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Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder farming systems of western Kenya M. Henry a,b,c,d, *, P. Tittonell c,e , R.J. Manlay a,d , M. Bernoux a , A. Albrecht f , B. Vanlauwe c a Institut de Recherche pour le De ´veloppement, IRD, UR SeqBio, SupAgro, Bat. 12, 2 place Viala, 34060 Montpellier Cedex 1, France b Institut des Re ´gions Chaudes, IRC-Montpellier Supagro - 1101, avenue Agropolis, BP 5098, 34033 Montpellier Cedex 1, France c Tropical Soil Biology and Fertility Institute of the International Centre for Tropical Agriculture (TSBF-CIAT), United Nations Avenue, P.O. Box 30677, Nairobi, Kenya d Paris Institute of Technology for Life, Food and Environmental Sciences, AgroParisTech-ENGREF, GEEFT, 648 rue Jean-Franc ¸ois Breton, BP 7355, 34086 Montpellier Cedex 4, France e Plant Production Systems, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands f Institut de Recherche pour le De ´veloppement, IRD-Madagascar, BP 434, 101 Antananarivo, Madagascar 1. Introduction Maintenance of (agro-)biodiversity and carbon sequestration through the process of photosynthesis are two important and complementary environmental service functions of agroecosys- tems. While C sequestration in the biosphere is seen as an option to mitigate climate change (e.g., Houghton et al., 1993), we are only beginning to understand the effects of biodiversity on the C cycle (Schulze, 2006). In tropical forests, carbon storage depends largely on species composition (Bunker et al., 2005) and thus there may exist a close relationship between C stocks and biodiversity. In agroecosystems, although organic C stocks in the soil represent often the largest C sink (Dixon, 1995), aboveground biodiversity may still play an important role in C sequestration with consequent positive Agriculture, Ecosystems and Environment 129 (2009) 238–252 ARTICLE INFO Article history: Received 19 March 2008 Received in revised form 14 August 2008 Accepted 5 September 2008 Available online 14 November 2008 Keywords: Sub-Saharan Africa Land use Trees on farm Tree allometry Agroforestry Clean Development Mechanism ABSTRACT While Carbon (C) sequestration on farmlands may contribute to mitigate CO 2 concentrations in the atmosphere, greater agro-biodiversity may ensure longer term stability of C storage in fluctuating environments. This study was conducted in the highlands of western Kenya, a region with high potential for agroforestry, with the objectives of assessing current biodiversity and aboveground C stocks in perennial vegetation growing on farmland, and estimating C sequestration potential in aboveground C pools. Allometric models were developed to estimate aboveground biomass of trees and hedgerows, and an inventory of perennial vegetation was conducted in 35 farms in Vihiga and Siaya districts. Values of the Shannon index (H), used to evaluate biodiversity, ranged from 0.01 in woodlots through 0.4–0.6 in food crop plots, to 1.3–1.6 in homegardens. Eucalyptus saligna was the most frequent tree species found as individual trees (20%), in windrows (47%), and in woodlots (99%) in Vihiga and the most frequent in woodlots (96%) in Siaya. Trees represented the most important C pool in aboveground biomass of perennial plants growing on-farm, contributing to 81 and 55% of total aboveground farm C in Vihiga and Siaya, respectively, followed by hedgerows (13 and 39%, respectively) and permanent crop stands (5 and 6%, respectively). Most of the tree C was located in woodlots in Vihiga (61%) and in individual trees growing in or around food crop plots in Siaya (57%). The homegardens represented the second C pool in importance, with 25 and 33% of C stocks in Vihiga and Siaya, respectively. Considering the mean total aboveground C stocks observed, and taking the average farm sizes of Vihiga (0.6 ha) and Siaya (1.4 ha), an average farm would store 6.5 0.1 Mg C farm 1 in Vihiga and 12.4 0.1 Mg C farm 1 in Siaya. At both sites, the C sequestration potential in perennial aboveground biomass was estimated at ca. 16 Mg C ha 1 . With the current market price for carbon, the implementation of Clean Development Mechanism Afforestation/ Reforestation (CDM A/R) projects seems unfeasible, due to the large number of small farms (between 140 and 300) necessary to achieve a critical land area able to compensate the concomitant minimum transaction costs. Higher financial compensation for C sequestration projects that encourage biodiversity would allow clearer win–win scenarios for smallholder farmers. Thus, a better valuation of ecosystem services should encourage C sequestration together with on-farm biodiversity when promoting CDM A/R projects. ß 2008 Elsevier B.V. All rights reserved. * Corresponding author at: Di.S.A.F.Ri. - Facolta ` di Agraria, Universita ` degli Studi della Tuscia, Via Camillo de Lellis, snc – 01100 Viterbo, Italy. Tel.: +39 0761 357394; fax: +39 0761 357389. E-mail address: [email protected] (M. Henry). Contents lists available at ScienceDirect Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee 0167-8809/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2008.09.006
Transcript
Page 1: Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder

Agriculture, Ecosystems and Environment 129 (2009) 238–252

Biodiversity, carbon stocks and sequestration potential in abovegroundbiomass in smallholder farming systems of western Kenya

M. Henry a,b,c,d,*, P. Tittonell c,e, R.J. Manlay a,d, M. Bernoux a, A. Albrecht f, B. Vanlauwe c

a Institut de Recherche pour le Developpement, IRD, UR SeqBio, SupAgro, Bat. 12, 2 place Viala, 34060 Montpellier Cedex 1, Franceb Institut des Regions Chaudes, IRC-Montpellier Supagro - 1101, avenue Agropolis, BP 5098, 34033 Montpellier Cedex 1, Francec Tropical Soil Biology and Fertility Institute of the International Centre for Tropical Agriculture (TSBF-CIAT), United Nations Avenue, P.O. Box 30677, Nairobi, Kenyad Paris Institute of Technology for Life, Food and Environmental Sciences, AgroParisTech-ENGREF, GEEFT, 648 rue Jean-Francois Breton, BP 7355,

34086 Montpellier Cedex 4, Francee Plant Production Systems, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlandsf Institut de Recherche pour le Developpement, IRD-Madagascar, BP 434, 101 Antananarivo, Madagascar

A R T I C L E I N F O

Article history:

Received 19 March 2008

Received in revised form 14 August 2008

Accepted 5 September 2008

Available online 14 November 2008

Keywords:

Sub-Saharan Africa

Land use

Trees on farm

Tree allometry

Agroforestry

Clean Development Mechanism

A B S T R A C T

While Carbon (C) sequestration on farmlands may contribute to mitigate CO2 concentrations in the

atmosphere, greater agro-biodiversity may ensure longer term stability of C storage in fluctuating

environments. This study was conducted in the highlands of western Kenya, a region with high potential

for agroforestry, with the objectives of assessing current biodiversity and aboveground C stocks in

perennial vegetation growing on farmland, and estimating C sequestration potential in aboveground C

pools. Allometric models were developed to estimate aboveground biomass of trees and hedgerows, and

an inventory of perennial vegetation was conducted in 35 farms in Vihiga and Siaya districts. Values of the

Shannon index (H), used to evaluate biodiversity, ranged from 0.01 in woodlots through 0.4–0.6 in food

crop plots, to 1.3–1.6 in homegardens. Eucalyptus saligna was the most frequent tree species found as

individual trees (20%), in windrows (47%), and in woodlots (99%) in Vihiga and the most frequent in

woodlots (96%) in Siaya. Trees represented the most important C pool in aboveground biomass of

perennial plants growing on-farm, contributing to 81 and 55% of total aboveground farm C in Vihiga and

Siaya, respectively, followed by hedgerows (13 and 39%, respectively) and permanent crop stands (5 and

6%, respectively). Most of the tree C was located in woodlots in Vihiga (61%) and in individual trees

growing in or around food crop plots in Siaya (57%). The homegardens represented the second C pool in

importance, with 25 and 33% of C stocks in Vihiga and Siaya, respectively. Considering the mean total

aboveground C stocks observed, and taking the average farm sizes of Vihiga (0.6 ha) and Siaya (1.4 ha), an

average farm would store 6.5 � 0.1 Mg C farm�1 in Vihiga and 12.4 � 0.1 Mg C farm�1 in Siaya. At both sites,

the C sequestration potential in perennial aboveground biomass was estimated at ca. 16 Mg C ha�1. With the

current market price for carbon, the implementation of Clean Development Mechanism Afforestation/

Reforestation (CDM A/R) projects seems unfeasible, due to the large number of small farms (between 140 and

300) necessary to achieve a critical land area able to compensate the concomitant minimum transaction costs.

Higher financial compensation for C sequestration projects that encourage biodiversity would allow clearer

win–win scenarios for smallholder farmers. Thus, a better valuation of ecosystem services should encourage C

sequestration together with on-farm biodiversity when promoting CDM A/R projects.

� 2008 Elsevier B.V. All rights reserved.

Contents lists available at ScienceDirect

Agriculture, Ecosystems and Environment

journa l homepage: www.e lsev ier .com/ locate /agee

1. Introduction

Maintenance of (agro-)biodiversity and carbon sequestrationthrough the process of photosynthesis are two important and

* Corresponding author at: Di.S.A.F.Ri. - Facolta di Agraria, Universita degli Studi

della Tuscia, Via Camillo de Lellis, snc – 01100 Viterbo, Italy. Tel.: +39 0761 357394;

fax: +39 0761 357389.

E-mail address: [email protected] (M. Henry).

