Carbon storage and emissions offset potential in an East African tropical rainforest

Forest Ecology and Management 235 (2006) 72–83 www.elsevier.com/locate/foreco

Carbon storage and emissions offset potential in an East African tropical rainforest Julia Glenday * Center for Environmental Studies, Brown University, Box 1943, Providence, RI 02912, United States Received 14 September 2005; received in revised form 29 May 2006; accepted 1 August 2006

Abstract Forestry based carbon emissions offset projects have potential to both mitigate climate change and foster sustainable forest management. Degraded African tropical forests could sequester large amounts of additional carbon, but the lack of empirical data limits the feasibility of initiating carbon offset projects in many threatened forests. This study examines the potential to increase carbon stocks in the Kakamega National Forest of western Kenya, a threatened biodiversity hotspot and Kenya’s only remaining rainforest. Carbon density values for indigenous forest and plantations were estimated based on forest inventory data from 95 randomized plots distributed throughout the forest. Total ecosystem carbon was estimated using allometric equations for tree biomass, destructive techniques for litter and herbaceous vegetation biomass, and Dumas combustion and spectroscopy for soils. Land cover maps for 1975, 1986, and 2000 were used to estimate both current carbon stocks and the influence of past land use changes. Mean carbon density in indigenous forest was 330  65 Mg C/ha, greater than that of the forest’s hardwood plantations (280  77 Mg C/ha) and significantly greater that that of softwood plantations (250  77 Mg C/ha). The distribution of carbon densities within the indigenous forest and the variation between plantation types suggest management practices could feasibly increase Kakamega’s carbon stock. Deforestation between 1975 and 1986 and limited reforestation from 1986 to 2000 have resulted in a net loss of 0.4–0.6 Tg C. If this loss were reversed, the value of possible associated carbon credits dwarfs the current operational budget for managing and protecting the forest, even at low carbon prices. Additional income could help address resource needs of impoverished communities surrounding the forest and promote sustainable protection of Kakamega’s high biodiversity. # 2006 Elsevier B.V. All rights reserved. Keywords: Africa; Carbon storage; Forest management; Kakamega; Kenya; Land cover change; Tropical forest

1. Introduction Scientific concerns regarding tropical deforestation and global climate change have motivated ongoing efforts to quantify the role of forests as terrestrial carbon stores in the global carbon cycle (Brown, 1997; Houghton, 1997; Watson et al., 2000; Clark et al., 2002). An estimated 13 million ha of tropical forest is lost each year to deforestation (FAO, 1999), emitting 5.6–8.6 Gt of carbon into the atmosphere. However, the quantification of carbon storage in tropical forests is far from complete, particularly in the forests of Sub-Saharan Africa, forests which account for one fifth of global net primary production (Brown and Gaston, 1995; Cao et al., 2001). Deforestation in Africa has been rapid, with the loss of

* Correspondence to: 2614 Augusta Dr., Durham, NC 27707, United States. Tel.: +1 919 493 4299. E-mail address: [email protected]. 0378-1127/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.foreco.2006.08.014

approximately 53,000 km2 of closed forest from 1990 to 2000 (FAO, 2003). Refinement of current carbon storage estimates enhance understanding of how changes in African forest cover may accelerate, or be used to mitigate, predicted climate change. Place-based studies are crucial to this effort and necessary for initiating appropriate mitigation strategies sustainable in the local socioeconomic, political, and cultural context. The emergence of a global market for carbon credits, earned through investments in activities that quantifiably offset or reduce carbon emissions, offers a powerful, but not yet fully refined, tool to finance improved forest management and sustainable development. By 2000, well before the 2005 ratification of the Kyoto Protocol and its Clean Development Mechanism, over 150 bilateral carbon-trading projects had been developed (Bass et al., 2000), yet few have been in Africa. Model-based assessments of carbon storage in Africa’s forests indicate that much of the areas that are biophysically capable of supporting carbon rich tropical forests are currently degraded

