewia sp.
Acacia ruficiens
Acacia ruficiens
Acacia ruficiens

S
M
L
S
M
L
S
M
L

Crown
DiameterRange<1m>1m <2m>2m

Crown
HeightRange

Average
Stem
Diameter(cm)<1m>1m <2m>2m
5
9
12Standard
Weight/Weight/stem
(kg)
3
15
33
1.5
4.3
9
23
43
131

Table 9. List of dominant shrub species and standard weights
Development of Allometry
The allometric equations for the project area, based on the aforementioned, field-collected destructive
harvest data, were produced for Wildlife Works by Ryan Anderson of EcoPartners. These equations
predict green weight(kg) as a function of DBH(cm), based on the data provided by Wildlife Works in the“AllometricFormulasEXP” spreadsheet. All equations have the form :The evaluation of goodness of fit is based on a cross-validation statistic, not R2. We reporting R2 as wellbecause people are used to seeing it, but we believe the cross validation statistic is a better indicator offit.
Destructive harvest in a wildlife conservation area is philosophically problematic, especially for trees oflarge diameter which are many decades if not hundreds of years old. As a result we harvested only a few
trees at large diameter. A consequence of this sample size is a tendency for the few large trees wesampled to have an overly large influence on the shape of the regression curve. When only one or twolarge trees are sampled, and they exhibit biomass much larger than the smaller trees, regression fit byleast squares tends to be highly influenced by those trees. This tends to lead to over estimation of
biomass for the smaller trees. For model fitting reasons, it is additionally problematic because (a) the
uncertainty in measuring the mass of a large tree is larger than a small, easily weighed tree, and (b) the
diameter-biomass relationship for large trees is inherently more variable than it is for small trees. The
consequence is that the model is heavily influenced by a few points whose response variable values are
known with little certainty.
To deal with the highly influential large points that have large variance, we used a weighted regression. A
discussion of this technique should be in any regression text, but we used “Applied Regression Including
Computing and Graphics” (Cook and Weisber g 1999, Wiley and Sons). The idea is that higher weight infitting the model should be given to those points that are known with greater certainty. We evaluatedweights individually for each model, and only used them in cases where the model residuals
demonstrated strong trends in variance. Weights were assumed to be proportional to either 1/BA or
1/BA2, where BA is basal area. In one unusual case (Lannea alata), the variance appeared higher for© Wildlife Works Carbon, LLC 201071

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

small trees than large trees, so we weighted this regression with weights proportional to DBH. We notethat the weighting considerably reduced the cross-validated estimate of bias ( ).
Coefficients for each equation are below:
Species
Acacia bussei
Acacia hockii
Acacia nilotica
Acacia tortilis
Boscia coriacea
Boswellia neglecta
Commiphora africana
Commiphora campestris
Commiphora confusa
Lannea alata
Lannea rivae
Acacia sp.
Boscia sp.
Boswellia sp.
Commiphora sp.
Lannea sp.
All species (<35 cm DBH)

Weight
Type
None
None
None
None
1/BA
1/BA2
1/BA2
1/BA2
None
DBH
None
None
1/BA
1/BA2
1/BA
None
None

a

b

N

Max DBH

R2

3.37960.68501.36152.60600.2033
1.30250.62930.06774
0.11470.56030.14881.14210.2033
1.30250.10527
0.32880.34111.6416
2.18201.9513
1.61752.3647
1.8332
1.94562.81562.66342.10272.64211.99542.3647
1.83322.665442.32332.3016

8
17
10
9
15
18
17
17
18
17
22
44
15
18
52
39
166

18
23
23
20
34.2
37
24
40
23
17
16
23
34.2
37
40
17
34.2

.80
.93
.86
.85
.77
.40
.75
.83
.77
.85
.54
.85
.77
.40
.87
.62
.74

7.82
-2.46
14.83
0.13
7.30
13.87
13.17
13.0722.912
13.216
11.7
1.99
7.30
13.87
11.26
11.18
9.50

Table 10. Accuracy allometry coefficients for dominant species in Rukinga.
A summary of the cross validation statistics for species appears below. The black diamond is the meancross validated residual, expressed as a percent. The boxplots show the quartiles (.25, median, .75), andmaximum of the cross-validated residuals. The dashed lines indicate +/- 15%, the bias threshold allowedby the MED.

© Wildlife Works Carbon, LLC 201072

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Species level:

Genus Level:© Wildlife Works Carbon, LLC 201073

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

All species combined (<35 cm):

The figures below show the fitted model plotted for each species and the cross validated residuals plotted
as a function of DBH. In the plot of fitted models, light grey curves show the f(-i) models fit during crossvalidation.