0167-8809/$ – see front matter � 2008 Elsevier B.V. All rights reserved.

doi:10.1016/j.agee.2008.09.006

complementary environmental service functions of agroecosys-tems. While C sequestration in the biosphere is seen as an option tomitigate climate change (e.g., Houghton et al., 1993), we are onlybeginning to understand the effects of biodiversity on the C cycle(Schulze, 2006). In tropical forests, carbon storage depends largelyon species composition (Bunker et al., 2005) and thus there mayexist a close relationship between C stocks and biodiversity. Inagroecosystems, although organic C stocks in the soil represent oftenthe largest C sink (Dixon, 1995), aboveground biodiversity may stillplay an important role in C sequestration with consequent positive

Page 2: Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252 239

impacts on belowground C sequestration (e.g., through litter fall,root exudation and turnover or soil erosion control). Agroecosys-tems with a broader diversity of plant species, living forms andproduction activities may achieve higher levels of productivity inthe long-term while maintaining larger and more stable Cstocks (Yachi and Loreau, 1999). Biodiversity in agroecosystemsmay also contribute to diversification of products and diets, and toincome stability (Brookfield et al., 2002)—a win–win alternative forsmallholder farmers in Sub-Saharan Africa (SSA), who mayeventually also benefit from C payment schemes.

At present, C sequestration is valued as a function of creditemission reductions (CERs), based on the difference between theamount of C stored in scenario projects and the baseline, currentamount of C stored in the system (UNFCCC, 2004). Here, wedefine C sequestration as the amount of C that can be additionallystored in an agroecosystem (Bernoux et al., 2006). Agro-forestrysystems stand larger chances to sequester C in the long-termthan annual cropping systems, adding aboveground C storagecapacity through a broader diversity of living forms, includingfruit or timber trees on-farm and/or perennial crops, pluspotential ‘fertiliser’ and ‘fodder’ trees. Worldwide, the averageamount of C stored in the aboveground compartments of agro-forestry systems was estimated to range between 40 and150 t C ha�1 (IPCC, 2007). Albrecht and Serigne (2003) estimateda potential C sequestration in tropical agro-forestry systems of95 t C ha�1 (varying widely between 12 and 228 t C ha�1).Variability in C sequestration and biodiversity can be high withincomplex agroecosystems, depending on factors such as vegetationage, structure, management practices, land uses and landscape(Montagnini and Nair, 2004).

In areas of the Tropics characterized by agro-forestry systemswith dense human population such as western Kenya, and wheresmallholder subsistence agriculture predominates, the expansionand intensification of integrated agro-forestry systems may be analternative to increase biodiversity and contribute to C seques-tration. Although afforestation is probably one of the quickestmeans of increasing aboveground C stocks, increasing thenumber of trees on farms that have an average area of around1 ha without compromising food production is a real challenge.Our region of study in western Kenya is characterized by highagricultural potential that attracted large human settlement inthe past, resulting in extensive land fragmentation and degrada-tion (Crowley and Carter, 2000). Previous studies in this region(Bradley, 1988) analysed the capacity of such smallholder systemsto produce wood biomass identifying five vegetation types –woodlots, windrows, individual trees, hedgerows and riparianvegetation – and developed allometric regressions for theirbiomass assessment. Lauriks et al. (1998) conducted a hedgerowinventory and proposed a typology of hedgerows based on theirfloristic composition and biomass density. Other, more recentstudies measured tree biodiversity in relation to farm character-istics (e.g., Kindt et al., 2004). However, formal studies linkingaboveground C storage capacity and biodiversity in theseintegrated smallholder agro-forestry systems are lacking.

To assess the capacity of smallholder farming systems to store Cin their aboveground biomass it is necessary to analyse (i) theircurrent status in terms of biomass structure, diversity andfunctioning, (ii) the factors driving variability in aboveground Cstocks across farms and (iii) the potential for increasing above-ground C stocks through changes in the structure of the agro-ecosystem and/or land use. This would allow identifying ‘niches’for tree intensification and C sequestration within the complexityof smallholder systems. Our specific objective was to assesscurrent biodiversity of permanent vegetation and aboveground Cstocks in representative smallholder farms of western Kenya.

Through estimating C sequestration potential under differentscenarios of intensification we explored the feasibility of imple-menting Clean Development Mechanism (CDM) projects involvingsmallholder farmers in the region.

2. Materials and methods

2.1. Study sites

The study was conducted in Vihiga and Siaya districts ofwestern Kenya (Fig. 1A). These districts were selected in a projectcovering a broader area of Kenya and Uganda, to represent areasof the East African highlands differing in demography, agroecol-ogy and access to markets (Tittonell, 2007). Vihiga and Siayadistricts cover an area of 570 and 1521 km2, respectively, andrepresent to a large extent the variability found in westernKenya, with average altitudes of 1600 and 1200 m.a.s.l., andannual rainfall of 1800 and 1400 mm following a bimodaldistribution (i.e., the long and the short rains) that allows twocropping seasons per year. About 78 and 86% of the area of Vihigaand Siaya districts correspond to agricultural land (GEFSOC,2005). Population densities range between 300 and 1200inhabitants km�2 (GOK, 2003), with average farm sizes <1 hain Vihiga and between 1 and 2 ha in Siaya. Dominant soil typesinclude Acrisols, Ferralsols and Nitisols (Jaetzold and Schmidt,1982) and are characterised by a good physical structure but lownutrient reserves due to prolonged weathering, and morerecently by intense agricultural use (Shepherd and Soule,1998). Individual farms are broadly oriented along typicaltoposequences, with the homestead located upslope nearest tothe road network and cropping activities located mostly on theslopes towards the waterways (Fig. 2) (Tittonell et al., 2005). Thearea of individual farms may be highly fragmented into land useunits that range between 0.033 and 0.7 ha in area. Smallholderfarms in the area can be considered agro-forestry systems in thesense that they integrate crop-livestock activities and on-farmwood production on small areas of land.

2.1.1. Land use types (LUT)

During our farm visits land use units were classified accordingto the dominant land use type observed into homegardens (HG),food-crop plots (FP), cash-crop plots (CP), pasture plots (PP) andwoodlots (WL) (Fig. 2). HG are the plots around the homesteadwhere farmers grow vegetables for the household, receiving mostorganic nutrient resources in the form of animal manure, compostand kitchen waste. FP are essentially a subsistence-oriented landuse type, cropped with maize, sorghum and beans. CP are typicallythose in which tea (Camellia sinensis L.) is grown; however, giventhe increasing market orientation of crops such as banana (Musa

sp.) and Napier grass (Pennisetum purpureum Schum.), these plotswere also counted as CP. Plots located in valley bottoms weremainly allocated to maize or vegetables and to eucalyptus(Eucalyptus saligna S.M.) woodlots in Vihiga, while these wereused for pastures, fallows, or vegetable cultivation in Siaya.

2.2. Farm sampling

Two localities were selected in each district, Emuhaia andEbusiloli villages in Vihiga district, and Nyabeda and Nyalungungavillages in Siaya district (Fig. 1B), and within each of these a Y-framed sampling scheme was randomly located to represent soil/landscape variability (Fig. 1C). One geo-referenced point waschosen in each village to locate the centre of the Y-frame with theaid of a GPS. Each Y-frame had a radius of 900 m and included 10farms along 3 transects diverting 120 degrees from each other. A

Page 3: Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder

Fig. 1. Farm sampling method. (A) GPS points were randomly located in two localities of Vihiga district and two of Siaya district, western Kenya; (B) from each GPS point three

axes departing 1208 from each other were demarcated, and three GPS points were located along each axis, at 100, 300 and 900 m from the center; (C) detail of a Y-sampling

frame indicating the 10 GPS points and the GIS polygons representing the adjacent farms to each of the points, which were selected for field assessments; (D) amplification of

one of the selected farms, indicating the internal boundaries between the various land use units.

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252240

farm in the center of the frame and three farms on each transect at100, 300 and 900 m from the centre were selected for detailedcharacterization and quantification of C stocks (Fig. 1D). Even-tually, 35 farms out of the 40 farms selected in the four villages (8in Ebusiloli, 8 in Emusutswi, 10 in Nyalugunga, and 9 in Nyabeda)were included in the assessments, since five of the randomlyselected farmers were not willing to participate.

2.3. Vegetation components and spatialisation

Permanent vegetation was divided into three main compo-nents: trees, hedgerows, and permanent crops. Food crops andNapier grass stands were not considered, since their abovegroundbiomass was removed after each cropping season. For the treecomponent, three different types of tree formations wereidentified: individual trees, windrows and woodlots. Individualtrees were isolated trees, planted in or around the cropland and/or

Fig. 2. Schematic presentation of the various aboveground vegetation components

considered. Each plot within a farm was allocated to one land use type (LUT):

homegarden, food crop, cash crop, pasture, or woodlot. Three vegetation

components were distinguished: trees, hedgerows, and permanent crops. Trees

were classified as individual tree (it), windrow (wt) and woodlot (wlt). Hedgerows

were classified as high (hh), medium (mh) and low (lh) density hedgerows.

Permanent crops included tea (t), banana (b) and Napier grass (g).

around the homestead for various purposes, e.g., fruit, firewood,shade, etc. Windrows were considered as linear tree formations,normally planted along the edge of the land use units. Woodlotswere defined as small, mono-specific areas of cultivated trees. Thesecond vegetation component, hedgerow, was defined as a linear,perennial, and homogenous vegetation component in terms offloristic composition and management, containing trees and othervegetation types. Based on the structure of the hedgerow, theywere classified as high-, medium-, or low-density hedgerows asexplained below. For the third component, permanent crops, thisstudy considered the three major ones observed in the region:Kikuyu grass (mostly Brachiaria sp.), Tea and Banana stands. Alltrees, hedgerows and permanent crop stands were geo-referencedand mapped with Garmin GPS Map76 (�3 m) (see example inFig. 3). Digital spatialisation was done with Arcview GIS 3.2 (ESRI,1997). Individual trees were represented as points, windrows andhedgerows as lines, and woodlots, permanent crop stands and fieldplots as polygons.

