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

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

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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 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 b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 19 March 2008 Received in revised form 14 August 2008 Accepted 5 September 2008 Available online 14 November 2008

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 farm1 in Vihiga and 12.4  0.1 Mg C farm1 in Siaya. At both sites, the C sequestration potential in perennial aboveground biomass was estimated at ca. 16 Mg C ha1. 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.

Keywords: Sub-Saharan Africa Land use Trees on farm Tree allometry Agroforestry Clean Development Mechanism

1. Introduction Maintenance of (agro-)biodiversity and carbon sequestration through 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 ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.agee.2008.09.006

complementary environmental service functions of agroecosystems. 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

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

impacts on belowground C sequestration (e.g., through litter fall, root exudation and turnover or soil erosion control). Agroecosystems with a broader diversity of plant species, living forms and production activities may achieve higher levels of productivity in the long-term while maintaining larger and more stable C stocks (Yachi and Loreau, 1999). Biodiversity in agroecosystems may also contribute to diversification of products and diets, and to income stability (Brookfield et al., 2002)—a win–win alternative for smallholder farmers in Sub-Saharan Africa (SSA), who may eventually also benefit from C payment schemes. At present, C sequestration is valued as a function of credit emission reductions (CERs), based on the difference between the amount of C stored in scenario projects and the baseline, current amount of C stored in the system (UNFCCC, 2004). Here, we define C sequestration as the amount of C that can be additionally stored in an agroecosystem (Bernoux et al., 2006). Agro-forestry systems stand larger chances to sequester C in the long-term than annual cropping systems, adding aboveground C storage capacity through a broader diversity of living forms, including fruit or timber trees on-farm and/or perennial crops, plus potential ‘fertiliser’ and ‘fodder’ trees. Worldwide, the average amount of C stored in the aboveground compartments of agroforestry systems was estimated to range between 40 and 150 t C ha1 (IPCC, 2007). Albrecht and Serigne (2003) estimated a potential C sequestration in tropical agro-forestry systems of 95 t C ha1 (varying widely between 12 and 228 t C ha1). Variability in C sequestration and biodiversity can be high within complex agroecosystems, depending on factors such as vegetation age, structure, management practices, land uses and landscape (Montagnini and Nair, 2004). In areas of the Tropics characterized by agro-forestry systems with dense human population such as western Kenya, and where smallholder subsistence agriculture predominates, the expansion and intensification of integrated agro-forestry systems may be an alternative to increase biodiversity and contribute to C sequestration. Although afforestation is probably one of the quickest means of increasing aboveground C stocks, increasing the number of trees on farms that have an average area of around 1 ha without compromising food production is a real challenge. Our region of study in western Kenya is characterized by high agricultural potential that attracted large human settlement in the past, resulting in extensive land fragmentation and degradation (Crowley and Carter, 2000). Previous studies in this region (Bradley, 1988) analysed the capacity of such smallholder systems to produce wood biomass identifying five vegetation types – woodlots, windrows, individual trees, hedgerows and riparian vegetation – and developed allometric regressions for their biomass assessment. Lauriks et al. (1998) conducted a hedgerow inventory and proposed a typology of hedgerows based on their floristic composition and biomass density. Other, more recent studies measured tree biodiversity in relation to farm characteristics (e.g., Kindt et al., 2004). However, formal studies linking aboveground C storage capacity and biodiversity in these integrated smallholder agro-forestry systems are lacking. To assess the capacity of smallholder farming systems to store C in their aboveground biomass it is necessary to analyse (i) their current status in terms of biomass structure, diversity and functioning, (ii) the factors driving variability in aboveground C stocks across farms and (iii) the potential for increasing aboveground C stocks through changes in the structure of the agroecosystem and/or land use. This would allow identifying ‘niches’ for tree intensification and C sequestration within the complexity of smallholder systems. Our specific objective was to assess current biodiversity of permanent vegetation and aboveground C stocks in representative smallholder farms of western Kenya.

239

Through estimating C sequestration potential under different scenarios of intensification we explored the feasibility of implementing Clean Development Mechanism (CDM) projects involving smallholder farmers in the region. 2. Materials and methods 2.1. Study sites The study was conducted in Vihiga and Siaya districts of western Kenya (Fig. 1A). These districts were selected in a project covering a broader area of Kenya and Uganda, to represent areas of the East African highlands differing in demography, agroecology and access to markets (Tittonell, 2007). Vihiga and Siaya districts cover an area of 570 and 1521 km2, respectively, and represent to a large extent the variability found in western Kenya, with average altitudes of 1600 and 1200 m.a.s.l., and annual rainfall of 1800 and 1400 mm following a bimodal distribution (i.e., the long and the short rains) that allows two cropping seasons per year. About 78 and 86% of the area of Vihiga and Siaya districts correspond to agricultural land (GEFSOC, 2005). Population densities range between 300 and 1200 inhabitants km2 (GOK, 2003), with average farm sizes <1 ha in Vihiga and between 1 and 2 ha in Siaya. Dominant soil types include Acrisols, Ferralsols and Nitisols (Jaetzold and Schmidt, 1982) and are characterised by a good physical structure but low nutrient reserves due to prolonged weathering, and more recently by intense agricultural use (Shepherd and Soule, 1998). Individual farms are broadly oriented along typical toposequences, with the homestead located upslope nearest to the road network and cropping activities located mostly on the slopes towards the waterways (Fig. 2) (Tittonell et al., 2005). The area of individual farms may be highly fragmented into land use units that range between 0.033 and 0.7 ha in area. Smallholder farms in the area can be considered agro-forestry systems in the sense that they integrate crop-livestock activities and on-farm wood production on small areas of land. 2.1.1. Land use types (LUT) During our farm visits land use units were classified according to the dominant land use type observed into homegardens (HG), food-crop plots (FP), cash-crop plots (CP), pasture plots (PP) and woodlots (WL) (Fig. 2). HG are the plots around the homestead where farmers grow vegetables for the household, receiving most organic nutrient resources in the form of animal manure, compost and kitchen waste. FP are essentially a subsistence-oriented land use type, cropped with maize, sorghum and beans. CP are typically those in which tea (Camellia sinensis L.) is grown; however, given the increasing market orientation of crops such as banana (Musa sp.) and Napier grass (Pennisetum purpureum Schum.), these plots were also counted as CP. Plots located in valley bottoms were mainly allocated to maize or vegetables and to eucalyptus (Eucalyptus saligna S.M.) woodlots in Vihiga, while these were used for pastures, fallows, or vegetable cultivation in Siaya. 2.2. Farm sampling Two localities were selected in each district, Emuhaia and Ebusiloli villages in Vihiga district, and Nyabeda and Nyalungunga villages in Siaya district (Fig. 1B), and within each of these a Yframed sampling scheme was randomly located to represent soil/ landscape variability (Fig. 1C). One geo-referenced point was chosen in each village to locate the centre of the Y-frame with the aid of a GPS. Each Y-frame had a radius of 900 m and included 10 farms along 3 transects diverting 120 degrees from each other. A