© Wildlife Works Carbon, LLC 201074

Kasigau Corridor REDD Project Phase I – VCS PD Requirements© Wildlife Works Carbon, LLC 201075

Kasigau Corridor REDD Project Phase I – VCS PD Requirements© Wildlife Works Carbon, LLC 201076

Kasigau Corridor REDD Project Phase I – VCS PD Requirements© Wildlife Works Carbon, LLC 201077

Kasigau Corridor REDD Project Phase I – VCS PD Requirements© Wildlife Works Carbon, LLC 201078

Kasigau Corridor REDD Project Phase I – VCS PD Requirements© Wildlife Works Carbon, LLC 201079

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Genus Level:

10

20

30

40

50
0
-50
0

10

20

DBH

DBH

CommiphoraCommiphora

30

40

30

40

150
100
50
-50

0

50
0

cross-validated error (%)

-50

1000

-100
-150

0

-150

-100

500

Green Weight

1500

100

150

2000

0

-100

-100

100

-50

0

cross-validated error (%)

400
200

300

Green Weight

500

50

600

700

100

Acacia100

Acacia

0

10

20

30
DBH

© Wildlife Works Carbon, LLC 2010

40

0

10

20
DBH

80

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Boscia

100
50
0
-50
-100

0

-100

200

-50

0

cross-validated error (%)

600
400

Green Weight

800

50

1000

100

1200

Boscia

20

30

40

0

10

20

DBH

DBH

Boswellia

Boswellia

30

40

30

40

0

10

20

30
DBH

© Wildlife Works Carbon, LLC 2010

40

50
0
-50
-100

-100

100

200

-50

0

cross-validated error (%)

400
300

Green Weight

500

50

600

100

10

100

0

0

10

20
DBH

81

0
10
20

DBH
30

© Wildlife Works Carbon, LLC 2010
40
200

40

100

200

2000

30

0

100

1500

20

0

cross-validated error (%)

1000

Green Weight

10

-100

-100

500
0

-200

-200

0

0

-150

-150

50

-100

-100

100

-50

-50

0

0

50

50

cross-validated error (%)

200

Green Weight
150

250

100

100

300

150

150

350

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

LanneaLannea

DBH
0

0

10

10

20
DBH

all
all

20

30
40

DBH
30
40

82

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Estimated Total Carbon Stock, Standard Error and Sample Size for each Stratum and Pool
The estimated total carbon stock, standard error and sample size for each stratum and each carbon poolis shown in the table below. This summary is based on the exhaustive field sampling procedures
explained in 'Standard Operating Procedure Biomass, 01/11/2011' and 'Standard Operating Procedure
Soils, 01/02/2011'.
StratumnArea (ha)Trees
CarbonMean(tCO2e/ ha)

ShrubsCarbonMean(tCO2e/ ha)

Herbaceous
CarbonMean(tCO2e /ha)

TotalStrataMean(tCO2e/ ha)

Total Strata
Carbon Stock(t C02-e)

ag active

12

713.7

67.98

23.08

2.88

172.24

122,925.5

dryland forest strata 1+2

266883.6

39.98

8.48

1.41

91.42

629,289.1

dryland forest strata 3

165651.1

40.75

2.45

0.99

81.01

457,776.5

dryland forest strata 4112773.4

47.51

3.04

0.77

94.09

260,949.1

dryland forest strata 5188133.4

46.23

2.30

2.14

92.89

755,520.4

dryland forest strata 6234345.5

35.87

7.26

2.36

83.39

362,368.4

grassland

41610.9

3.05

1.40

4.85

17.06

27,474.3

montane forest

3

57.1

45.56

33.45

0.00

144.868,265.6
2,624,568.9

Total:

30,168.66

Table 11. Total carbon stocks for trees, shrubs and herbaceous material for Rukinga Ranch
A detailed biometric database containing all carbon pool measurements for all plots for the project areavailable to the validators for perusal upon request in the 'Rukinga Carbon Trees Shrubs Grass v7,
01/14/2011' carbon pool database.
Standard errors of the total for each stratum is listed in the table below:
StrataFPC * a2 * var / n