2.4. Diversity of perennial vegetation on-farm

Species biodiversity was determined for the permanentvegetation growing in or around each individual land use unit j,by considering tree species and species growing on the hedgerow.Permanent and annual crops were considered monospecific.Measurement of tree biodiversity was based on a complete treespecies inventory in farms using local and/or scientific names (e.g.,Kindt et al., 2004). Biodiversity was measured for individual landuse units j, which delimited by fix boundaries, calculating an indexbased on the number of species and their abundance. Out of a widerange of biodiversity indexes with different calculation methodsavailable in literature (Magurran, 1988), we chose to use theShannon index (H), which has been proposed to estimatebiodiversity in carbon sequestration projects (Ponce-Hernandez,

Page 4: Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder

Fig. 3. Map of aboveground C densities in plant biomass of a sample farm in Vihiga district. C densities are presented for trees, hedgerows and permanent crops. Individual

trees are represented with points. Windrows and hedges are represented as lines. Woodlots, permanent crops and plots are represented by polygons.

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252 241

2004). Shannon index was calculated by multiplying the abun-dance of a species (pi) by the logarithm of this number:

H j ¼ �Xm

i¼1

pi j lnðpi jÞ (1)

where H is the Shannon index for vegetation component orformation or land use type j depending on the scale.

pi j ¼ni j

N j(2)

where ni is the number of subjects from the species i and N thetotal number of subjects within the plot j.Hedgerow biodiversitywas also assessed with the Shannon index but rather thanidentifying the species of each stem, a simplified methodologyto estimate frequency of plant species was used, based oncalibration of visual observations. First, the exact species of eachhedgerow stem was identified for 20 hedgerows representingthe range of plant species and hedgerow density classes obser-ved, and the species composition calculated for each of them.Then, the floristic composition of each hedgerow estimatedthrough visual observation, and expressed as a percentage ofeach of the species and the total number of these, was calibratedagainst their exact species composition. Since field measure-ments provided data for the proportion of species i within eachhedgerow h, noted Pih, the proportion of species i for each plot j,noted Pij, was estimated based on two assumptions: (1) thenumber of subjects of species i within the hedgerow h wasproportional of hedgerow length (Lh) and (2) proportional tohedgerow density class (dh): there were three times moresubjects within a high density than a low-density hedgerow,and two times more within a medium than a low-densityhedgerow. Thus, the proportion of species i around plot j wascalculated as:

Pi j ¼Pn

h¼1

Pni¼1 Pih � Lh � dhPn

h¼1 Lh � dh

(3)

2.5. Development of allometric relationships

Allometric relationships for trees are generally based onmeasurement of the diameter at breast height (DBH) (Brownet al., 1989). To develop these relationships, 26 trees were selectedfrom various species and different functions (i.e., timber trees, fruittrees, etc.), growing as individual trees, in windrows or woodlots, inor around plots under different LUT, and with DBH ranging 5–32 cm.For each of these trees, DBH, height and crown diameter weremeasured. DBH was measured with a calliper. The height of treeslower than 6 m was measured with a levelling staff and the height ofthose higher than 6 m with a Suunto dendrometer. Tree groundcover was based on measurement of the projection of the crowndiameter with a measuring tape. Trees were logged according tolocal practice, leaving a stump 0.1–0.9 m high. Abovegroundbiomass of the trunk (Mtr), branches (Mb), and leaves (Ml) wascalculated from the measured fresh biomass. Fresh biomass wasdirectly measured with a spring balance (�100 g). The biomass ofpreviously pruned branches (Mp) was derived from linear regressionmodels that were developed with the data from the destructive treebiomass assessments. Such models linked the basal diameter of thepruned branches with their fresh biomass for each of the speciesconsidered (Fig. 4). The biomass of the stump (Ms) was calculated fromits volume and wood density estimates based on 5 samples taken in themiddle of the trunk. The fresh biomass of these wood samples wasmeasured with an electronic balance (�1 g) and their volume bymeasuring the volume of water displaced (�10 cm3) after immersion inwater. The moisture content of the woody biomass was assessed on 26samples cut into small pieces (�5 cm� 2 cm), oven-dried at 106 8C(�5 8C), and weighed. The total leaf dry biomass was calculated fromthe fresh biomass, corrected with their moisture content. The total treedry mass (Mt) was calculated based on fresh biomass and moisturecontent of the different plant parts, and then aggregated as:

Mt ¼1

1þ sw� ðMtr þMb þM p þMsÞ þ

1

1þ slMl (4)

where sw is wood moisture and sl is leaf moisture content.

Page 5: Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder

Fig. 4. Relation between the log of basal branch diameter (in cm) and the log of branch fresh weight (in kg) for the predominant tree species growing in smallholder farms of

western Kenya. These allometric relationships were used to estimate biomass of pruned branches from measurements of their basal diameter during the biomass inventories.

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252242

Allometric relationships to calculate Mt were obtained using thegeneric formula of Satoo (1955), cited in Ponce-Hernandez (2004):

ln Mt ¼ aþ b� lnðDBH2 � heightÞ (5)

with DBH in cm and tree height in m.Since hedgerow biomass is mainly a function of plant species

and density (Lauriks et al., 1998), hedgerow allometric coefficients

were built for hedgerow types differing in floristic composition anddensity class (low, medium and high). Hedgerow types based onfloristic composition were identified using hierarchical clusteranalysis of hedgerow composition data (n = 440), as explained inLauriks et al. (1998). To measure the specific biomass density (Kh)of different hedgerows, 3 samples per hedgerow type and densityclass were selected. Each sample consisted of all the biomasscorresponding to two meters of hedgerow cut at the soil surface.

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M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252 243

These hedgerow biomass samples were chopped into small piecesand weighed. The moisture content was measured on sub-samples,dried at 65 8C, and the dry biomass (mh) calculated. Kh wascalculated as:

Kh ¼mdh

vh(6)

where vh is the volume of the hedgerow sample calculated frommeasurements of its width and height prior to sampling.

Biomass of tea plantations, banana mats, and grassland standswere estimated from specific coefficients found in literature. Theaboveground biomass of grassland (Mg) was estimated assuming adensity of 6.2 t ha�1 (�75%) proposed by IPCC (2003) for tropicalgrasslands. According to Ng’etich and Stephens (2001), the above-ground biomass of a 36-months old tea plantation in Kericho, Kenya,ranges between 17.6 and 23 t ha�1 for different clones andexperimental sites. In this study, biomass for tea plantation wasbased on a mean value (Mte) of 20.3 t ha�1. Aboveground biomass ofbananas (Mba) was estimated using the following empirical model(Arifin, 2001 cited in Hairiah et al., 2001):

Mba ¼ 0:03� DBH2:13 (7)

With Mba in g and DBH in cm.

2.6. Quantifying aboveground C stocks

All individual and windrow trees were inventoried in the 35farms visited, and a 30 m2 sub-plot was inventoried in each ofthe woodlots present on these farms. The inventories includedall non-destructive measurements necessary to use the allo-metric models introduced (e.g., DBH, height) plus the floristiccomposition of the different vegetation components. Hedgerowbiomass (Mh) was estimated by multiplying hedgerow-specificbiomass density (Kh) times hedgerow volume (Vh) for the 440hedgerows that were measured. Vh was assessed from length,width and height measurements. Lengths shorter than 20 mwere measured with a tape measure while those exceeding 20 mwere measured with a Garmin GPS Map76 (�3 m). Hedgerowbiomass was obtained from the following equation:

Mh ¼ Vh � Kh (8)

The estimation of banana biomass was based on measurements ofDBH for all banana stems and the biomass allometric relationshipintroduced earlier (Eq. (7)). Tea and grass biomass were calculatedfrom the area of the plots and the assumed average biomassdensities indicated above. The area of all plots was measured witha Garmin GPS Map76 (�3 m). The biomass of trees, hedgerows andpermanent crops was aggregated at plot and farm scale to calculatetotal biomass per plot, per land use type (LUT), per vegetationcomponent, per tree management type, per hedgerow floristic typeand density class, and per individual farm. Aboveground C stock fortrees (Ct), hedgerows (Ch) and permanent crops (Cp) were obtainedfrom conversion of aboveground biomass (Mt, Mp and Mh) into C usinga conversion coefficient of 0.5 kg C kg DM�1 (IPCC, 2007). C stockswere expressed as:

(i) a

boveground C stock on an area basis (Mg ha�1), dividing thestock of C of a certain vegetation component by the area of theplot that contained it;

(ii) a

boveground C density (kg m�2), dividing the stock of C of acertain vegetation component by its ground cover (i.e., verticalprojection).

2.7. A simple estimation of potential C sequestration for a small-scale

CDM project

Scenarios were analysed in which the aboveground C stocks wereincreased without affecting the distribution of LUT in a way thatcould compromise food security, basically by intensifying the treeand hedgerow vegetation components. The scenarios consisted ofsimply increasing aboveground C stock (ACS) assuming that landuses or management types would reach the maximum C stock (Cmax)that was measured across all farms in this study. Since the maximumACS that was measured is case specific, and in order to obtain morerealistic estimates of maximum C stock that could be achieved inmost farms, Cmax was assumed to be the third quartile in thedistribution of the actual ACS measurements. Maximum above-ground C stocks (MACS) were then obtained by multiplying Cmax

times land area. The potential for C sequestration was calculated asthe difference between Cmax and current C stocks. Based on thispotential C sequestration, the minimum size necessary to imple-ment a CDM project (i.e., the minimum area, the minimum numberof households to be involved) was calculated.