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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.

farm in the center of the frame and three farms on each transect at 100, 300 and 900 m from the centre were selected for detailed characterization and quantification of C stocks (Fig. 1D). Eventually, 35 farms out of the 40 farms selected in the four villages (8 in Ebusiloli, 8 in Emusutswi, 10 in Nyalugunga, and 9 in Nyabeda) were included in the assessments, since five of the randomly selected farmers were not willing to participate. 2.3. Vegetation components and spatialisation Permanent vegetation was divided into three main components: trees, hedgerows, and permanent crops. Food crops and Napier grass stands were not considered, since their aboveground biomass was removed after each cropping season. For the tree component, three different types of tree formations were identified: individual trees, windrows and woodlots. Individual trees were isolated trees, planted in or around the cropland and/or

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. Woodlots were defined as small, mono-specific areas of cultivated trees. The second vegetation component, hedgerow, was defined as a linear, perennial, and homogenous vegetation component in terms of floristic composition and management, containing trees and other vegetation types. Based on the structure of the hedgerow, they were classified as high-, medium-, or low-density hedgerows as explained below. For the third component, permanent crops, this study considered the three major ones observed in the region: Kikuyu grass (mostly Brachiaria sp.), Tea and Banana stands. All trees, hedgerows and permanent crop stands were geo-referenced and mapped with Garmin GPS Map76 (3 m) (see example in Fig. 3). Digital spatialisation was done with Arcview GIS 3.2 (ESRI, 1997). Individual trees were represented as points, windrows and hedgerows as lines, and woodlots, permanent crop stands and field plots as polygons. 2.4. Diversity of perennial vegetation on-farm

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).

Species biodiversity was determined for the permanent vegetation 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 tree species inventory in farms using local and/or scientific names (e.g., Kindt et al., 2004). Biodiversity was measured for individual land use units j, which delimited by fix boundaries, calculating an index based on the number of species and their abundance. Out of a wide range of biodiversity indexes with different calculation methods available in literature (Magurran, 1988), we chose to use the Shannon index (H), which has been proposed to estimate biodiversity in carbon sequestration projects (Ponce-Hernandez,

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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.

2004). Shannon index was calculated by multiplying the abundance of a species (pi) by the logarithm of this number: Hj ¼ 

m X

pi j lnð pi j Þ

(1)

i¼1

where H is the Shannon index for vegetation component or formation or land use type j depending on the scale. pi j ¼

ni j Nj

(2)

where ni is the number of subjects from the species i and N the total number of subjects within the plot j.Hedgerow biodiversity was also assessed with the Shannon index but rather than identifying the species of each stem, a simplified methodology to estimate frequency of plant species was used, based on calibration of visual observations. First, the exact species of each hedgerow stem was identified for 20 hedgerows representing the range of plant species and hedgerow density classes observed, and the species composition calculated for each of them. Then, the floristic composition of each hedgerow estimated through visual observation, and expressed as a percentage of each of the species and the total number of these, was calibrated against their exact species composition. Since field measurements provided data for the proportion of species i within each hedgerow h, noted Pih, the proportion of species i for each plot j, noted Pij, was estimated based on two assumptions: (1) the number of subjects of species i within the hedgerow h was proportional of hedgerow length (Lh) and (2) proportional to hedgerow density class (dh): there were three times more subjects within a high density than a low-density hedgerow, and two times more within a medium than a low-density hedgerow. Thus, the proportion of species i around plot j was calculated as: Pn Pn P  Lh  dh P i j ¼ h¼1 Pni¼1 ih (3) h¼1 Lh  dh

2.5. Development of allometric relationships Allometric relationships for trees are generally based on measurement of the diameter at breast height (DBH) (Brown et al., 1989). To develop these relationships, 26 trees were selected from various species and different functions (i.e., timber trees, fruit trees, etc.), growing as individual trees, in windrows or woodlots, in or around plots under different LUT, and with DBH ranging 5–32 cm. For each of these trees, DBH, height and crown diameter were measured. DBH was measured with a calliper. The height of trees lower than 6 m was measured with a levelling staff and the height of those higher than 6 m with a Suunto dendrometer. Tree ground cover was based on measurement of the projection of the crown diameter with a measuring tape. Trees were logged according to local practice, leaving a stump 0.1–0.9 m high. Aboveground biomass of the trunk (Mtr), branches (Mb), and leaves (Ml) was calculated from the measured fresh biomass. Fresh biomass was directly measured with a spring balance (100 g). The biomass of previously pruned branches (Mp) was derived from linear regression models that were developed with the data from the destructive tree biomass assessments. Such models linked the basal diameter of the pruned branches with their fresh biomass for each of the species considered (Fig. 4). The biomass of the stump (Ms) was calculated from its volume and wood density estimates based on 5 samples taken in the middle of the trunk. The fresh biomass of these wood samples was measured with an electronic balance (1 g) and their volume by measuring the volume of water displaced (10 cm3) after immersion in water. The moisture content of the woody biomass was assessed on 26 samples cut into small pieces (5 cm  2 cm), oven-dried at 106 8C (5 8C), and weighed. The total leaf dry biomass was calculated from the fresh biomass, corrected with their moisture content. The total tree dry mass (Mt) was calculated based on fresh biomass and moisture content of the different plant parts, and then aggregated as: Mt ¼

1 1  ðM tr þ M b þ M p þ Ms Þ þ M 1 þ sw 1 þ sl l

where s w is wood moisture and sl is leaf moisture content.

(4)

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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.

Allometric relationships to calculate Mt were obtained using the generic formula of Satoo (1955), cited in Ponce-Hernandez (2004): ln M t ¼ a þ 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 and density class (low, medium and high). Hedgerow types based on floristic composition were identified using hierarchical cluster analysis of hedgerow composition data (n = 440), as explained in Lauriks et al. (1998). To measure the specific biomass density (Kh) of different hedgerows, 3 samples per hedgerow type and density class were selected. Each sample consisted of all the biomass corresponding to two meters of hedgerow cut at the soil surface.