J. Glenday / Forest Ecology and Management 235 (2006) 72–83

and deforested (Brown and Gaston, 1995; Zhang and Justice, 2001) and that Kenya specifically could almost double its current aboveground biomass. Kenya lost 930 km2 of closed forest from 1990 to 2000 (FAO, 2003). The Kakamega National Forest of western Kenya – a protected area with a long history of deforestation, a high use value for surrounding residents, and a constant threat of further degradation – provides a promising and important site for initiating carbon offset activities in Kenya. Believed to be the easternmost relic of the GuineoCongolian rainforest belt that once spanned the breadth of Africa (Kendall, 1969; Kokwaro, 1988; Wass, 1995), the Kakamega National Forest is Kenya’s only remaining rainforest fragment larger than a few hundred hectares. Bio-physical conditions and historical accounts indicate that much of western Kenya was once forested and could still support closed canopy forest (Kendall, 1969; Kokwaro, 1988; Lovett and Wasser, 1993), however Kakamega Forest is now set in a landscape dominated by small scale agriculture and high population densities of 10 people/ha (Kendall, 1969; Kokwaro, 1988; Wass, 1995). Regional trends of forest loss have continued even within the national forest boundaries: more than 50% of Kakamega’s indigenous forest cover was cleared in a span of 30 years (Wass, 1995). Despite its reduced size, the remaining 140 km2 of indigenous forest is the headwaters for the district’s rivers (Kokwaro, 1988), retains a globally significant level of biodiversity (Wass, 1995), and provides essential goods and services (fuelwood charcoal, water, grazing areas, medicinal, and edible plants) to a heavily reliant local population (Kokwaro, 1988; Emerton, 1994; Wass, 1995; Nambiro, 2000). This study uses field data and GIS analysis to (a) estimate carbon storage in the current landscape of the Kakamega National Forest, (b) provide an assessment of the potential for further carbon sequestration based on observed carbon densities, and (c) identify possible management options to achieve this in light of trends in forest management and land use in Kakamega. 2. Methods 2.1. Site description and land use history The Kakamega National Forest (08100 N–08210 N, 348580 E) is located on the edge of the Lake Victoria basin in Western Province, Kenya. The area is 1520–1680 m above sea level with an annual rainfall of 2000 mm and a mean daily maximum temperature of 26 8C and mean daily minimum of 11 8C. The region’s soils are predominantly volcanic clays and clay loams, ferralo-chromic acrisols with some humic cabrisols and acrisols at forest edges. The Kakamega National Forest, established in 1933, covers approximately 240 km2 of which one third is currently used for agriculture or forest plantations. The remainder is largely closed canopy indigenous forest with a 30 m canopy dominated by evergreen hardwood species, the most common of which are Funtumia africana, Ficus species, Croton species, and Celtis species.

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Government management of Kakamega was initiated in the 1920s under the pervue of the Forest Department (FD). Local residents were evicted from the forest, concentrating populations and the need for land and resources around forest margins. Initially managed for colonial timber enterprise, roads and sawmills were established in the forest. Commercial logging ensued and clear felled areas were converted to timber plantations of indigenous hardwoods (KIFCON, 1994). By the 1960s increased timber and fuelwood demand spurred planting of fast growing, non-indigenous softwood species. In 1986, the felling of indigenous trees was outlawed and all subsequent plantations have been planted with exotic species such as Pinus patula, Cupressus lustanica, and Eucalyptus saligna (Kakamega Forest Department, 2003). In the 1940s, the FD initiated the ‘shamba (small farm) system’, in the forest. Local farmers were allowed to plant food crops on cleared plots in the National Forest if timber seedlings were intercropped and maintained. After approximately five years of tree growth, the farmer relinquished use of the plot to the FD. Both sawmill workers and farmers came to live inside the National Forest. Concern over the impacts of this system on the forest lead to a prohibition of new plantation area establishment in the 1970s and a complete ban on the shamba system in 1985. By 1988 all forest residents were again evicted and, in the absence of shamba system labor to replant, the majority of local sawmills closed (Sharp, 1993). By 1997, 20% of people living within 10 km of the forest were landless and households’ average annual expenditures exceeded annual incomes. In response, a strictly non-residential shamba system was temporarily re-introduced and, despite a 2000 ban on logging in Kenya’s national forests, a few large logging companies have been permitted to continue operations (Sharp, 1993). Since 1985, Kakamega has been managed by both the Forest Department (FD) and Kenya Wildlife Service (KWS). The FD retained 200 km2, 55% of which was indigenous forest. Cattle grazing and the collection of dead fuelwood, medicinal plants, and thatching grass were still permitted in FD controlled land in 2003, while logging and charcoal burning had become illegal. In two FD Nature Reserves (7 km2) established in 1967 and in the KWS managed Kakamega National Reserve (40 km2) established in 1985, all extractive uses have been prohibited. As a quasigovernmental body, KWS has been able to retain and use the revenues it produces, unlike the FD, which operates on national budget allocation. In 2003, KWS spent roughly 35 times as much money per hectare of forest to manage the National Reserve as the FD was allocated for the rest of Kakamega. The National Reserve has been more heavily patrolled than FD areas and entry fees restrict forest access. Household surveys indicated that those residing within 10 km of the KWS National Reserve received almost no income from the forest, while those adjacent to the FD forest had remained reliant on forest resources, with approximately 60% of average household income directly tied to forest use (Emerton, 1994; Sharp, 1993; Nambiro, 2000).