A major limitation of this simple approach, however, was theuncertainty about estimates of the costs of project implementationand the financial compensations that would be due to farmersparticipating in a CDM project. Conservatively, the minimum CDMproject size was assumed to correspond to the area that wouldcapture sufficient CO2 to cover the transaction costs of a CDMproject, as estimated by Locatelli and Pedroni (2006). Theseauthors estimated the following transaction costs: project design(USD 20,000), validation and registration (USD 20,000), monitoring(USD 2000), and verification costs (USD 15,000 each five years).The minimum CDM project size was calculated based on a Cmarket price of USD 10/t CO2, a crediting period of 20 years, andwithout considering project discount rates. To be viable, a CDMAfforestation/Reforestation project would have to store a mini-mum of 10,200 t of CO2 or approximately 2800 t C. The minimumnumber of participants was estimated by assuming average farmsizes of 0.6 ha in Vihiga and 1.4 in Siaya.

Three scenarios were analysed. In the first scenario (S1), thestock of C in tree biomass was increased to the MACS that wasmeasured in this study for individual trees in homegardens, foodcrop, cash crop and pastures land use units, and for trees inwindrows and woodlots. In the second scenario (S2), the stock of Cin hedgerows was increased to its maximum value as measured inhigh-, medium- and low-density hedgerows. In the third scenario(S3) both tree and hedgerow C stocks were increased up to theirMACS simultaneously.

2.8. Data analysis

Statistical analysis was implemented with XLSTAT Pro 7.2(AddinSoft, 2003). The typology of hedgerow floristic compositionwas obtained using hierarchical cluster analysis (Lauriks et al.,1998). Analysis of variance (ANOVA) was done to assess thedifferences in biodiversity and C stocks between land use types, andin C sequestration potential between sites, with means comparisonsusing the least square difference. Pearson statistical tests wereperformed to test correlations between aboveground C stocks andbiodiversity for different land use and management types.

3. Results

3.1. Plant diversity on farms

The diversity of perennial plant species varied widely betweenlocations, vegetation components, tree formations and land use

Page 7: Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder

Table 1Biodiversity of perennial plant species in different vegetation components of the 35 farms sampled in Vihiga and Siaya districts of western Kenya. The diversity of plant

species is quantified through the Shannon index for different tree formations (individual trees, windrows and woodlots), hedgerow density classes and permanent crop

stands; the most frequent species in each case are indicated.

District Vegetation component Formations, classes and stands n (plot) Shannon index Main species

Vihiga Trees Individual tree 119 0.74 � 0.12 Euc: 20%, Psi:11%, Per: 9%, Man: 7%

Windrow 15 0.36 � 0.10 Euc: 47%, Cup: 12%

Woodlot 17 0.01 � 0.01 Euc: 99%

Mean 146 0.50 � 0.05 Euc: 93%, Cup: 2%

Significance (F) ***

Hedgerows Low density 56 0.62 � 0.06 Eup: 42%, Lan: 14%, Psi: 13%, Dra: 11%

Medium density 70 0.64 � 0.04 Eup: 37%, Lan: 32%, Psi: 7%, Dra: 6%

High density 56 0.42 � 0.06 Eup: 26%, Lan: 22%, Psi: 16%, Dra: 10%

Mean 129 0.73 � 0.04 Eup: 35%, Lan: 26%, Psi: 11, Dra: 9%

Significance (F) *

Permanent crops Banana 19 – Musa sp.

Tea 2 – Camellia sinensis

Pasture 20 – Brachiaria sp.

Siaya Trees Individual tree 165 0.86 � 0.05 Mar: 47%, Man: 8%, Per: 6%, Euc: 5%

Windrow 17 0.17 � 0.06 Mar: 37%, Cup: 17%, Cas: 7%, Gre: 7%

Woodlot 3 0.22 � 0.22 Euc: 96%

Mean 224 0.62 � 0.04 Mar: 32%, Man: 18%, Euc: 8%

Significance (F) ***

Hedgerows Low density 108 0.39 � 0.04 Eup: 51%, Lan: 40%

Medium density 67 0.44 � 0.04 Lan: 55%, Eup: 28%

High density 37 0.46 � 0.06 Lan: 56%, Eup: 28%

Mean 152 0.49 � 0.03 Lan: 56%, Eup: 21%, Tith: 7%

Significance (F) ns

Permanent crops Banana 30 – Musa sp.

Pasture 36 – Brachiaria sp.

Cas: Cassia siamea, Cup: Cupressus lucastica, Dra: Draceana steudneri, Euc: Eucalyptus saligna, Eup: Euphorbia tirucalli, Fic: Ficus lutea, Gre: Grevillia robusta, Lan: Lantana camara,

Man: Mangifera indica, Mar: Markhamia lutea, Per: Persia americana, Psi: Psidium guajava. � standard error.* P(H0: Fobs > Fth = 0) < 0.05.*** P(H0: Fobs > Fth = 0) < 0.001.

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252244

types. A total of 99 perennial plant species were identified growingin the 35 farms visited, of which 76 were found in the treecomponent, 30 in the hedgerow component and 7 in bothcomponents. A total of 49 tree species were identified in thetwo locations of Vihiga and 56 in the two of Siaya. Tree biodiversityas measured with the Shannon index (H) was significantly(P < 0.05) higher in Siaya (H = 0.62) than in Vihiga (H = 0.50)(Table 1). At both sites the diversity of tree species was poorestin woodlots, intermediate in windrows and richest for treesgrowing scattered within or around the field crop plots (i.e.,individual trees in Table 1). While all of the tree species identifiedat both sites were seen growing as individual trees, only 25% ofthem were found in windrows, and only 10 and 4% in woodlots inVihiga and Siaya, respectively. Eucalyptus saligna was the mostfrequent tree species found as individual trees (20%), in windrows(47%), and in woodlots (99%) in Vihiga and the most frequentspecies in woodlots (96%) in Siaya, where Markhamia lutea K.

Schum. was most commonly observed as individual trees (47%) andin windrows (37%).

The floristic composition, structural arrangement and function-ality of hedgerows differed between Vihiga and Siaya, with 87% ofall species seen growing in hedgerows observed in Vihiga andapproximately half of that in Siaya. The average diversity of plantspecies growing in hedgerows was richer in Vihiga than in Siaya,exhibiting average H values of 0.73 and 0.49, respectively (Table 1).In Vihiga, high-density hedgerows were poorer in plant speciesdiversity than medium- and low-density hedgerows, while nodifferences in plant species diversity were observed betweenhedgerow density classes in Siaya. Euphorbia tirucalli and Lantana

camara were the species most commonly observed in growinghedgerows at both sites, with frequencies of 35 and 26% in Vihiga,and 21 and 56% in Siaya, respectively.

A hierarchical cluster analysis of hedgerow floristic compositionyielded nine hedgerow types, which are indicative of distincthedgerow configurations and functions (Table 2), as confirmed byour field assessments. Hedgerows that were planted with the mainobjective of demarcating boundaries (types 1, 5, 8 and 9) weredominated by Euphorbia tirucalli and Lantana camara and seconda-rily by Draceana steudneri (type 2). Hedgerows planted with theobjective of providing firewood (type 6) were dominated byMarkhamia lutea, while those planted with the additional objectiveof fruit production (type 4) were dominated by Psidium guajava.Hedgerows were also planted to harvest organic material for use insoil fertility management as organic soil amendments or mulches(type 3), and were dominated by Tithonia diversifolia and Acanthus

pubescens. Finally, hedgerows were also planted for ornamentalpurposes (type 7) mostly around the homestead, often demarcatingthe compound area from the field crop plots. Most of the hedgerowsobserved in the 35 farms sampled corresponded to type 8 (24.1 and54.2% in Vihiga and Siaya, respectively), while types 6 and 4 had thelowest frequency at both sites. On average, 9.3 and 15.3% of the farmarea was occupied with hedgerows in Vihiga and Siaya, respectively.Hedgerow type 8 covered 2.1 and 8.1% of the area of the surveyedfarms in Vihiga and Siaya, respectively, while hedgerow type 6covered barely 0.2 and 0.5% (Table 2).

The diversity of tree species growing in or around the land useunits varied widely between plots that were under different typesof land use (LUT), with the poorest values obviously in woodlots(H = 0.09 and 0.34) and the richest in homegardens (H = 1.3 and1.6) at both sites (Table 3). Biodiversity within hedgerows did notdiffer significantly between LUTs in Vihiga. In Siaya, hedgerowswere more diverse around the homegardens (H = 0.60) and pastureplots (H = 0.56) than in food (H = 0.29) and cash crop (H = 0.06)plots, or woodlots (H = 0.23) (P < 0.05).

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Table 2A typology of hedgerows based on their floristic composition (% of the total number of species). Ornamental species regrouped plant species used for ornamental purposes.

Other species regrouped plant species of minor importance in term of frequency.

Species Hedgerow types (% of species)

1 2 3 4 5 6 7 8 9

(n = 28) (n = 26) (n = 55) (n = 17) (n = 86) (n = 16) (n = 57) (n = 138) (n = 17)

Euphorbia tirucalli L. 47.0 � 3.9 1.5 � 0.7 0.5 � 0.4 4.4 � 2.1 90 � 1.4 6.3 � 3.0 5.8 � 1.7 1.5 � 0.4 71.0 � 2.9

Lantana camara L. 49.0 � 3.7 3.3 � 2.1 3.5 � 1.1 7.9 � 3.5 2.4 � 0.4 6.3 � 3.6 6.0 � 1.8 89.0 � 1.3 23.0 � 1.8

Markhamia lutea

K. Schum.

0.4 � 0.3 6.7 � 3.2 0.9 � 0.5 4.4 � 2.4 1.2 � 0.4 66.0�4.3 0.2 � 0.18 1.7 � 0.5 –

Dracaena steudneri Engl. 3.6 � 2.0 85.0 � 3.5 0.3 � 0.2 – 2.3 � 0.9 4.4 � 2.2 – 0.6 � 0.3 –

Psidium guajava L. 0.4 � 0.3 – 2.5 � 0.9 74 � 5.1 2.2 � 0.5 3.1 � 2.5 0.9 � 0.88 3.8 � 0.7 6.2 � 2.8

Tithonia diversifolia

Hemsl.