Sample
Size
12

Mean
Stock

Variance

FPC

172.24106559.66

0.9974508238095.9

67143.41

dryland forest strata 1+2

26

91.42

4726.310.999

8607012582.3

92773.99

dryland forest strata 3

16

81.01

1348.43

0.999

2689881737.7

51864.07

dryland forest strata 4

11

94.09

1132.370.999

791212498.2

28128.5

dryland forest strata 5

18

92.89

752.861.000

2765646392.8

52589.41

dryland forest strata 6

23

83.39

3772.490.999

3094010378.6

55623.83

grassland

4

17.06

18.72

1.000

12139791.7

3484.22montane forest

3

144.8613667.31

0.990

14679751.0

3831.416

ag active

Standard
Error

Table 12. Standard Errors for each stratum for all carbon pools for Rukinga Ranch
Trees, shrubs, grass (forest)
Standard Error
95% interval
Error percentage

149942.73
293887.74
11.20%

Table 13. Combined standard error percentage for trees, shrubs and grass
A detailed standard error analysis for each carbon pool by stratum is available in the database 'Rukinga
Carbon Trees Shrubs Grass v7, 01/14/2011'© Wildlife Works Carbon, LLC 201083

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

Soil Carbon measurements were not stratified, as test measurements were made using the strata foundin figure 10, and it was concluded that stratification did not improve measurement accuracy. Soil samples
were measured both inside Rukinga (the project area) and in the reference region at shambas (farms).
The table below shows a summary (means) for the soil organic carbon measured inside Rukinga Ranchand in the shambas in the reference region.
0-30cmbulk
density(g/cm3)

Carbon(%)

Soil
Carbon(t/ha)

Reference

1.50

0.55

Rukinga

1.32

0.70

31-100cm
bulk
density(g/cm3)

Carbon(%)

Soil
Carbon(t/ha)

24.44

Soil
GHG
equiv.
(t/ha)
89.63

1.41

0.38

27.38

100.401.34

0.92

total (1m)
Soil
Carbon(t/ha)

36.65

Soil
GHG
equiv.
(t/ha)
134.38

61.09

Soil
GHG
equiv.
(t/ha)
224.0184.85

311.13112.24411.53

Table 14. Mean Soil Carbon Stocks measured inside Rukinga and in the Reference Region
The % soil loss was determined as 0.456 (see section 6.5.5 - fitting the soil carbon loss model) and thecorresponding total carbon loss is determined by multiplying this percentage loss by the total carbon
stock measured inside Rukinga Ranch:
Rukinga Ranch
Mean Carbon Stock measured in Rukinga

411.53 t CO2eStandard Error of mean carbon stock

21.21 t CO2e

Percent Error at 95% confidence

0.10

Soil Crediting Area (conservatively reduced)

28,776.39 ha

Total soil carbon stock measured in Rukinga

11,842,347.78 t CO2e

Total Soil "loss"

5,396,221.82 tonnes

Table 15. Summary for soil carbon stocks in Rukinga Ranch
Standard error for soil stocks measured inside Rukinga Ranch are as follows:

Soil - Rukinga
total stocks
Se total
95% interval
Error percentage

11,842,347.78
610,218.21
1,196,027.68
10.10%

Table 16. Standard error percentage for soil
Details for the soil carbon loss model, including standard error analysis are available in the 'Rukinga 1m
Soil Analysis, 01/14/2011' spreadsheet.
Estimated Total Carbon Stock and Standard Error for Entire Project Area
The total carbon stocks for trees, shrubs and grass for Rukinga Ranch, above and below ground, is
2,624,569 tonnes CO2e.

© Wildlife Works Carbon, LLC 201084

Kasigau Corridor REDD Project Phase I – VCS PD Requirements

As it is assumed that soil carbon is not 100% depleted during the deforestation process, soil carbon
values are measured inside Rukinga Ranch as well as outside the ranch in the reference region at
deforested locations. The percentage soil carbon loss is multiplied by the total carbon stock inside
Rukinga to yield the carbon "loss" value, and is 5,396,222 tonnes CO2e.
The total monitored carbon stock for the Kasigau Corridor Phase I Project is:
8,020,791 tonnes CO2e
The total carbon inventory standard error across all pools is the quadratic sum of errors for all pools for all
strata:Total inventory error
total stocks
Se total
95% interval
Error percent

14,466,916.7
628,370.1775
1,231,605.548
8.51%

Table 17. Total Carbon inventory error
Monitoring of Deforestation in the Project Area
For future monitoring periods, Wildlife Works will measure any deforestation within the project area either
through intensification of biomass plots, or assessment of remotely sensed imagery. Any measured
deforestation will be directly applied to the project's net emissions totals (i.e. subtracted from emissions
reductions) for the with-project scenario. If the level of deforestation within the project area falls below thede minimus level as stated in IPCC 2006, it shall be excluded.

© Wildlife Works Carbon, LLC 201085