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2.2. Sampling design Forest carbon stocks were measured in six pools (live tree aboveground biomass, tree belowground biomass, coarse deadwood (10 cm diameter), litter, herbaceous vegetation, and soil) within 95 20 m  20 m plots stratified across landuses (Brown, 1997; MacDicken, 1997). Samples were stratified into indigenous forest, hardwood plantation, and softwood plantation, with the number of plots roughly proportional to the area in each class. Within the three land cover classes, plots were further stratified into two age classes. By comparing land cover maps from 1986, and 2000 using ArcView GIS# (Environmental Systems Research Institute, Inc., USA). ‘Young’ plots had gained remotely sensed forest or plantation cover after 1986 and were therefore under 20 years old. ‘Old’ plots had established tree cover prior to 1986, but plots ranged widely in actual age due to the mosaic of localized forest disturbances and plantation rotations. Older hardwood plantations ranged from 25 to 50 year old (Kakamega Forest Department, 2003). Plots were also classified as visibly undisturbed or disturbed by extractive use (grazing, firewood collection, logging, and charcoal burning) based on the presence or absence of paths, cut stumps, cut branches, or charcoal pits within plots. To minimize damage, plots were located using existing footpaths. Plots were established perpendicular to paths at random distances from paths. Plantation plots were randomly located within units containing common plantation species: indigenous hardwoods (mixed species plantations or monocultures of Maesopsis eminii, Prunus africana), exotic hardwoods (E. saligna, Bischoffia javanica), and exotic softwoods (P. patula, C. lustanica). The Kakamega Plantation Registry (Kakamega Forest Department, 2003) provided maps of plantation units, but lacked up-to-date species composition and planting date information. 2.3. Estimation of carbon density and current carbon storage 2.3.1. Vegetative biomass All live and dead trees and lianas >20 cm dbh were measured in 95 20 m  20 m plots with trees >5 cm and <20 cm dbh measured in 10 m  10 m nested subplots. Diameters of trees with buttresses were measured above the point where buttresses merged with the bole, if this occurred below 3 m, otherwise the dbh was measured and adjusted using a proportional correction factor to estimate above buttress diameter. Species-specific diameter correction factors were calculated from measurements of average buttress protrusion at 1.3 m height from what was assumed to be a circular central bole (Appendix A). The relative importance of each species was calculated using relative density, relative frequency and relative coverage (Brower et al., 1997). Tree biomass was estimated using allometric equations. A general tropical moist forest equation (Brown, 1997) was used for indigenous species, but specific equations were used for: Eucalyptus spp. (Specht and West,

2003), C. lustanica (Monteith, 1979), Pinus spp. (Brown, 1997), and lianas (Putz, 1983). For indigenous species with low wood density (F. africana, Ficus exasperata, Ficus lutea, Ficus sur, Ficus thongnoni, M. eminii, Macaranga kilimansharica, and Trema orientalis) the biomass was adjusted proportionally to the mean wood density assumed in the generalized equation as in Chave et al. (2003). Belowground biomass was calculated using a root–shoot biomass ratio of 0.24 (Cairns et al., 1997). It was assumed that 50% of vegetative biomass was carbon (MacDicken, 1997). The mass of coarse deadwood was estimated in each plot by measuring diameters and decomposition status of all downed trees and branches with diameter 10 cm along two perpendicular 20 m transects. The density of deadwood was calculated using Harmon and Sexton (1996) method and decomposition class data reported by Clark et al. (2002). The mass of standing dead trees was estimated using allometric equations, discounted by decomposition status. In the corners of 47 randomly selected plots out of the 95, 0.5 m  0.5 m subplots were established in which all understory vegetation, excluding tree saplings, was cut and weighed. Litter was separately collected and weighed in each subplot. Two hundred grams of herbaceous and litter subsamples were dried for wet-dry weight ratios used to estimate total sample dry biomass. 2.3.2. Soil carbon Soil cores were collected in the same plots as litter samples using three increments: 0–20, 20–40, 40–60 cm. Samples were air-dried, sieved to 2 mm, and pulverized. Carbon concentrations were predicted using a spectral library approach (Shepherd and Walsh, 2002) using diffuse reflectance spectroscopy (FieldSpec FR spectroradiometer; Analytical Spectral Devices Inc., Boulder, Colorado) at wavelengths from 0.35 to 2.5 mm with a spectral sampling interval of 1 nm (Shepherd et al., 2003). Over 50% of the samples were also analyzed using the modified Dumas combustion method. Soil carbon concentrations were calibrated to their reflectance spectra using partial least squares regression with Unscrambler 7.5# software (CAMO Inc., Corvallis, OR, USA). Regression models were used to predict concentrations from spectra for those samples for which there were no direct carbon determinations. Based on Awiti (2001) a bulk density of 0.64  0.03 g/cm3 was assumed. 2.3.3. Aggregation and extrapolation A total ecosystem carbon density mean (Mg C/ha) for each land cover class was estimated by averaging the plot carbon densities for each of the six pools. Forest carbon stock was determined two ways: by multiplying the cover class averages by the area of each cover class, and by interpolating carbon density across the indigenous forest area. Interpolation was carried out on a one hectare grid in which unsampled grid cells were assigned carbon densities using inverse distance weighting (IDW) of values from the five nearest plots (ArcView GIS, Environmental Systems Research Institute, Inc., USA; Houghton et al., 2001; Reese et al., 2002).