– 1.5 � 1.0 23 � 5.6 0.6 � 0.6 1.2 � 0.7 5.0 � 3.3 0.5 � 0.53 2.0 � 0.6 –

Acanthus pubescens

Engl.

– 0.6 � 0.6 23 � 5.3 6.8 � 3.7 0.5 � 0.5 – 1.8 � 1.2 0.1 � 0.1 –

Ornamental 1.1 � 1.1 1.5 � 1.5 0.2 � 0.2 – 0.7 � 0.4 3.1 � 3.1 83.0 � 3.0 0.6 � 0.3 –

Others 0.4 � 0.3 – 46 � 6.2 2.1 � 1.4 0.1 � 0.1 6.3 � 3.4 2.1 � 1.3 1.0 � 0.5 –

Hedgerow ground cover (%)

Vihiga 4.62 9.81 13.4 9.28 18.9 2.73 9.80 24.1 7.35

Siaya 9.76 2.39 3.52 0.24 15.4 2.52 7.61 54.2 4.30

Proportion of the farm area (%)

Vihiga 0.51 � 0.21 1.71 � 1.19 1.15 � 0.26 0.88 � 0.44 1.38 � 0.42 0.21 � 0.17 0.76 � 0.21 2.14 � 0.81 0.55 � 0.28

Siaya 1.53 � 0.52 0.39 � 0.22 0.57 � 0.24 0.03 � 0.03 2.10 � 0.48 0.49 � 0.20 1.15 � 0.55 8.09 � 1.57 0.89 � 0.44

� standard error.

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252 245

3.2. Aboveground biomass and C stocks

The stock of C in the aboveground biomass of trees wascalculated using the allometric model proposed by Satoo (cited inPonce-Hernandez, 2004). The relationships between total above-ground biomass and volume (r2 = 0.95***) and branch fresh weightand branch basal diameter (r2 ranged 0.66***–0.94***) were highlysignificant (Figs. 4 and 5). The average aboveground stock of C intrees expressed on the basis of their crown surface area, or tree Cdensity, was similar in Vihiga and Siaya farms for trees growingscattered in or around the land use units and for those growing inwindrows, but it was larger in Siaya for trees growing in woodlots(Fig. 6A). However, individual trees represented an importantaboveground C stock in Vihiga (38%) and particularly Siaya (82%).Trees growing in windrows represented barely 6 and 15% of thetotal aboveground C stock, respectively, while woodlots repre-sented most (56%) of the C stock in Vihiga and only 3% in Siaya(Fig. 6B). The aboveground tree C density based on vegetation

Table 3Biodiversity (Shannon index H) of perennial vegetation growing in or around land

use units under different land use types (LUT) in smallholder farms in western

Kenya.

District Land use types n Biodiversity (H)

Trees Hedgerows

Vihiga Homegarden 18 1.31�0.17 a 0.87�0.12 a

Food crop 89 0.46�0.06 b 0.58�0.06 a

Cash crop 15 0.40�0.16 bc 0.48�0.10 a

Pasture 4 0.44�0.44 bc 0.88�0.44 a

Woodlot 20 0.09�0.04 c 0.38�0.09 a

Mean 146 0.51�0.05 0.59�0.04

Significance (F) *** ns

Siaya Homegarden 20 1.59�0.12 a 0.60�0.09 a

Food crop 173 0.49�0.04 c 0.29�0.03 b

Cash crop 8 0.62�0.32 bc 0.06�0.06 b

Pasture 20 0.91�0.15 b 0.56�0.07 a

Woodlot 3 0.34�0.19 c 0.23�0.23 b

Mean 222 0.63�0.04 0.33�0.03

Significance (F) *** ***

*P(H0: Fobs > Fth = 0) < 0.05, **P(H0: Fobs > Fth = 0) < 0.01. � standard error.*** P(H0: Fobs > Fth = 0) < 0.001, �standard error.

ground cover was also greater in woodlots than in individual treesand windrows at both sites (P < 0.01) (not shown). The averageaboveground stock of C in tree biomass expressed on the basis ofthe area of the land use units where the trees were inventoried wasgreater in Vihiga than in Siaya (8.8 and 4.9 t C ha�1 respectively,P < 0.05), and it differed between plots under different land usetypes (P < 0.001) (Table 4). At both sites, the larger tree C stockswere found in woodlots, followed by homegardens and by food andcash crop land use units, whereas the smaller tree C stocks weremeasured in pasture plots.

The average aboveground stock of C in hedgerow biomassexpressed on the basis of vegetation ground cover, or hedgerow Cdensity, varied between hedgerow density classes, and was largerin Siaya than in Vihiga for the high-density hedgerows (P < 0.0001)(Fig. 6C). In Vihiga, most of the hedgerow C stock was distributedbetween medium-density hedgerows (43%) and high-density

Fig. 5. Allometric relationship between tree volume (diameter at breast height,

DBH, squared times its height, Ht) and total aboveground biomass using the

expression of Satoo (1955): ln(Mt) = a + b � ln(DBH2 � Ht), where Mt is total tree

aboveground biomass in kg dry matter, DBH in cm and Ht in m.

Page 9: Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder

Fig. 6. Aboveground C densities and distribution in vegetation components of western Kenya farms. (A) Average aboveground tree C density (i.e., C stock over crown surface

area) for trees growing scattered in or around land use units (individual trees), in windrows or in woodlots; (B) distribution of the total aboveground tree C stock among these

different tree formations; (C) average aboveground C density in the biomass of hedgerows grouped by vegetation densities; (D) distribution of the total aboveground

hedgerow C among these different hedgerow density classes. Error bars indicate �SE.

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252246

hedgerows (41%), while most of the hedgerow C was found in high-density hedgerows (83%) in Siaya (Fig. 6D). When hedgerow Cstocks were expressed on the basis of the area of the field aroundwhich they grew, the larger land use units of Siaya had greaterhedgerow C stocks than those of Vihiga (P < 0.0001) (Table 4). Thecalculated hedgerow C stocks were highly variable between landuse units, as a consequence of high variability in field sizes,

Table 4Aboveground C stock (Mg ha�1) in perennial vegetation growing in or around land use

District Land use types n Aboveground C

Trees

Vihiga Homegarden 18 9.6 � 2.0 b

Food crop 89 2.9 � 0.6 c

Cash crop 15 2.5 � 1.3 c

Pasture 4 0.4 � 0.4 c

Woodlot 20 36.9 � 8.3 a

Mean 146 8.8 � 1.6

Significance (F) ***

Siaya Homegarden 20 8.3 � 2.8 b

Food crop 173 2.9 � 0.6 b

Cash crop 8 6.1 � 3.0 b

Pasture 20 1.8 � 0.5 b

Woodlot 3 115.9 � 62.0 a

Mean 222 4.9 � 1.2

Significance (F) ***

� standard error.* P(H0: Fobs > Fth = 0) < 0.05.** P(H0: Fobs > Fth = 0) < 0.01.*** P(H0: Fobs > Fth = 0) < 0.001, �standard error.

hedgerow floristic composition and density. The differences in theaverage hedgerow C stock between plots under different land usewere not statistically significant, except for the hedgerowsgrowing around the woodlots in Vihiga, which stored more Cthan under the other land use types. Different types of hedgerowsstored different amounts of aboveground biomass when they weregrown at variable densities (Table 5). Hedgerow type 5, which

units under different land use types (LUT) in smallholder farms in western Kenya.

stock (t C ha�1)

Hedgerows Permanent crops Total

1.1 � 0.2 b 3.1 � 0.04 a 13.8 � 2.0 b

1.3 � 0.2 b 0.1 � 0.02 c 4.3 � 0.6 c

1.1�0.4 b 1.2 � 0.7 b 4.8 � 1.7 bc

0.6 � 0.3 b 2.1 � 1.0 b 3.0 � 1.3 c

2.6 � 1.0 a 0.0 � 0.0 c 39.4 � 8.5 a

1.4 � 0.2 0.6 � 0.1 10.8 � 1.7* *** ***

5.9 � 1.7 a 3.0 � 0.2 a 17.3 � 2.9 b

2.8 � 0.4 a 0.0 � 0.0 d 5.7 � 0.7 c

1.9 � 1.1 a 0.4 � 0.2 c 8.3 � 4.0 c

6.4 � 1.9 a 2.6 � 0.3 b 10.8 � 2.0 bc

6.7 � 3.9 a 0.0 � 0.0 d 122.6 � 59.2 a

3.4 � 0.4 0.6 � 0.1 8.9 � 1.3** *** ***

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Table 5Average biomass density (kg DM m�3) for each hedgerow type based on their floristic composition, growing at low, medium and high density.

Density class Hedgerow type

1 2 3 4 5 6 7 8 9

Low n/a 0.45 � 0.09 n/a 0.90 � 0.29 1.27 � 0.40 n/a n/a 0.67 � 1.22 n/a

Medium 1.03 � 0.16 1.19 � 0.07 0.56 � 0.03 1.63 � 0.28 2.35 � 0.14 1.06 � 0.33 0.76 � 0.29 1.45 � 0.13 0.73 � 0.05

High n/a 2.05 � 0.52 1.36 � 0.41 2.12 � 0.21 2.54 � 1.56 1.11 � 0.47 0.79 � 0.20 2.29 � 0.48 1.01 � 0.39

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252 247

consisted mainly of Euphorbia tirucalli (cf. Table 2), had the highestbiomass density while hedgerow type 7, which consisted mainly ofa mixture of ornamental species, had the lowest biomass density.