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

These hedgerow biomass samples were chopped into small pieces and weighed. The moisture content was measured on sub-samples, dried at 65 8C, and the dry biomass (mh) calculated. Kh was calculated as: Kh ¼

mdh

vh

(6)

where vh is the volume of the hedgerow sample calculated from measurements of its width and height prior to sampling. Biomass of tea plantations, banana mats, and grassland stands were estimated from specific coefficients found in literature. The aboveground biomass of grassland (Mg) was estimated assuming a density of 6.2 t ha1 (75%) proposed by IPCC (2003) for tropical grasslands. According to Ng’etich and Stephens (2001), the aboveground biomass of a 36-months old tea plantation in Kericho, Kenya, ranges between 17.6 and 23 t ha1 for different clones and experimental sites. In this study, biomass for tea plantation was based on a mean value (Mte) of 20.3 t ha1. Aboveground biomass of bananas (Mba) was estimated using the following empirical model (Arifin, 2001 cited in Hairiah et al., 2001): M ba 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 35 farms visited, and a 30 m2 sub-plot was inventoried in each of the woodlots present on these farms. The inventories included all non-destructive measurements necessary to use the allometric models introduced (e.g., DBH, height) plus the floristic composition of the different vegetation components. Hedgerow biomass (Mh) was estimated by multiplying hedgerow-specific biomass density (Kh) times hedgerow volume (Vh) for the 440 hedgerows that were measured. Vh was assessed from length, width and height measurements. Lengths shorter than 20 m were measured with a tape measure while those exceeding 20 m were measured with a Garmin GPS Map76 (3 m). Hedgerow biomass was obtained from the following equation: Mh ¼ V h  K h

(8)

The estimation of banana biomass was based on measurements of DBH for all banana stems and the biomass allometric relationship introduced earlier (Eq. (7)). Tea and grass biomass were calculated from the area of the plots and the assumed average biomass densities indicated above. The area of all plots was measured with a Garmin GPS Map76 (3 m). The biomass of trees, hedgerows and permanent crops was aggregated at plot and farm scale to calculate total biomass per plot, per land use type (LUT), per vegetation component, per tree management type, per hedgerow floristic type and density class, and per individual farm. Aboveground C stock for trees (Ct), hedgerows (Ch) and permanent crops (Cp) were obtained from conversion of aboveground biomass (Mt, Mp and Mh) into C using a conversion coefficient of 0.5 kg C kg DM1 (IPCC, 2007). C stocks were expressed as: (i) aboveground C stock on an area basis (Mg ha1), dividing the stock of C of a certain vegetation component by the area of the plot that contained it; (ii) aboveground C density (kg m2), dividing the stock of C of a certain vegetation component by its ground cover (i.e., vertical projection).

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2.7. A simple estimation of potential C sequestration for a small-scale CDM project Scenarios were analysed in which the aboveground C stocks were increased without affecting the distribution of LUT in a way that could compromise food security, basically by intensifying the tree and hedgerow vegetation components. The scenarios consisted of simply increasing aboveground C stock (ACS) assuming that land uses or management types would reach the maximum C stock (Cmax) that was measured across all farms in this study. Since the maximum ACS that was measured is case specific, and in order to obtain more realistic estimates of maximum C stock that could be achieved in most farms, Cmax was assumed to be the third quartile in the distribution of the actual ACS measurements. Maximum aboveground C stocks (MACS) were then obtained by multiplying Cmax times land area. The potential for C sequestration was calculated as the difference between Cmax and current C stocks. Based on this potential C sequestration, the minimum size necessary to implement a CDM project (i.e., the minimum area, the minimum number of households to be involved) was calculated. A major limitation of this simple approach, however, was the uncertainty about estimates of the costs of project implementation and the financial compensations that would be due to farmers participating in a CDM project. Conservatively, the minimum CDM project size was assumed to correspond to the area that would capture sufficient CO2 to cover the transaction costs of a CDM project, as estimated by Locatelli and Pedroni (2006). These authors 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 C market price of USD 10/t CO2, a crediting period of 20 years, and without considering project discount rates. To be viable, a CDM Afforestation/Reforestation project would have to store a minimum of 10,200 t of CO2 or approximately 2800 t C. The minimum number of participants was estimated by assuming average farm sizes of 0.6 ha in Vihiga and 1.4 in Siaya. Three scenarios were analysed. In the first scenario (S1), the stock of C in tree biomass was increased to the MACS that was measured in this study for individual trees in homegardens, food crop, cash crop and pastures land use units, and for trees in windrows and woodlots. In the second scenario (S2), the stock of C in hedgerows was increased to its maximum value as measured in high-, medium- and low-density hedgerows. In the third scenario (S3) both tree and hedgerow C stocks were increased up to their MACS simultaneously. 2.8. Data analysis Statistical analysis was implemented with XLSTAT Pro 7.2 (AddinSoft, 2003). The typology of hedgerow floristic composition was obtained using hierarchical cluster analysis (Lauriks et al., 1998). Analysis of variance (ANOVA) was done to assess the differences in biodiversity and C stocks between land use types, and in C sequestration potential between sites, with means comparisons using the least square difference. Pearson statistical tests were performed to test correlations between aboveground C stocks and biodiversity for different land use and management types. 3. Results 3.1. Plant diversity on farms The diversity of perennial plant species varied widely between locations, vegetation components, tree formations and land use

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Table 1 Biodiversity 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 Windrow Woodlot Mean Significance (F)

119 15 17 146

0.74  0.12 0.36  0.10 0.01  0.01 0.50  0.05

Euc: Euc: Euc: Euc:

20%, Psi:11%, Per: 9%, Man: 7% 47%, Cup: 12% 99% 93%, Cup: 2%

Low density Medium density High density Mean Significance (F)

56 70 56 129

Eup: Eup: Eup: Eup:

42%, 37%, 26%, 35%,

Hedgerows

Siaya

***

0.62  0.06 0.64  0.04 0.42  0.06 0.73  0.04

Lan: Lan: Lan: Lan:

14%, 32%, 22%, 26%,

Psi: Psi: Psi: Psi:

13%, Dra: 11% 7%, Dra: 6% 16%, Dra: 10% 11, Dra: 9%

*

Permanent crops

Banana Tea Pasture

19 2 20

Trees

Individual tree Windrow Woodlot Mean Significance (F)

165 17 3 224

Hedgerows

Low density Medium density High density Mean Significance (F)

108 67 37 152

Permanent crops

Banana Pasture

– – –

Musa sp. Camellia sinensis Brachiaria sp.

0.86  0.05 0.17  0.06 0.22  0.22 0.62  0.04

Mar: 47%, Man: 8%, Per: 6%, Euc: 5% Mar: 37%, Cup: 17%, Cas: 7%, Gre: 7% Euc: 96% Mar: 32%, Man: 18%, Euc: 8%

***

30 36

0.39  0.04 0.44  0.04 0.46  0.06 0.49  0.03 ns

Eup: 51%, Lan: Lan: 55%, Eup: Lan: 56%, Eup: Lan: 56%, Eup:

– –

Musa sp. Brachiaria sp.