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Table 1 Carbon density and distribution of carbon amongst carbon pools Carbon pool

Mean carbon density (Mg C/ha  95% confidence interval) Indigenous forest

Above ground tree biomass Below ground tree biomass Deadwood biomass Soil carbon Herbaceous biomass Litter biomass

Hardwood plantation

Softwood plantation

Old (N = 46)

Young (N = 16)

Old (N = 10)

Young (N = 13)

Old (N = 5)

Young (N = 5)

200  36 49  9 1.2  0.4 100  17 0.8  0.4 5.4  0.9

81  32 20  8 0.1  0.1 63  36 0.3  0.1 5  1.4

200  49 51  12 0.7  0.4 110  37 0.9  0.4 6.3  2.0

111  44 28  11 0.4  0.5 93  24 0.8  0.6 4.2  1.3

150  50 38  12 0.7  1.1 94  15 0.3  0.1 6.2  2.1

72  29 18  7 0.5  0.7 120  41 0.3  0.1 2.7  1.7

Total mean carbon density

360  63

Area in 2000 Area-weighted mean carbon density

12,400

170  78 1580 330  65

2.3.4. Statistical analyses and uncertainty assessment One-way ANOVA was used to detect significant differences among land cover class carbon densities. Tukey’s HSD test was used for pairwise comparisons of mean tree biomass carbon densities due to non-normal data distributions, while a student’s t-test was used for comparison of soil carbon. Unless specified, an alpha of 0.05 was used to determine significance. To estimate uncertainty in average carbon density for each cover class, 5000 iteration Monte Carlo analyses were run using Crystal Ball 5.5# (Decisioneering, Inc., USA) software with probability distributions of tree diameter, plot size, wood density, and buttress correction factors. The uncertainty of the total carbon stock for the forest was calculated by scaling the uncertainty range of each cover class by its aerial contribution to the total forest area.

370  90 920

240  71

290  82

2210 280  77

280

210  75 350 250  78

Surveys (DRSRS) were overlain to quantify land-cover change and examine spatial distribution of change. Maps were created using satellite image and aerial photograph analyses and ground-truthing. Changes in carbon stocks over 25 year were estimated assuming that carbon densities in 1975 and 1986 were similar to those measured in 2003. Lacking maps for previous and equivalent time intervals, cover in 1975 and 1986 maps could not be classified as ‘young’ and ‘old’ in 2000. For this reason, area-weighted average carbon densities were calculated based on the distribution of ‘young’ and ‘old’ cover in 2000 and applied to the earlier time periods. 3. Results 3.1. Carbon densities in land cover classes

2.4. Spatial analysis of carbon distribution in indigenous forest Georeferenced town, road, river, park boundary, and forest ranger station locations (DRSRS, 2000) were used to look for trends in carbon storage values with distance from potential cofactors. The program R 4.0 was used to assess spatial autocorrelation in carbon density data without reference to a particular cofactor. 2.5. Assessment of land cover change (1975–2000) effects on carbon storage estimates Land cover maps from 1975, 1986, and 2000 provided by the Kenya Department of Remote Sensing and Resource

The area-weighted mean carbon density for indigenous forest was 330  65 Mg C/ha (Table 1). This was greater than that of hardwood plantations, 280  77 Mg C/ha ( p = 0.25), and significantly greater than that of softwood plantations, 250  78 Mg C/ha ( p = 0.03). The difference between forest and hardwood plantation carbon appeared due to the agedistribution within these classes: the hardwood plantation area was 70% ‘young’ with low carbon, while the indigenous forest was 89% ‘old’. Comparing age stratified cover classes (Tables 1 and 2), old hardwood plantation carbon density, 370  90 Mg C/ha, was very similar to old indigenous forest, 360  63 Mg C/ha ( p = 0.72). Young hardwood plantations had greater mean carbon density (240  71 Mg C/ha) than young indigenous

Table 2 Results of pairwise comparisons (Tukey HSD) for aboveground tree biomass carbon density among land cover types p-Value