The largest part of the C stock in the aboveground biomass ofpermanent crops was essentially found in pastures (Vihiga: 71%and Siaya: 96%), with tea plantations representing the second mostimportant C stock in Vihiga (no tea plantation found in Siaya—cf.Table 1). At both sites, the average stock of C in permanent cropsexpressed on plot area basis was larger in homegardens (due to thepresence of banana stands) followed by pasture plots (P < 0.05)(Table 4). The average aboveground C in permanent crops was notsignificantly different between Vihiga and Siaya. At both sites, theaggregated stock of C in the aboveground biomass of trees,hedgerows and permanent crops was the largest in woodlotsfollowed by the homegardens (Table 4). The average stock ofaboveground C in homegardens was three times larger than in foodcrop, cash crop and pasture plots in Vihiga. In Siaya, homegardensstored more C in their aboveground biomass than the rest of theland use types and also more than in Vihiga. The average totalaboveground C stock did not differ significantly between sites.

Summarizing, the tree vegetation component represented themost important C pool of the aboveground biomass of perennialplants growing on-farm, contributing 81 and 55% of total C inVihiga and Siaya, respectively, followed by hedgerows (13 and 39%,respectively) and permanent crop stands (5 and 6%, respectively).Most of tree C was located in woodlots in Vihiga (61%) and inindividual trees growing in or around food crop plots in Siaya(57%). The homegardens represented the second C pool inimportance, with 25 and 33% of C stocks in Vihiga and Siaya,respectively. Cash crop and pasture plots stored the smallest Cstock, with 2.2 and 0.4% of the total farm C stock in Vihiga. In Siaya,cash crop plots and woodlots represented the smallest C stock,storing 2.4 and 1.5% of total C, respectively. Considering the meantotal aboveground C stocks of Table 4, and taking the average farmsizes of Vihiga (0.6 ha) and Siaya (1.4 ha), an average farm wouldstore 6.5 � 0.1 Mg C farm�1 in Vihiga and 12.4 � 0.1 Mg C farm�1 inSiaya.

3.3. On-farm biodiversity and aboveground C stocks

There was no straightforward relationship between biodiver-sity of perennial plant species growing in a certain land use unitand the stock of C in their aboveground biomass. The largeststorage of aboveground C was measured in woodlots (Table 4),which were practically monospecific (Table 3). The homegardenshad the richest diversity of perennial plant species (generallywith H > 1), and the largest average aboveground C stocksamong the rest of the land use types excluding woodlots(Fig. 7A and B). Roughly, and in plots under land use types otherthan woodlots, a wider diversity of species tended to be associatedwith somewhat larger aboveground C stocks in Vihiga. In Siaya,the land use units cropped with annual crops had less diversity ofperennial plant species growing in or around the land use unitsthan the homegardens, but could have larger aboveground Cstocks. Monospecific tree formations tended to store more C in

their aboveground biomass, while the land use units having alarge diversity of individual trees growing in or around themdid not necessarily store larger amounts of aboveground C (Fig. 7Cand D).

The trend towards larger C stocks in land use units with a widerdiversity of perennial species could also be ascribed to theiraverage area, since larger plots are more likely to have more trees,more C stored in these trees, and a also larger chance of havingdifferent tree species growing in or around them. However, therewas no direct relationship between the area of the plots and thediversity of perennial species (Fig. 8A) or between area and thetotal amount of C stored in perennial aboveground biomass(Fig. 8C). A similar pattern (or the lack of it) was observed for farm-scale biodiversity in relation to the entire area of the farms(Fig. 8B). Most farms at both sites stored less than 10 Mg of C in theaboveground biomass of their perennial vegetation, with thenotably exception of two farms in Vihiga and a few more in Siaya,which stored up to 30–40 Mg C aboveground (Fig. 8D).

3.4. Potential C gains and minimum CDM project size

There seems to be greater potential to increase the on-farmstocks of C in perennial biomass by intensifying tree biomass thanby intensifying hedgerows (Fig. 9). The amount of C stored in theaboveground biomass of trees could be potentially increased onaverage by 14 mg ha�1 in Vihiga and by 10.8 Mg ha�1 in Siaya(Table 6). While an extra 4 and 6.8 Mg C ha�1 could be stored inhedgerow aboveground biomass in Vihiga and Siaya, respectively,the total potential for C sequestration in perennial vegetation didnot differ significantly across sites (ca. 16 Mg ha�1). The calculatedextra C stocks in aboveground biomass of trees are more thandouble the current stocks (Fig. 9A). About half of the potential for Csequestration in tree biomass in Vihiga could be achieved byintensifying tree windrows, while in Siaya there is ample room tointensify woodlots (73% of the C sequestration potential) (Table 6).Intensifying perennial vegetation in the homegardens representsless than 10% of the C sequestration potential at both sites. Theminimum size for a small scale CDM A/R projects in Vihiga andSiaya would need to cover an area of 199 and 256 ha, respectively,for the scenario of increasing the stocks of C in aboveground treebiomass up to its attainable maximum (S1); 702 and 407 hathrough planting and intensifying hedgerows around plots (S2);and 171 and 175 ha through intensification of both tree andhedgerow biomass. Considering an average farm size of 0.6 ha inVihiga and 1.2 ha in Siaya, the minimum number of households tobe engaged in a small scale CDM A/R project that proposes tointensify all tree and hedgerow formations – particularly the linearplant formations around plots and farms – would be about 300 and140 in Vihiga and Siaya, respectively.

4. Discussion

The diversity of perennial plant species growing on-farm variedacross sites, vegetation components and land use units groupedaccording to the type of land use observed (Tables 1 and 3), but it

Page 11: Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder

Fig. 7. Above, aboveground stock of carbon in permanent vegetation (Mg ha�1) growing in land use units under different land use (homegardens, annual crop plots, perennial

crop plots and pasture plots) plotted against the diversity of permanent vegetation species on each filed plot, as indicated by the Shannon index for all the farms sampled in

Vihiga (A) and Siaya (B) districts of western Kenya. Below, density of C stored in aboveground tree biomass (tree C stock over tree ground cover, in kg m�2) plotted against the

diversity or trees growing in windrows, woodlots or as individual trees scattered within field crop plots, in Vihiga (C) and Siaya (D) districts.

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252248

was not related to the area of the land use units or farms (cf.: Fig. 8).Most of the species identified were found in the tree vegetationcomponent, while hedgerows were less diverse. A larger diversityof tree species was observed in Siaya, which may be the result of alonger history of agriculture in Vihiga, with consequently earlierand more intense deforestation in this densely populated area ofwestern Kenya (Crowley and Carter, 2000). On the contrary,hedgerow plant species diversity was more important in Vihigathan in Siaya. Livestock keeping represented a more importantactivity in Siaya, where hedgerows were mainly used as fences toprotect crops from marauding animals. Hedgerows planted withthis purpose consisted essentially of high-density Euphorbia

tirucalli L. and Lantana camara L. stands. In Vihiga, hedgerowswere planted with more diverse purposes, such as demarcatingland use units or farm boundaries, providing firewood, fruits orbiomass to feed (stalled) livestock or to use as soil amendments forsoil fertility management, or simply for ornamental purposes. Thisis in agreement with the observations of Lauriks et al. (1998) whoremarked that farmers in Vihiga are used to ‘manage’ theirhedgerows, whereas people in the more extensive farming systemsof Siaya do not practice any active hedgerow management.

Higher tree species diversity was particularly observed in thehomegardens, where they are normally grown for fruit production

or medicinal purposes (Figeroa-Gomez, 2007). Homegardens aretypically located around the homestead, and often surrounded byornamental hedgerows. Ornamental plant species were diverseand were essentially planted as low-density hedgerows (i.e.,demarcating internal boundaries), which may contribute toexplain the richer diversity of species observed in this densityclass in Vihiga (cf.: Table 1). Ornamental species were rarer inSiaya, where hedgerow biodiversity varied less across densityclasses or plots grouped by land use. Several other factors maydetermine the floristic composition and structure (i.e., height,density) of hedgerows. For example, the type of relations withneighbouring farms, security in land tenure or the type of livestockmanagement system (free grazing vs. stalled) may explain thepresence of different hedgerow configurations and purposes. Kindtet al. (2004) pointed out that households managed by women usedto plant more trees to delimitate their farm because they weremore likely to face land tenure problems. Thus, the diversity ofperennial plant species grown on-farm may be also affected bysocioeconomic aspects of the household—a relationship that maybe worth exploring if the intensification of perennial biomass andbiodiversity were to be promoted among smallholders.

The stocks of C in the aboveground perennial biomass measuredin the 35 farms visited (between 9 and 11 Mg C ha�1, on average)

Page 12: Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder

Fig. 8. Biodiversity and aboveground C stock in perennial vegetation growing on smallholder farms of western Kenya (Vihiga ad Siaya districts). (A) Diversity of species at plot

scale versus land use unit area; (B) diversity of species at farm scale versus farm area; (C) stock of C in aboveground biomass at plot scale; (D) idem at farm scale.

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252 249

were notably lower than the estimates for tropical agro-forestrysystems presented by Dixon (1995) and by Woomer et al. (1997).This is presumably because they were only focusing on agrofor-estry plots while this study considered a mixture of agroforestryand cropping systems. Variability in within-farm aboveground Cstocks was also very important, with C stocks ranging between 0.5and 120 Mg ha�1 for different vegetation components and land usetypes. Most of the tree C was present in woodlots in Vihiga and inscattered individual trees in Siaya. Land scarcity and populationpressure on natural resources are more intense in Vihiga, wherelarger demands for firewood are met through woodlot plantations.The relationship between the type of land use observed in a certainland use unit and the aboveground C stock in tree and hedgerowbiomass measured in or around such plot did not always exhibit aclear pattern. In other words, tree C stocks were not significantly

Fig. 9. Relationship between current aboveground C stocks in trees (A) and hedgerows (

difference between the current C stock measured and the C stock corresponding to the t

type. The shadowed area below the hypothetical upper boundary line illustrates the C se

For clarity, one point has been omitted from panel A (Siaya: current stock 12.7 Mg ha�1

panel B.

different between food crop, cash crop and pasture plots, while inSiaya hedgerow C stocks were not significantly different betweenall land use types except woodlot.