40% 28% 28% 21%, Tith: 7%

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.

types. A total of 99 perennial plant species were identified growing in the 35 farms visited, of which 76 were found in the tree component, 30 in the hedgerow component and 7 in both components. A total of 49 tree species were identified in the two locations of Vihiga and 56 in the two of Siaya. Tree biodiversity as 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 poorest in woodlots, intermediate in windrows and richest for trees growing scattered within or around the field crop plots (i.e., individual trees in Table 1). While all of the tree species identified at both sites were seen growing as individual trees, only 25% of them were found in windrows, and only 10 and 4% in woodlots in Vihiga and Siaya, respectively. 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 species in woodlots (96%) in Siaya, where Markhamia lutea K. Schum. was most commonly observed as individual trees (47%) and in windrows (37%). The floristic composition, structural arrangement and functionality of hedgerows differed between Vihiga and Siaya, with 87% of all species seen growing in hedgerows observed in Vihiga and approximately half of that in Siaya. The average diversity of plant species 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 species diversity than medium- and low-density hedgerows, while no differences in plant species diversity were observed between hedgerow density classes in Siaya. Euphorbia tirucalli and Lantana camara were the species most commonly observed in growing hedgerows 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 composition yielded nine hedgerow types, which are indicative of distinct hedgerow configurations and functions (Table 2), as confirmed by our field assessments. Hedgerows that were planted with the main objective of demarcating boundaries (types 1, 5, 8 and 9) were dominated by Euphorbia tirucalli and Lantana camara and secondarily by Draceana steudneri (type 2). Hedgerows planted with the objective of providing firewood (type 6) were dominated by Markhamia lutea, while those planted with the additional objective of fruit production (type 4) were dominated by Psidium guajava. Hedgerows were also planted to harvest organic material for use in soil 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 ornamental purposes (type 7) mostly around the homestead, often demarcating the compound area from the field crop plots. Most of the hedgerows observed in the 35 farms sampled corresponded to type 8 (24.1 and 54.2% in Vihiga and Siaya, respectively), while types 6 and 4 had the lowest frequency at both sites. On average, 9.3 and 15.3% of the farm area was occupied with hedgerows in Vihiga and Siaya, respectively. Hedgerow type 8 covered 2.1 and 8.1% of the area of the surveyed farms in Vihiga and Siaya, respectively, while hedgerow type 6 covered barely 0.2 and 0.5% (Table 2). The diversity of tree species growing in or around the land use units varied widely between plots that were under different types of land use (LUT), with the poorest values obviously in woodlots (H = 0.09 and 0.34) and the richest in homegardens (H = 1.3 and 1.6) at both sites (Table 3). Biodiversity within hedgerows did not differ significantly between LUTs in Vihiga. In Siaya, hedgerows were more diverse around the homegardens (H = 0.60) and pasture plots (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 2 A 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)

Euphorbia tirucalli L. Lantana camara L. Markhamia lutea K. Schum. Dracaena steudneri Engl. Psidium guajava L. Tithonia diversifolia Hemsl. Acanthus pubescens Engl. Ornamental Others

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)

47.0  3.9 49.0  3.7 0.4  0.3

1.5  0.7 3.3  2.1 6.7  3.2

0.5  0.4 3.5  1.1 0.9  0.5

4.4  2.1 7.9  3.5 4.4  2.4

90  1.4 2.4  0.4 1.2  0.4

6.3  3.0 6.3  3.6 66.04.3

5.8  1.7 6.0  1.8 0.2  0.18

1.5  0.4 89.0  1.3 1.7  0.5

71.0  2.9 23.0  1.8 –

3.6  2.0 0.4  0.3 –

85.0  3.5 – 1.5  1.0

0.3  0.2 2.5  0.9 23  5.6

– 74  5.1 0.6  0.6

2.3  0.9 2.2  0.5 1.2  0.7

4.4  2.2 3.1  2.5 5.0  3.3

– 0.9  0.88 0.5  0.53

0.6  0.3 3.8  0.7 2.0  0.6

– 6.2  2.8 –

1.8  1.2

0.1  0.1

–

83.0  3.0 2.1  1.3

0.6  0.3 1.0  0.5

– –

–

0.6  0.6

23  5.3

6.8  3.7

0.5  0.5

–

1.1  1.1 0.4  0.3

1.5  1.5 –

0.2  0.2 46  6.2

– 2.1  1.4

0.7  0.4 0.1  0.1

3.1  3.1 6.3  3.4

Hedgerow ground cover (%) Vihiga 4.62 Siaya 9.76

9.81 2.39

13.4 3.52

9.28 0.24

Proportion of the farm area (%) Vihiga 0.51  0.21 Siaya 1.53  0.52

1.71  1.19 0.39  0.22

1.15  0.26 0.57  0.24

18.9 15.4

0.88  0.44 0.03  0.03

1.38  0.42 2.10  0.48

2.73 2.52

9.80 7.61

0.21  0.17 0.49  0.20

0.76  0.21 1.15  0.55

24.1 54.2

2.14  0.81 8.09  1.57

7.35 4.30

0.55  0.28 0.89  0.44

 standard error.

3.2. Aboveground biomass and C stocks The stock of C in the aboveground biomass of trees was calculated using the allometric model proposed by Satoo (cited in Ponce-Hernandez, 2004). The relationships between total aboveground biomass and volume (r2 = 0.95***) and branch fresh weight and branch basal diameter (r2 ranged 0.66***–0.94***) were highly significant (Figs. 4 and 5). The average aboveground stock of C in trees expressed on the basis of their crown surface area, or tree C density, was similar in Vihiga and Siaya farms for trees growing scattered in or around the land use units and for those growing in windrows, but it was larger in Siaya for trees growing in woodlots (Fig. 6A). However, individual trees represented an important aboveground C stock in Vihiga (38%) and particularly Siaya (82%). Trees growing in windrows represented barely 6 and 15% of the total aboveground C stock, respectively, while woodlots represented most (56%) of the C stock in Vihiga and only 3% in Siaya (Fig. 6B). The aboveground tree C density based on vegetation

ground cover was also greater in woodlots than in individual trees and windrows at both sites (P < 0.01) (not shown). The average aboveground stock of C in tree biomass expressed on the basis of the area of the land use units where the trees were inventoried was greater in Vihiga than in Siaya (8.8 and 4.9 t C ha1 respectively, P < 0.05), and it differed between plots under different land use types (P < 0.001) (Table 4). At both sites, the larger tree C stocks were found in woodlots, followed by homegardens and by food and cash crop land use units, whereas the smaller tree C stocks were measured in pasture plots. The average aboveground stock of C in hedgerow biomass expressed on the basis of vegetation ground cover, or hedgerow C density, varied between hedgerow density classes, and was larger in Siaya than in Vihiga for the high-density hedgerows (P < 0.0001) (Fig. 6C). In Vihiga, most of the hedgerow C stock was distributed between medium-density hedgerows (43%) and high-density