Young indigenous forest

Old hardwood plantation

Young hardwood plantation

Old softwood plantation

Young softwood plantation

Old indigenous forest Young indigenous forest Old hardwood plantation Young hardwood plantation Old softwood plantation

0.0012

0.7207 0.0028

0.0003 0.8228 0.0014

0.0433 0.2633 0.0382 0.1710

0.0001 0.0656 0.0001 0.0793 0.0154

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forest (170  78 Mg C/ha), although the difference was not significant ( p = 0.82). Stratifying indigenous forest plots into KWS versus FD managed areas did not produce statistically significant differences in either old (KWS, 370  94 Mg C/ha; FD, 340  80 Mg C/ha; p = 0.90) or young (KWS, 160  86 Mg C/ha; FD, 160  42 Mg C/ha; p = 0.91) plots. The tree species distribution in young, regenerating forest was found to be different than that observed in the mature forest. Young forest plots were dominated by primary colonizers and fast-growing species, such as Bridellia micrantha, Harungana madagascariensis, Psidium quajava, and woody shrubs (Vernonia amygdalina and Solanum mauritania). Old forest plots were dominated by tall canopy species, F. africana, Croton megalocarpus, and Celtis species (C. durandii and C. mildbraedii). This distribution confirms previously observed successional patterns (Kokwaro, 1988; Tsingalia, 1988; Earlham University, 1999). The range of plot carbon densities within a cover class was greatest in old indigenous forest: plot values ranged by

600 Mg C/ha, almost twice the variation seen in other classes. The distribution of tree biomass carbon densities among old and young indigenous forest plots was non-normal and positively skewed (Fig. 1). Soil carbon densities also showed a wide variation in old indigenous forest plots, with a range of 120 Mg C/ha, but with a less skewed distribution (Fig. 1). Forest plot tree carbon storage showed a linear positive correlation with soil carbon ( p < 0.05, r2 = 0.32). Plantation plots showed a range of carbon density values reflecting plantation age and species. Mature indigenous hardwood and B. javanica (exotic tropical hardwood) plantations had carbon densities between 360 and 400 Mg C/ha, values higher than the mean found in indigenous forest plots. Mature plantations of the faster growing, low wood density species, E. saligna (190 Mg C/ha), C. lustanica (220 Mg C/ha), and P. patula (240 Mg C/ha), had carbon densities that were one half to two thirds the values found for the indigenous hardwoods. However, these low carbon density plantations mature more quickly and were roughly half the age of the high carbon density hardwoods.

Fig. 1. Distribution of plot tree biomass and soil carbon densities amongst young and old indigenous forest plots.

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3.2. Current carbon stock In 2000 there was a total carbon stock of 5.7  0.6 Tg C in the Kakamega National Forest, based on mean cover class carbon densities (Table 1). The majority of carbon was in the indigenous forest (4.7  0.7 Tg C). Using the IDW interpolation of tree biomass carbon density (Fig. 2) produced a value of 3.2 Tg C stored in indigenous forest trees, adding soil, litter, deadwood, and herbaceous vegetation carbon (1.5 Tg C in sum) yielded the same 4.7 Tg C. 3.3. Error and uncertainty analysis Tree biomass adjustments made for buttressed and low wood density trees had notable influences on mean forest and

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hardwood plantation biomass because 20–30% of old plots and 37% of young forest plots containing trees with buttresses and 50% of plots containing at least one low wood density species. Compared to unadjusted values for forest and hardwood plantation biomass, the buttress adjustments decreased means by 14–17% and the wood density adjustments decreased them by 9–11%. These adjustments did not alter the carbon density ranking of the land cover classes and did decrease standard deviations in the effected classes. Variation in plot carbon within each cover class accounted for most of the uncertainty in mean carbon density estimates. Uncertainty associated with tree and plot size measurements and biomass adjustments resulted in small coefficients of variation (CV) of 0.13 for individual plot carbon densities

Fig. 2. Plot carbon density and interpolated carbon density surface for the Kakamega Forest. The forest area was transformed into a 1 ha cell grid. Grid cells were assigned carbon density values based on an inverse distance weighting (IDW) of values from the five closest sampled plots.