Most C inventories at regional scale are based on identification ofland use types or land cover (e.g., Achard et al., 2004). Such anapproach would certainly underestimate variability in C stockswithin farms and land use types. Instead, C inventories based onidentification of vegetation forms and management would bemore reliable to estimate aboveground C stocks. Identification ofwoodlots, windrows and individual trees, high-, medium- and low-density hedgerows, and tea, grass and banana plantation could bedone using aerial photographs or satellite imageries. Althoughdifferences in aboveground C stocks between farms were notanalysed in this study, we expect that the socio-economic diversityof households will further influence the size of on-farm C stocks, not

B) ad their respective potential for C sequestration (delta C stock), calculated as the

hird quartile in the distribution of C stocks for each vegetation component/land use

questration potential in each vegetation component, irrespective of site differences.

; delta C 47.7 Mg ha�1), which corresponds to the point above the boundary line in

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Table 6C sequestration potential in the aboveground biomass of perennial vegetation by intensifying the tree component (SI), the hedgerow component (S2) or both (S3).

Component Scenario 1: Intensifying tree biomass Scenario 2: Intensifying hedgerow

biomass

Scenario 3:

S1 + S2

Land use Individual trees Windrows Woodlots High Medium Low

Homegardens Food crops Cash crops Pastures

Number of plots

Vihiga 18 89 14 3 16 20 98 112 84 144

Siaya 20 171 8 20 19 3 178 77 39 222

Maximuma C stock attainable (Mg C ha�1)

Vihiga 11.7 3.8 1.4 1.1 32.7 54.4 22.9 14.5 7.9 nr

Siaya 7.4 3.4 5.3 2.3 30.5 230.0 34.3 18.1 9.5 nr

Average aboveground C sequestered (Mg C ha-1)

Vihiga 5.6 � 1.06 2.6 � 0.16 0.7 � 0.16 0.7 � 0.37 31.4 � 0.57 22.7 � 3.61 7.8 � 0.03 4.6 � 0.02 2.7 � 0.02 16.3 � 1.58

Siaya 2.9 � 0.58 2.1 � 0.10 3.5 � 0.89 1.2 � 0.22 29.2 � 0.83 109.8 � 56.86 11.0 � 0.02 4.4 � 0.03 2.1 � 0.04 15.9 � 0.98

Significance (F) * ** ns * ns ns *** ns ns ns

Proportion of total C sequestered (%)

Vihiga 8.8 4.1 1.1 1.2 49.2 35.7 51.5 30.8 17.7

Siaya 2.0 1.4 2.3 0.8 19.7 73.8 62.5 25.4 12.1

Average total C sequestered (Mg C ha�1)

Vihiga 14.0 � 1.54 4.0 � 0.46 16.3 � 1.58

Siaya 10.8 � 2.35 6.8 � 1.06 15.9 � 0.98

Significance (F) ns ns ns

Minimum project size (ha)

Vihiga 199 702 171

Siaya 256 407 175

a Maximum C stocks correspond to the third quartile in their distribution.* P(H0: Fobs > Fth = 0) < 0.05.** P(H0: Fobs > Fth = 0) < 0.01.*** P(H0: Fobs > Fth = 0) < 0.001, �standard error.

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252250

only through influencing land size (e.g., Tschakert and Tappan,2004), but also determining the feasibility, profitability andacceptability of project activities that may affect participation inCDM A/R projects (Franzel, 1999).

The biophysical potential to increase C stocks on-farmdepended on land availability and use, vegetation componentsand current aboveground C stocks. In Vihiga, current C stocks werelarger in woodlots than in windrows and individual trees, but thepotential to increase C stock was greater in windrows since land isbarely available to extend woodlots or planting more individualtrees. Without compromising food security, the potential toincrease C stocks in aboveground biomass was calculated at16 Mg ha�1 at both sites. While Houghton et al. (1993) estimated aC sequestration potential in aboveground biomass throughagroforestry interventions of 59 Mg C ha�1 for sub-Saharan Africa,the estimates of C sequestration presented by Unruh et al. (1993)were more in agreement with the calculations presented inTable 6. On the other hand, Woomer et al. (1997) argued that66 Mg C ha�1 could be sequestered both above- and belowgroundthrough nutrient recapitalization and agroforestry. Belowground Cstocks include C in organic litter, soil organic C (SOC) and C in rootbiomass (Hairiah et al., 2001), and they represent the major C poolsin agro-ecosystems (e.g., 80% of total C stocks in agroforestrysystems—Dixon, 1995). Estimating belowground C stocks, parti-cularly the root C pool, is time-consuming and may be subject to arelatively high degree of uncertainty (Manlay et al., 2002). Earlierapproaches have estimated belowground C stocks using generalcoefficients to estimate root:shoot biomass ratios (IPCC, 2007) andby defining ranges of SOC contents per soil type (GEFSOC, 2005).

Roughly, using a mean root-shoot biomass ratio for this tropicallatitudinal zone of 0.24 (Cairns et al., 1997), the average stocks of Cin root biomass would be in the order of 3.6, 1.0, 1.2 Mg C ha�1 inhomegardens, food crop fields and woodlots, respectively. Measu-rements of SOC and bulk density in fields under different land use

in our study area (Tittonell, 2007) allow us to estimate soil C stocksranging between 24 and 56 Mg C ha�1—for the upper 0.3 m of thesoil. Thus, there is much more to gain in terms of C sequestration inthe belowground C components.

Investments in afforestation/reforestation (A/R) within theCDM framework are proposed as an option for adaptation toclimate change, mitigation of atmospheric C and sustainable ruraldevelopment. Additionally, a larger tree cover would lead todecrease soil degradation. In our study area, the minimum numberof participants to be involved in small scale CDM A/R, just to coverthe transaction costs, with a sequestration potential of 16 Mg ha�1

(without compromising food production) was calculated in 170households. Furthermore, to produce economical incentives toencourage farmers to participate and to cover the costs ofimplementation, the CDM A/R project should sequester more than16 Mg C ha�1. If the potential for C sequestration belowground wasalso considered, the number of participants would be considerablysmaller, but yet large enough to compromise the feasibility of theproject, implying high implementation and monitoring costs. Thetransaction costs used in our calculations (Locatelli and Pedroni,2006) were conservatively low, and did not include implementa-tion costs or potential financial compensation to farmers. Thestudy of Woomer et al. (1997) estimated C sequestration costs ofUSD 47 Mg C�1 in smallholder farms of East African highlands,while financial compensations through the C market oscillatearound USD 10 Mg CO2

�1.Four alternatives can be identified to decrease the number of

participants needed to implement a potential AR/CDM initiative:(1) increasing C stock more than that which was proposed in thisstudy, which would necessarily compete with food security, (2)reducing transaction costs (i.e., decreasing the cost of technolo-gies), or (3) increasing C market prices, e.g., by valuing otherservices related to C sequestration such as sustainable develop-ment or biodiversity. The last proposition consists of including soil

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M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252 251

conservation, particularly in degraded areas such as westernKenya, that have great potential for soil C sequestration (Vagenet al., 2004).

Within the framework of the CDM it seems more economicallyattractive to plant mono-specific woodlots rather than multi-usagetree systems in the context of C trade. Species functionalcharacteristics strongly influence ecosystem properties and, influctuating environments such as tropical agroecosystems, long-term productivity may increase with species richness due to anincreased capacity to buffer physical disturbances (Yachi andLoreau, 1999). Due to such multi-functionality, C that issequestered within diverse and stable agroecosystems should betherefore better valued than C sequestered in poor-biodiversity(e.g., monoclonal eucalyptus plantation) systems. However, themechanisms that were set up to reach the commitment goals of theKyoto protocol of the United Nations Framework Convention onClimate Change (UNFCC) consider only afforestation and refor-estation activities as eligible under the CDM (UNFCCC, 2004). Itsimplementation requires that the impact of C sequestration onbiodiversity be taken into account, but financing is not an explicitfunction of biodiversity. C sequestration by intensifying agro-forestry systems such as those found in homegardens represents awin–win strategy that could increase both biodiversity, C storageand contribute to decrease soil degradation and householdnutritional security and diversity of farm income. Intensivehomegardens may be also easier to promote among farmers, asthey could be targeted to only certain plots within a farm.

5. Conclusion

A wide diversity of perennial plant species was recordedgrowing on smallholder farms of western Kenya, particularly oftrees growing in homegardens or scattered in or around the foodand cash crop land use units of the farms. These trees contributedto the aboveground C storage, but to a lesser degree than thecontribution of mono-specific woodlots dominated by Eucalyptus

saligna. Biodiversity or perennial vegetation and aboveground Cstocks did not increase with the land use unit or farm areas, exceptfor a weak positive trend indicating greater C stocks in the largerfarms in Siaya. There was no direct relationship between thediversity of perennial plant species growing on-farm and theaboveground C stocks, and thus biodiversity can be seen as anindependent, additive agro-ecosystem function. C sequestrationprojects that contribute to enhance biodiversity should beconsidered as more ethical and stable in the long-term thanconventional afforestation/reforestation projects that do notconsider biodiversity or ecosystem function. The implementingCDM A/R projects in densely populated regions of sub-SaharanAfrica, such as western Kenya is seriously limited by the poorpotential for C sequestration without compromising food produc-tion on-farm. Large areas of farmland are necessary to at leastcover the transaction costs of implementing an CDM A/R project,and these translate into large numbers of smallholder farmers to beinvolved, joining their hands in adopting C sequestration practicesover a minimum time period of 20 years. In order to increase thepotential implementation of A/R CDM projects in western Kenya,funding would have to better consider other environmentalservices and do not limit their actions to afforestation andreforestation activities. An extra limitation that face the imple-mentation of CDM A/R projects over large areas is the account-ability of C in the agro-ecosystem. The detailed inventorying usedin this study revealed important information in terms of variabilityof C stocks with farms and land use systems that cannot be ignored.However, this type of inventories are impractical at regional scale.But the information collected here may contribute to refining

broader scale methods based on remote sensing. Further studiesaiming at analyzing the feasibility of C sequestration in farmingsystems of western Kenya should focus on the long-term resilienceof C storage and biodiversity, on the potential for belowground Csequestration, and on social factors that may influence adoption ofC sequestration practices.