Table 3 Biodiversity (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

Vihiga

Homegarden Food crop Cash crop Pasture Woodlot Mean Significance (F)

18 89 15 4 20 146

Homegarden Food crop Cash crop Pasture Woodlot Mean Significance (F)

20 173 8 20 3 222

Biodiversity (H) Trees

Siaya

*

**

Hedgerows

1.310.17 0.460.06 0.400.16 0.440.44 0.090.04 0.510.05

a b bc bc c

0.870.12 0.580.06 0.480.10 0.880.44 0.380.09 0.590.04 ns

a a a a a

1.590.12 0.490.04 0.620.32 0.910.15 0.340.19 0.630.04

a c bc b c

0.600.09 0.290.03 0.060.06 0.560.07 0.230.23 0.330.03

a b b a b

***

***

***

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.

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.

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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.

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

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

Table 4 Aboveground C stock (Mg ha1) in 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

Aboveground C stock (t C ha1) Trees

Hedgerows

Permanent crops

Total

Vihiga

Homegarden Food crop Cash crop Pasture Woodlot Mean Significance (F) Homegarden Food crop Cash crop Pasture Woodlot Mean Significance (F)

18 89 15 4 20 146

9.6  2.0 b 2.9  0.6 c 2.5  1.3 c 0.4  0.4 c 36.9  8.3 a 8.8  1.6

1.1  0.2 b 1.3  0.2 b 1.10.4 b 0.6  0.3 b 2.6  1.0 a 1.4  0.2

3.1  0.04 a 0.1  0.02 c 1.2  0.7 b 2.1  1.0 b 0.0  0.0 c 0.6  0.1

13.8  2.0 b 4.3  0.6 c 4.8  1.7 bc 3.0  1.3 c 39.4  8.5 a 10.8  1.7

***

***

Siaya

 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.

20 173 8 20 3 222

***

*

8.3  2.8 b 2.9  0.6 b 6.1  3.0 b 1.8  0.5 b 115.9  62.0 a 4.9  1.2

5.9  1.7 2.8  0.4 1.9  1.1 6.4  1.9 6.7  3.9 3.4  0.4

***

**

a a a a a

3.0  0.2 0.0  0.0 0.4  0.2 2.6  0.3 0.0  0.0 0.6  0.1 ***

a d c b d

17.3  2.9 b 5.7  0.7 c 8.3  4.0 c 10.8  2.0 bc 122.6  59.2 a 8.9  1.3 ***

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Table 5 Average biomass density (kg DM m3) for each hedgerow type based on their floristic composition, growing at low, medium and high density. Density class

Low Medium High

Hedgerow type 1

2

3

4

5

6

7

8

9

n/a 1.03  0.16 n/a

0.45  0.09 1.19  0.07 2.05  0.52

n/a 0.56  0.03 1.36  0.41

0.90  0.29 1.63  0.28 2.12  0.21

1.27  0.40 2.35  0.14 2.54  1.56

n/a 1.06  0.33 1.11  0.47

n/a 0.76  0.29 0.79  0.20

0.67  1.22 1.45  0.13 2.29  0.48

n/a 0.73  0.05 1.01  0.39

consisted mainly of Euphorbia tirucalli (cf. Table 2), had the highest biomass density while hedgerow type 7, which consisted mainly of a mixture of ornamental species, had the lowest biomass density. The largest part of the C stock in the aboveground biomass of permanent crops was essentially found in pastures (Vihiga: 71% and Siaya: 96%), with tea plantations representing the second most important C stock in Vihiga (no tea plantation found in Siaya—cf. Table 1). At both sites, the average stock of C in permanent crops expressed on plot area basis was larger in homegardens (due to the presence of banana stands) followed by pasture plots (P < 0.05) (Table 4). The average aboveground C in permanent crops was not significantly different between Vihiga and Siaya. At both sites, the aggregated stock of C in the aboveground biomass of trees, hedgerows and permanent crops was the largest in woodlots followed by the homegardens (Table 4). The average stock of aboveground C in homegardens was three times larger than in food crop, cash crop and pasture plots in Vihiga. In Siaya, homegardens stored more C in their aboveground biomass than the rest of the land use types and also more than in Vihiga. The average total aboveground C stock did not differ significantly between sites. Summarizing, the tree vegetation component represented the most important C pool of the aboveground biomass of perennial plants growing on-farm, contributing 81 and 55% of total C in Vihiga 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 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. Cash crop and pasture plots stored the smallest C stock, 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 mean total aboveground C stocks of Table 4, 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 farm1 in Vihiga and 12.4  0.1 Mg C farm1 in Siaya. 3.3. On-farm biodiversity and aboveground C stocks There was no straightforward relationship between biodiversity of perennial plant species growing in a certain land use unit and the stock of C in their aboveground biomass. The largest storage of aboveground C was measured in woodlots (Table 4), which were practically monospecific (Table 3). The homegardens had the richest diversity of perennial plant species (generally with H > 1), and the largest average aboveground C stocks among the rest of the land use types excluding woodlots (Fig. 7A and B). Roughly, and in plots under land use types other than woodlots, a wider diversity of species tended to be associated with somewhat larger aboveground C stocks in Vihiga. In Siaya, the land use units cropped with annual crops had less diversity of perennial plant species growing in or around the land use units than the homegardens, but could have larger aboveground C stocks. Monospecific tree formations tended to store more C in