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Table 3 A comparison of mean aboveground tree biomass carbon densities for recently disturbed and undisturbed plots in indigenous forest Cover class

Old indigenous forest Young indigenous forest

Number of plots

Mean aboveground tree biomass carbon density (Mg C/ha)

Undisturbed

Disturbed

Undisturbed

Disturbed

Difference

32 6

14 10

266 135

189 114

77 21

values across all land cover classes. However CVs for mean cover class carbon densities were high because of plot variation within classes: 0.62 for old indigenous forest, 0.53 for young indigenous forest, 0.43 for old hardwood plantations, 0.69 for young hardwood plantation, 0.37 for old softwood plantation, and 0.65 for young softwood plantation.

p-Value

0.0367 0.693

number of large (dbh  50 cm) and very large trees (dbh  70 cm) in a plot predicted over 50% of the variation in plot carbon densities in the indigenous forest ( p < 0.05). Species richness and tree abundance showed significant covariance with carbon density, but explained little of the variation (r2 < 0.1, p < 0.1). 3.5. Land cover change

3.4. Patterns in carbon density distribution There was no significant spatial autocorrelation in carbon density distribution, but some spatial patterns were evident in the indigenous forest. Visibly disturbed plots in young and old indigenous forest had lower mean carbon densities than those in which human disturbances were not observed, 21 and 77 Mg C/ ha lower respectively, however the difference was only significant in mature forest (Table 3). On average, visibly disturbed plots were closer to forest access points (agricultural areas, grazing areas and paths) than undisturbed (200 m closer, p = 0.001). Straight-line distance from various sources of anthropogenic influence showed little power to predict carbon density at this sampling intensity (all analyses: r2 < 0.1, 0.001 < p < 0.4). Visual analyses of the interpolated carbon density surface (Fig. 2) revealed high carbon densities near the KWS and FD forest stations. Although there was no significant linear relationship, mean old forest carbon density within 2 km of either forest station was 280 Mg C/ha greater than the average for plots located at distances greater than 2 km from a station ( p = 0.001). Roughly 80–90% of the difference in total carbon density among plots was the result of differences in tree biomass. The

Between 1975 and 1986, Kakamega lost 18% of natural forest cover (Table 4) to agricultural use (52% of the loss) and expansion of grasslands and open forest (45%). There was also an almost 40% decline in hardwood plantation cover, but agricultural areas seen replacing plantations (48% of the loss) would likely have been replanted under pre-1985 shamba system cultivation. Assuming similar age and species compositions to those found in 2000, these changes would have resulted in a loss of 19% (1.2 Tg C) of Kakamega’s carbon stock, mostly due to indigenous forest losses. From 1986 to 2000, there was a 13% increase in tree cover. Indigenous forest increased by 700 ha to 86% of its 1975 extent. Seventy percent of this resulted from forest colonization of grasslands and open forests, primarily near forest stations (Fig. 3). Overall, grassland and open forest reforestation rates were similar on the KWS (46% of open area forested) and FD (40%) managed lands. Roughly 1400 ha of agricultural land gained forest cover, 70% of which was converted to hardwood plantation, presumably through the shamba system. There was a net loss of softwood plantation, replaced by agricultural land, which may be replanted under the resumed shamba system. Land cover changes between

Table 4 Land cover and land cover change in Kakamega National Forest, 1975–2000 Land cover class

1975

1986

Area (ha)

Area (ha)

Change 1975–1986 (%)

Area (ha)

Change 1986–2000 (%)

Indigenous forest Hardwood plantation Softwood plantation

16142 2314 938

13307 1404 968

18 39 3

13967 3131 632

5 123 35

Tree cover

19395

15678

19

17730

13

1492 1063 1743 16

1860 1837 4269 64

25 73 145 309

1110 858 3836 16

40 53 10 75

0

0

158

23708

23708

23708

Forest glades Open forest/shrubs Agriculture Other (built up, quarry) Excised area Total area

2000

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Fig. 3. Trends in deforestation and afforestation in Kakamega National Forest from 1975 to 2000. Maps of land cover provided by DRSRS were overlayed in a GIS system. Deforestation and agricultural expansion resulted in the loss of forest cover at the southern and northwestern edges of the forest, mostly between 1975 and 1986. Forest regeneration occurred in grasslands around the KWS and FD stations from 1986 to 2000.

1986 and 2000 resulted in a 0.6 Tg C increase in carbon storage, with one third of the increase due to indigenous forest regeneration and two-thirds due to increased hardwood plantations. As a result of deforestation between 1975 and 1986 and reforestation between 1986 and 2000, Kakamega experienced a 9% net decline in total tree cover (forest and plantation) and 14% (2200 ha) net decline in the area of natural forests over 25 year. It was estimated that this shift in forest cover resulted in the loss of 0.4–0.6 Tg C. If the 2000 distribution of ‘young’ and ‘old’ treed areas was assumed to apply throughout the 1975–2000 period, calculations indicated a 0.6 Tg C loss in carbon stock. However, stratifying the 1986 map into ‘young’ and ‘old’ forest classes

with reference to 1975, revealed a significant age distribution change among hardwood plantations: 70% of hardwood plantations were ‘young’ in 2000, with only 22% were ‘young’ in 1986. Assuming this, only 0.4 Tg C would have been lost between 1975 and 2000. However, the FD plantation register indicated that the proportion of high carbon density indigenous hardwood species planted after the late 1980s decreased with the increased use of E. saligna (Kakamega Forest Department, 2003). Incomplete plantation documentation prevented quantitative assessment, but the loss of carbon rich hardwood plantations between 1975 and 1986 and their replacement with low carbon density plantations in 1986–2000 would have resulted in a greater net carbon loss than the above calculations assume.