Acknowledgements

We thank the European Union for funding this research throughthe Africa NUANCES Project (Contract No. INCO-CT-2004-003729),and the Tropical Soil and Biology and Fertility of the InternationalCentre for Tropical Agriculture (TSBF-CIAT) for technical andlogistical support. This work received financial support from IRD,ICRAF and AgroParisTech-ENGREF. The authors wish to thank JohnMukalama, Isaac Ekise, Laban Nyambega, Guideon Omito, StefenNjoroge, Caroline Awlor, Naman Obuyi, Benson Aluko and ElliotOwino for their assistance.

References

Achard, F., Eva, H.D., Mayaux, P., Stibig, H.-J., Belward, A., 2004. Improved estimatesof net carbon emissions from land cover change in the tropics for the 1990s.Global Biogeochimical Cycles 18.

AddinSoft, 2003. User’s Manual. Addinsoft, Inc., Brooklyn, NY, USA.Albrecht, A., Serigne, T.K., 2003. Carbon sequestration in tropical agroforestry

systems. Agriculture, Ecosystems and Environment 99, 15–27.Bernoux, M., Feller, C., Cerri, C.C., Eschenbrenner, V., Cerri, C.E.P., 2006. Soil carbon

sequestration. In: Roose, E., Lal, R., Feller, C., Barthes, B., Stewart, R. (Eds.), Soilerosion and carbon dynamics. CRC Press, Boca Raton, pp. 13–22.

Bradley, P.N., 1988. Survey of woody biomass on farms in Western Kenya. Ambio 17,40–48.

Brookfield, H., Stocking, M., Brookfield, M., 2002. Guidelines on agrodiversityassessment. In: Brookfield, H., Padoch, C., Parsons, H., Stocking, M. (Eds.),Cultivating Biodiversity: Understanding, Analysing and Using AgriculturalDiversity. ITDG Publishing, London, UK, pp. 41–56.

Brown, S., Gillespie, A.J.R., Lugo, A.E., 1989. Biomass estimation methods for tropicalforest with application to forest inventory data. Forest Science 35, 881–902.

Bunker, D.E., DeClerck, F., Bradford, J.C., Colwell, R.K., Perfecto, I., Phillips, O.L.,Sankaran, M., Naeem, S., 2005. Species loss and aboveground carbon storage in atropical forest. Science 310, 1029–1031.

Cairns, M.A., Brown, S., Helmer, E.H., Baumgardner, G.A., 1997. Root biomassallocation in the world’s upland forests. Oecologia 111, 1–11.

Crowley, E.L., Carter, S.E., 2000. Agrarian change and the changing relationshipsbetween toil and soil in Maragoli, Western Kenya (1900–1994). Human Ecology28, 383–414.

Dixon, R.K., 1995. Agroforestry systems: sources or sinks of greenhouse gases?Agroforestry Systems 31, 99–116.

ESRI, 1997. ARC/VIEW GIS V. 3.2. The User’s Guides. Esri France.Figeroa-Gomez, B.M., 2007. The Contribution of Traditional Vegetables to House-

hold Nutritional Security in Two Communities of Vihiga and Migori Districts.Wageningen University, Kenya.

Franzel, S., 1999. Socioeconomic factors affecting the adoption potential ofimproved fallows in Africa. Agroforestry Systems 47, 305–321.

GEFSOC, 2005. Assessment of soil organic carbon stocks and change at nationalscale. In: Technical Report of GEF Co-financed Project No. GFL-2740-02-4381.University of Reading, UK, GEF Implementing Agency, The United NationsEnvironment Programme.

GOK, 2003. Kenya: Interim Poverty reduction Strategy Paper 2000–2003. WorldBank.

Hairiah, K., Sitompul, S.M., Van Noordwijk, M., Palm, C., 2001. Methods for SamplingCarbon Stocks Above and Below Ground. SEA Regional Research Programme.International Centre For Research in Agroforestry, Bogor, Indonesia.

Houghton, J., Unruh, J.D., Lefebvre, P., 1993. Current land use in the tropics and itspotential for sequestering carbon. Global Biogeochimical Cycles 7, 305–320.

IPCC, 2003. Good Practice Guidance for Land Use, Land-Use Change and Forestry.IPCC National Greenhouse Gas Inventories Programme, Kanagawa, Japan.

IPCC, 2007. 2006 IPCC Guidelines for National Greenhouse Gas Inventories.Jaetzold, R., Schmidt, H., 1982. Farm Management Handbook of Kenya, vol. II, part A.

MALD, Western Kenya.Kindt, R., Simons, A., Van Damme, P., 2004. Do farm characteristics explain differ-

ences in tree species diversity among western Kenya? Agroforestry Systems 63,63–74.

Lauriks, R., De Wulf, R., Carter, S.E., Niang, A., 1998. A methodology for description ofborder hedges and the analysis of variables influencing their distribution: a casestudy in western Kenya. Agroforestry Systems 44, 69–87.

Locatelli, B., Pedroni, L., 2006. Will simplified modalities and procedures make moresmall-scale forestry projects viable under the Clean Development Mechanism?Mitigation and Adaptation Strategies for Global Change 11, 621–643.

Page 15: Biodiversity, carbon stocks and sequestration potential in aboveground biomass in smallholder

M. Henry et al. / Agriculture, Ecosystems and Environment 129 (2009) 238–252252

Magurran, A.E., 1988. Ecological Diversity and its Measurement. Princeton Uni-versity Press, Princeton.

Manlay, R.J., Kaire, M., Masse, D., Chotte, J.-L., Ciornei, G., Floret, C., 2002. Carbon,nitrogen and phosphorus allocation in agro-ecosystems of a West Africansavanna. I. The plant component under semi-permanent cultivation. Agricul-ture, Ecosystems and Environment 88, 215–232.

Montagnini, F., Nair, P.K.R., 2004. Carbon sequestration: underexploited environ-mental benefit of agroforestry systems. Agroforestry Systems 61, 281–295.

Ng’etich, W.K., Stephens, W., 2001. Response of tea to environment in Kenya. 1.Genotype � environment interactions foe total dry matter production andyield. Experimental Agriculture 37, 333–342.

Ponce-Hernandez, R., 2004. Assessing Carbon Stocks and Modelling Win–winScenarios of Carbon Sequestration through Land-use Changes. Food and Agri-culture Organization of the United Nations, Rome, Italy.

Satoo, T., 1955. Materials for the study of growth in stands. Tokyo UniversityForestry Bulletin 48, 69–123 (in Japanese, English summary).

Schulze, E.-D., 2006. Biological control of the terrestrial carbon sink. Biogeosciences3, 147–166.

Shepherd, K.D., Soule, M.J., 1998. Soil fertility management in west Kenya: dynamicsimulation of productivity, profitability and sustainability at differentresource endowment levels. Agriculture, Ecosystems and Environment 71,131–145.

Tittonell, P., Vanlauwe, B., Leffelaar, P.A., Giller, K.E., 2005. Exploring diversity in soilfertility management of smallholder farms in Western Kenya. II. Within-farm

variability in resource allocation, nutrient flows and soil fertility status. Agri-culture, Ecosystems and Environment 110, 166–184.

Tittonell, P., 2007. Msimu wa Kupanda—Targeting Resources for Integrated soilFertility Management within Diverse, Heterogenous and Dynamic FarmingSystems of East Africa. Wageningen University.

Tschakert, P., Tappan, G., 2004. The social context of carbon sequestration: con-siderations from a multi-scale environmental history of the Old Peanut Basin ofSenegal. Journal of Arid Environments 59, 535–564.

UNFCCC. 2004. Modalities and procedures for afforestation and reforestationproject activities under the clean development mechanism in the first commit-ment period of the Kyoto Protocol. p. 13–31. In. Report of the Conference of theParties on its ninth session, held at Milan from 1 to 12 December 2003,Addendum, Decision 19/CP.9, Document FCCC/CP/2003/6/Add.2, UnitedNations Office at Geneva, Geneva.

Unruh, J.D., Houghton, J., Lefebvre, P.A., 1993. Carbon storage in agroforestry: anestimate for sub-Saharan Africa. Climate Research 3, 39–52.

Vagen, T.G., Lal, R., Singh, B.R., 2004. Soil carbon sequestration in sub-SaharanAfrica: a review. Land Degradation and Development 16, 54–71.

Woomer, P.L., Palm, C.A., Qureshi, J.N., Kotto-Same, J., 1997. Carbon sequestrationand organic resource management in African smallholder agriculture. In: Lal,R., Kimble, J.M., Follett, R.F., Stewart, B.A. (Eds.), Management of CarbonSequestration in Soil. CRC Press Inc., Boca Raton, pp. 153–173.

Yachi, S., Loreau, M., 1999. Biodiversity and ecosystem productivity in a fluctuatingenvironment: the insurance hypothesis. Ecology 96, 1463–1468.


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