their aboveground biomass, while the land use units having a large diversity of individual trees growing in or around them did not necessarily store larger amounts of aboveground C (Fig. 7C and D). The trend towards larger C stocks in land use units with a wider diversity of perennial species could also be ascribed to their average area, since larger plots are more likely to have more trees, more C stored in these trees, and a also larger chance of having different tree species growing in or around them. However, there was no direct relationship between the area of the plots and the diversity of perennial species (Fig. 8A) or between area and the total amount of C stored in perennial aboveground biomass (Fig. 8C). A similar pattern (or the lack of it) was observed for farmscale 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 the aboveground biomass of their perennial vegetation, with the notably 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-farm stocks of C in perennial biomass by intensifying tree biomass than by intensifying hedgerows (Fig. 9). The amount of C stored in the aboveground biomass of trees could be potentially increased on average by 14 mg ha1 in Vihiga and by 10.8 Mg ha1 in Siaya (Table 6). While an extra 4 and 6.8 Mg C ha1 could be stored in hedgerow aboveground biomass in Vihiga and Siaya, respectively, the total potential for C sequestration in perennial vegetation did not differ significantly across sites (ca. 16 Mg ha1). The calculated extra C stocks in aboveground biomass of trees are more than double the current stocks (Fig. 9A). About half of the potential for C sequestration in tree biomass in Vihiga could be achieved by intensifying tree windrows, while in Siaya there is ample room to intensify woodlots (73% of the C sequestration potential) (Table 6). Intensifying perennial vegetation in the homegardens represents less than 10% of the C sequestration potential at both sites. The minimum size for a small scale CDM A/R projects in Vihiga and Siaya would need to cover an area of 199 and 256 ha, respectively, for the scenario of increasing the stocks of C in aboveground tree biomass up to its attainable maximum (S1); 702 and 407 ha through planting and intensifying hedgerows around plots (S2); and 171 and 175 ha through intensification of both tree and hedgerow biomass. Considering an average farm size of 0.6 ha in Vihiga and 1.2 ha in Siaya, the minimum number of households to be engaged in a small scale CDM A/R project that proposes to intensify all tree and hedgerow formations – particularly the linear plant formations around plots and farms – would be about 300 and 140 in Vihiga and Siaya, respectively. 4. Discussion The diversity of perennial plant species growing on-farm varied across sites, vegetation components and land use units grouped according to the type of land use observed (Tables 1 and 3), but it

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Fig. 7. Above, aboveground stock of carbon in permanent vegetation (Mg ha1) 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 m2) 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.

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 vegetation component, while hedgerows were less diverse. A larger diversity of tree species was observed in Siaya, which may be the result of a longer history of agriculture in Vihiga, with consequently earlier and more intense deforestation in this densely populated area of western Kenya (Crowley and Carter, 2000). On the contrary, hedgerow plant species diversity was more important in Vihiga than in Siaya. Livestock keeping represented a more important activity in Siaya, where hedgerows were mainly used as fences to protect crops from marauding animals. Hedgerows planted with this purpose consisted essentially of high-density Euphorbia tirucalli L. and Lantana camara L. stands. In Vihiga, hedgerows were planted with more diverse purposes, such as demarcating land use units or farm boundaries, providing firewood, fruits or biomass to feed (stalled) livestock or to use as soil amendments for soil fertility management, or simply for ornamental purposes. This is in agreement with the observations of Lauriks et al. (1998) who remarked that farmers in Vihiga are used to ‘manage’ their hedgerows, whereas people in the more extensive farming systems of Siaya do not practice any active hedgerow management. Higher tree species diversity was particularly observed in the homegardens, where they are normally grown for fruit production

or medicinal purposes (Figeroa-Go´mez, 2007). Homegardens are typically located around the homestead, and often surrounded by ornamental hedgerows. Ornamental plant species were diverse and were essentially planted as low-density hedgerows (i.e., demarcating internal boundaries), which may contribute to explain the richer diversity of species observed in this density class in Vihiga (cf.: Table 1). Ornamental species were rarer in Siaya, where hedgerow biodiversity varied less across density classes or plots grouped by land use. Several other factors may determine the floristic composition and structure (i.e., height, density) of hedgerows. For example, the type of relations with neighbouring farms, security in land tenure or the type of livestock management system (free grazing vs. stalled) may explain the presence of different hedgerow configurations and purposes. Kindt et al. (2004) pointed out that households managed by women used to plant more trees to delimitate their farm because they were more likely to face land tenure problems. Thus, the diversity of perennial plant species grown on-farm may be also affected by socioeconomic aspects of the household—a relationship that may be worth exploring if the intensification of perennial biomass and biodiversity were to be promoted among smallholders. The stocks of C in the aboveground perennial biomass measured in the 35 farms visited (between 9 and 11 Mg C ha1, on average)

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

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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.

were notably lower than the estimates for tropical agro-forestry systems presented by Dixon (1995) and by Woomer et al. (1997). This is presumably because they were only focusing on agroforestry plots while this study considered a mixture of agroforestry and cropping systems. Variability in within-farm aboveground C stocks was also very important, with C stocks ranging between 0.5 and 120 Mg ha1 for different vegetation components and land use types. Most of the tree C was present in woodlots in Vihiga and in scattered individual trees in Siaya. Land scarcity and population pressure on natural resources are more intense in Vihiga, where larger demands for firewood are met through woodlot plantations. The relationship between the type of land use observed in a certain land use unit and the aboveground C stock in tree and hedgerow biomass measured in or around such plot did not always exhibit a clear pattern. In other words, tree C stocks were not significantly

different between food crop, cash crop and pasture plots, while in Siaya hedgerow C stocks were not significantly different between all land use types except woodlot. Most C inventories at regional scale are based on identification of land use types or land cover (e.g., Achard et al., 2004). Such an approach would certainly underestimate variability in C stocks within farms and land use types. Instead, C inventories based on identification of vegetation forms and management would be more reliable to estimate aboveground C stocks. Identification of woodlots, windrows and individual trees, high-, medium- and lowdensity hedgerows, and tea, grass and banana plantation could be done using aerial photographs or satellite imageries. Although differences in aboveground C stocks between farms were not analysed in this study, we expect that the socio-economic diversity of households will further influence the size of on-farm C stocks, not

Fig. 9. Relationship between current aboveground C stocks in trees (A) and hedgerows (B) ad their respective potential for C sequestration (delta C stock), calculated as the difference between the current C stock measured and the C stock corresponding to the third quartile in the distribution of C stocks for each vegetation component/land use type. The shadowed area below the hypothetical upper boundary line illustrates the C sequestration potential in each vegetation component, irrespective of site differences. For clarity, one point has been omitted from panel A (Siaya: current stock 12.7 Mg ha1; delta C 47.7 Mg ha1), which corresponds to the point above the boundary line in panel B.