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Table 5 Comparing Kakamega’s old indigenous forest carbon density and carbon density estimates for mature Neotropical moist forests Country/region

Carbon density (Mg C/ha)

Source

Total ecosystem

Aboveground biomass

Amazonia Amazonia Amazonia Amazonia Panama Venezuala

386

178 192 196 232 141 179

Kakamega, Kenya (old indigenous forest)

360  63

200  36

4. Discussion 4.1. Forest carbon stock Kakamega’s indigenous rainforest had an average aboveground biomass density (400  72 Mg/ha) consistent with potential AGB modeled for African lowland moist forest (412 Mg/ha; Brown and Gaston, 1995). The indigenous forest’s carbon density was similar to densities seen in the Neotropics (Table 5), highlighting this land cover type as an important potential carbon store. The precision of carbon storage estimates for Kakamaga Forest was largely constrained by heterogeneity within land cover classes. The positively skewed distribution of plot carbon densities and the dominant influence of large tree distribution on plot carbon in Kakamega was similar to distributions seen in Neotropical moist forests (Chave et al., 2003; Keller et al., 2001; Brown et al., 1995; Clark and Clark, 1996). An IDW nearest neighbor interpolation was used to estimate indigenous forest carbon stock to account for this spatial variation and not extrapolate a mean from a non-normal distribution. However the interpolation produced the same value as the application of the calculated average. 4.2. Potential to increase carbon storage Kakamega’s history of heavy anthropogenic use has clearly reduced the amount of carbon stored in its landscape. Before the 1930s, cohesive indigenous forest extended far beyond the boundaries of the Kakamega National Forest (Kendall, 1969; Kokwaro, 1988; Wass, 1995). If the entire National Forest area excluding forest glades had maintained indigenous forest cover with a similar carbon density to current forest, which may be depressed by ongoing human disturbance, the area would store 7–10 Tg C as opposed to the 5.7  0.6 Tg C stored in 2000. Reversing these losses in a sustainable manner through appropriate forest management, rehabilitation, and/or land cover changes could significantly increase Kakamega’s carbon storage over a baseline of continued degradation with limited regeneration. 4.2.1. Regeneration and protection of indigenous forest Forest classified as young in 2000 was characterized by early successional species and had a mean carbon density

DeFries et al. (2000) Houghton et al. (2001) Potter et al. (1998) Fearnside (1997) Chave et al. (2003) Delaney et al. (1997)

190 Mg C/ha lower than old forest ( p = 0.0012). If protected, regeneration of Kakamega’s 1600 ha of ‘young’ secondary forest could increase carbon storage by roughly 0.3 Tg C assuming the area attained the average carbon density measured in ‘old’ forest. It may also be possible to expand indigenous forest cover in grasslands and degraded areas through protection from heavy use and/or enrichment planting. Kokwaro (1988) suggested forest glades were maintained by edaphic conditions, however some may have been opened in the past by grazing elephants, buffalo, and antelope, now extirpated (Kokwaro, 1988; Wass, 1995), and more recently by cows and goats. Some forest colonization of grasslands was seen over the period 1986–2000. If Kakamega’s 1550 ha of open vegetation became closed forest, this could yield 0.5 Tg C in tree biomass alone and soil carbon may also increase. Heterogeneous carbon density within ‘old’ indigenous forest suggests further potential for increasing carbon stock by reducing human disturbances. Carbon density trends may be linked to accessibility for human use: visibly disturbed old forest plots had lower carbon densities than undisturbed (77 Mg C/ha lower, p = 0.036) and were located closer to forest access routes. Management activities, such as forest patrols and environmental education projects, in the immediate vicinity of forest stations may have influenced carbon storage by preventing human disturbance. Old indigenous forest plots within 2 km of a forest station had no evidence of human disturbance and had a significantly higher average carbon density (690  130 Mg C/ ha) than those at greater distances (340  30 Mg C/ha, p = 0.001). However, this effect did not extend throughout the KWS National Reserve and FD Nature Reserve areas: on average reserve plots did not have statistically lower disturbance frequencies or higher carbon densities than the rest of the forest. 4.2.2. Plantation areas Softwood plantations had significantly lower carbon densities than either indigenous forest or hardwood plantation and mature E. saligna plantations had roughly half the carbon density of other hardwoods. If softwood plantation areas in the year 2000 had the average carbon density of the hardwood plantation area, stored carbon stock could be increased by