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Table 6 C 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

Land use

Individual trees

Number of plots Vihiga Siaya

Scenario 2: Intensifying hedgerow biomass Windrows

Woodlots

High

Medium

Low

16 19

20 3

98 178

112 77

84 39

144 222

1.1 2.3

32.7 30.5

54.4 230.0

22.9 34.3

14.5 18.1

7.9 9.5

nr nr

0.7  0.37 1.2  0.22

31.4  0.57 29.2  0.83 ns

22.7  3.61 109.8  56.86 ns

7.8  0.03 11.0  0.02

4.6  0.02 4.4  0.03 ns

2.7  0.02 2.1  0.04 ns

16.3  1.58 15.9  0.98 ns

49.2 19.7

35.7 73.8

30.8 25.4

17.7 12.1

Homegardens

Food crops

Cash crops

Pastures

18 20

89 171

14 8

3 20

Maximuma C stock attainable (Mg C ha1) Vihiga 11.7 3.8 Siaya 7.4 3.4

1.4 5.3

Average aboveground C sequestered (Mg C ha-1) Vihiga 5.6  1.06 2.6  0.16 Siaya 2.9  0.58 2.1  0.10 * ** Significance (F)

0.7  0.16 3.5  0.89 ns

Proportion of total C sequestered (%) Vihiga 8.8 Siaya 2.0

1.1 2.3

4.1 1.4

Scenario 3: S1 + S2

*

1.2 0.8

***

51.5 62.5

Average total C sequestered (Mg C ha1) Vihiga Siaya Significance (F)

14.0  1.54 10.8  2.35 ns

4.0  0.46 6.8  1.06 ns

16.3  1.58 15.9  0.98 ns

Minimum project size (ha) Vihiga Siaya

199 256

702 407

171 175

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. *

only through influencing land size (e.g., Tschakert and Tappan, 2004), but also determining the feasibility, profitability and acceptability of project activities that may affect participation in CDM A/R projects (Franzel, 1999). The biophysical potential to increase C stocks on-farm depended on land availability and use, vegetation components and current aboveground C stocks. In Vihiga, current C stocks were larger in woodlots than in windrows and individual trees, but the potential to increase C stock was greater in windrows since land is barely available to extend woodlots or planting more individual trees. Without compromising food security, the potential to increase C stocks in aboveground biomass was calculated at 16 Mg ha1 at both sites. While Houghton et al. (1993) estimated a C sequestration potential in aboveground biomass through agroforestry interventions of 59 Mg C ha1 for sub-Saharan Africa, the estimates of C sequestration presented by Unruh et al. (1993) were more in agreement with the calculations presented in Table 6. On the other hand, Woomer et al. (1997) argued that 66 Mg C ha1 could be sequestered both above- and belowground through nutrient recapitalization and agroforestry. Belowground C stocks include C in organic litter, soil organic C (SOC) and C in root biomass (Hairiah et al., 2001), and they represent the major C pools in agro-ecosystems (e.g., 80% of total C stocks in agroforestry systems—Dixon, 1995). Estimating belowground C stocks, particularly the root C pool, is time-consuming and may be subject to a relatively high degree of uncertainty (Manlay et al., 2002). Earlier approaches have estimated belowground C stocks using general coefficients to estimate root:shoot biomass ratios (IPCC, 2007) and by defining ranges of SOC contents per soil type (GEFSOC, 2005). Roughly, using a mean root-shoot biomass ratio for this tropical latitudinal zone of 0.24 (Cairns et al., 1997), the average stocks of C in root biomass would be in the order of 3.6, 1.0, 1.2 Mg C ha1 in homegardens, food crop fields and woodlots, respectively. Measurements of SOC and bulk density in fields under different land use

in our study area (Tittonell, 2007) allow us to estimate soil C stocks ranging between 24 and 56 Mg C ha1—for the upper 0.3 m of the soil. Thus, there is much more to gain in terms of C sequestration in the belowground C components. Investments in afforestation/reforestation (A/R) within the CDM framework are proposed as an option for adaptation to climate change, mitigation of atmospheric C and sustainable rural development. Additionally, a larger tree cover would lead to decrease soil degradation. In our study area, the minimum number of participants to be involved in small scale CDM A/R, just to cover the transaction costs, with a sequestration potential of 16 Mg ha1 (without compromising food production) was calculated in 170 households. Furthermore, to produce economical incentives to encourage farmers to participate and to cover the costs of implementation, the CDM A/R project should sequester more than 16 Mg C ha1. If the potential for C sequestration belowground was also considered, the number of participants would be considerably smaller, but yet large enough to compromise the feasibility of the project, implying high implementation and monitoring costs. The transaction costs used in our calculations (Locatelli and Pedroni, 2006) were conservatively low, and did not include implementation costs or potential financial compensation to farmers. The study of Woomer et al. (1997) estimated C sequestration costs of USD 47 Mg C1 in smallholder farms of East African highlands, while financial compensations through the C market oscillate around USD 10 Mg CO21. 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 this study, which would necessarily compete with food security, (2) reducing transaction costs (i.e., decreasing the cost of technologies), or (3) increasing C market prices, e.g., by valuing other services related to C sequestration such as sustainable development or biodiversity. The last proposition consists of including soil

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conservation, particularly in degraded areas such as western Kenya, that have great potential for soil C sequestration (Vagen et al., 2004). Within the framework of the CDM it seems more economically attractive to plant mono-specific woodlots rather than multi-usage tree systems in the context of C trade. Species functional characteristics strongly influence ecosystem properties and, in fluctuating environments such as tropical agroecosystems, longterm productivity may increase with species richness due to an increased capacity to buffer physical disturbances (Yachi and Loreau, 1999). Due to such multi-functionality, C that is sequestered within diverse and stable agroecosystems should be therefore better valued than C sequestered in poor-biodiversity (e.g., monoclonal eucalyptus plantation) systems. However, the mechanisms that were set up to reach the commitment goals of the Kyoto protocol of the United Nations Framework Convention on Climate Change (UNFCC) consider only afforestation and reforestation activities as eligible under the CDM (UNFCCC, 2004). Its implementation requires that the impact of C sequestration on biodiversity be taken into account, but financing is not an explicit function of biodiversity. C sequestration by intensifying agroforestry systems such as those found in homegardens represents a win–win strategy that could increase both biodiversity, C storage and contribute to decrease soil degradation and household nutritional security and diversity of farm income. Intensive homegardens may be also easier to promote among farmers, as they could be targeted to only certain plots within a farm. 5. Conclusion A wide diversity of perennial plant species was recorded growing on smallholder farms of western Kenya, particularly of trees growing in homegardens or scattered in or around the food and cash crop land use units of the farms. These trees contributed to the aboveground C storage, but to a lesser degree than the contribution of mono-specific woodlots dominated by Eucalyptus saligna. Biodiversity or perennial vegetation and aboveground C stocks did not increase with the land use unit or farm areas, except for a weak positive trend indicating greater C stocks in the larger farms in Siaya. There was no direct relationship between the diversity of perennial plant species growing on-farm and the aboveground C stocks, and thus biodiversity can be seen as an independent, additive agro-ecosystem function. C sequestration projects that contribute to enhance biodiversity should be considered as more ethical and stable in the long-term than conventional afforestation/reforestation projects that do not consider biodiversity or ecosystem function. The implementing CDM A/R projects in densely populated regions of sub-Saharan Africa, such as western Kenya is seriously limited by the poor potential for C sequestration without compromising food production on-farm. Large areas of farmland are necessary to at least cover the transaction costs of implementing an CDM A/R project, and these translate into large numbers of smallholder farmers to be involved, joining their hands in adopting C sequestration practices over a minimum time period of 20 years. In order to increase the potential implementation of A/R CDM projects in western Kenya, funding would have to better consider other environmental services and do not limit their actions to afforestation and reforestation activities. An extra limitation that face the implementation of CDM A/R projects over large areas is the accountability of C in the agro-ecosystem. The detailed inventorying used in this study revealed important information in terms of variability of 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

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broader scale methods based on remote sensing. Further studies aiming at analyzing the feasibility of C sequestration in farming systems of western Kenya should focus on the long-term resilience of C storage and biodiversity, on the potential for belowground C sequestration, and on social factors that may influence adoption of C sequestration practices. Acknowledgements We thank the European Union for funding this research through the Africa NUANCES Project (Contract No. INCO-CT-2004-003729), and the Tropical Soil and Biology and Fertility of the International Centre for Tropical Agriculture (TSBF-CIAT) for technical and logistical support. This work received financial support from IRD, ICRAF and AgroParisTech-ENGREF. The authors wish to thank John Mukalama, Isaac Ekise, Laban Nyambega, Guideon Omito, Stefen Njoroge, Caroline Awlor, Naman Obuyi, Benson Aluko and Elliot Owino for their assistance. References Achard, F., Eva, H.D., Mayaux, P., Stibig, H.-J., Belward, A., 2004. Improved estimates of 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.), Soil erosion 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 agrodiversity assessment. In: Brookfield, H., Padoch, C., Parsons, H., Stocking, M. (Eds.), Cultivating Biodiversity: Understanding, Analysing and Using Agricultural Diversity. ITDG Publishing, London, UK, pp. 41–56. Brown, S., Gillespie, A.J.R., Lugo, A.E., 1989. Biomass estimation methods for tropical forest 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 a tropical forest. Science 310, 1029–1031. Cairns, M.A., Brown, S., Helmer, E.H., Baumgardner, G.A., 1997. Root biomass allocation in the world’s upland forests. Oecologia 111, 1–11. Crowley, E.L., Carter, S.E., 2000. Agrarian change and the changing relationships between toil and soil in Maragoli, Western Kenya (1900–1994). Human Ecology 28, 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-Go´mez, B.M., 2007. The Contribution of Traditional Vegetables to Household Nutritional Security in Two Communities of Vihiga and Migori Districts. Wageningen University, Kenya. Franzel, S., 1999. Socioeconomic factors affecting the adoption potential of improved fallows in Africa. Agroforestry Systems 47, 305–321. GEFSOC, 2005. Assessment of soil organic carbon stocks and change at national scale. In: Technical Report of GEF Co-financed Project No. GFL-2740-02-4381. University of Reading, UK, GEF Implementing Agency, The United Nations Environment Programme. GOK, 2003. Kenya: Interim Poverty reduction Strategy Paper 2000–2003. World Bank. Hairiah, K., Sitompul, S.M., Van Noordwijk, M., Palm, C., 2001. Methods for Sampling Carbon 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 its potential 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 differences 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 of border hedges and the analysis of variables influencing their distribution: a case study in western Kenya. Agroforestry Systems 44, 69–87. Locatelli, B., Pedroni, L., 2006. Will simplified modalities and procedures make more small-scale forestry projects viable under the Clean Development Mechanism? Mitigation and Adaptation Strategies for Global Change 11, 621–643.

252

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

Magurran, A.E., 1988. Ecological Diversity and its Measurement. Princeton University 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 African savanna. I. The plant component under semi-permanent cultivation. Agriculture, Ecosystems and Environment 88, 215–232. Montagnini, F., Nair, P.K.R., 2004. Carbon sequestration: underexploited environmental 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 and yield. Experimental Agriculture 37, 333–342. Ponce-Hernandez, R., 2004. Assessing Carbon Stocks and Modelling Win–win Scenarios of Carbon Sequestration through Land-use Changes. Food and Agriculture Organization of the United Nations, Rome, Italy. Satoo, T., 1955. Materials for the study of growth in stands. Tokyo University Forestry Bulletin 48, 69–123 (in Japanese, English summary). Schulze, E.-D., 2006. Biological control of the terrestrial carbon sink. Biogeosciences 3, 147–166. Shepherd, K.D., Soule, M.J., 1998. Soil fertility management in west Kenya: dynamic simulation of productivity, profitability and sustainability at different resource endowment levels. Agriculture, Ecosystems and Environment 71, 131–145. Tittonell, P., Vanlauwe, B., Leffelaar, P.A., Giller, K.E., 2005. Exploring diversity in soil fertility management of smallholder farms in Western Kenya. II. Within-farm

variability in resource allocation, nutrient flows and soil fertility status. Agriculture, Ecosystems and Environment 110, 166–184. Tittonell, P., 2007. Msimu wa Kupanda—Targeting Resources for Integrated soil Fertility Management within Diverse, Heterogenous and Dynamic Farming Systems of East Africa. Wageningen University. Tschakert, P., Tappan, G., 2004. The social context of carbon sequestration: considerations from a multi-scale environmental history of the Old Peanut Basin of Senegal. Journal of Arid Environments 59, 535–564. UNFCCC. 2004. Modalities and procedures for afforestation and reforestation project activities under the clean development mechanism in the first commitment period of the Kyoto Protocol. p. 13–31. In. Report of the Conference of the Parties 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, United Nations Office at Geneva, Geneva. Unruh, J.D., Houghton, J., Lefebvre, P.A., 1993. Carbon storage in agroforestry: an estimate for sub-Saharan Africa. Climate Research 3, 39–52. Vagen, T.G., Lal, R., Singh, B.R., 2004. Soil carbon sequestration in sub-Saharan Africa: a review. Land Degradation and Development 16, 54–71. Woomer, P.L., Palm, C.A., Qureshi, J.N., Kotto-Same, J., 1997. Carbon sequestration and organic resource management in African smallholder agriculture. In: Lal, R., Kimble, J.M., Follett, R.F., Stewart, B.A. (Eds.), Management of Carbon Sequestration in Soil. CRC Press Inc., Boca Raton, pp. 153–173. Yachi, S., Loreau, M., 1999. Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Ecology 96, 1463–1468.