J. Glenday / Forest Ecology and Management 235 (2006) 72–83

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Table 6 Predicted carbon storage under KIFCON’s suggested forest zonation scenario Suggested land use

Area (ha)

Representative sampled cover

Carbon density (Mg C/ha)

Non-extractive forest zone Extactive use forest zone 10 year plantationa 30 year plantationa

12,070 6,600 2,100 3,200

Old indigenous forest Disturbed indigenous forest E. saligna C. lustanica

356 203 94b 108b

Total stored a b

6.2 Tg C

KIFCON estimated wood needs for one village scaled up for all villages bordering Kakamega Forest. Half of projected carbon density for mature stand.

0.02 Tg C. Replacing these with indigenous forest would increase the stock by 0.06 Tg C. Kakamega’s plantations could be managed to both store carbon and help meet local resource needs. If harvested and regrown at a sustainable rate, plantation rotation areas can be assumed to store half the carbon of a mature stand (Schroeder, 1992). Half the carbon density of a mature stand of B. javanica (180 Mg C/ha), would result in higher carbon storage efficiency than land with half the carbon storage density of E. saligna (90 Mg C/ha), however the ability of each species to meet wood demand rates would need to be weighed against the carbon benefit. Using slow maturing species coupled with promotion of fuelwood saving techniques could help match use rates with growth rates. 4.3. Management options for carbon offset projects Regeneration of indigenous forest in Kakamega could substantially increase carbon sequestration and could be promoted by enrichment planting of degraded areas and increased protection against extractive use. However, as resource strapped local communities continue to rely on extractive forest use, even where illegal, Kakamega may need to be managed as a multiuse forest for sustainability and to prevent ‘leakage’ of carbon benefits (by driving extractive use to other areas). A management strategy that could balance local resource needs and deforestation prevention is the ‘Biosphere Reserve’ model: a natural reserve area is divided into a core zone in which no extractive uses are permitted surrounded by zones for extractive use by local communities who directly participate in planning use and management (UNESCO, 2002). Kenya Indigenous Forest Conservation Program (KIFCON) proposed this for Kakamega in 1993, suggesting that the KWS Reserve and a core region of the FD forest be reserved for strictly non-extractive use while an 11,900 area around forest margins be available for extractive uses of natural forest and plantations as co-managed by the FD, KWS, and local community groups (Sharp, 1993). Applying carbon densities found in this study to areas suggested for the different land uses (Table 6), indicated that this plan could increase carbon stock by 0.5 Tg C or more. A 0.5 Tg C carbon offset is within the range of internationally funded reforestation and forest management based carbon-offset projects in Belize, Malaysia, Mexico,

and Russia (World Resources Institute, 2002). Even with low speculated carbon prices ($3–5/t C, under $18/t CO2), a carbon storage project of this size could bring in as much as $2.5 million over the course of forest regeneration or plantation growth. If this carbon was sequestered over a period of 50 years credit sales could yield $50,000/year. Even if 50% of this went to carbon trading transaction costs, such as verification and monitoring, the trade would yield sums greater than the annual operation budgets of the Kakamega FD, KWS, or any local community conservation organization. 5. Conclusion The East African indigenous rainforest found in Kakamega supports high levels of biodiversity and provides sundry ecosystem services to Western Kenya. In addition, as a high carbon density land cover type, it can provide a global service as carbon store helping to mitigate climate change. While past human disturbances have reduced forest areas and depressed forest carbon densities, the results of this illustrates the potential to increase carbon storage in the Kakamega National Forest at a scale that is economically, and perhaps ecologically, significant for the region. Acknowledgements This project was made possible by the Watson Scholars Program and the Royce Fellowship Program of Brown University and facilitated by the Kenya Forest Department, Kenya Wildlife Service (KWS), Kakamega Environmental Education Program (KEEP), the International Center for Insect Physiology and Ecology (ICIPE), and the World Agroforestry Center (ICRAF). Many thanks to Lauren McGeoch and Michal Kapitulnik and to Patrick Luteshi, Bonface Shimenga, and Winstone Opondo of KEEP for help, support, and field work. Thanks also to Dr. Steven Hamburg, Dr. Ian Gordon, and Matthew Delaney for advising. This project would not have been possible without the technical support from GIS/RS and soil lab specialists at Brown University and ICRAF, Nairobi. Thanks to Lucie Rogo and Betty Nzokia at the Kenya Department of Remote Sensing and Resource Surveys (DRSRS) for providing land cover maps and to Dr. Alex Awiti for providing additional soil data.

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Appendix A Buttress correction factor for tree diameters measured over buttresses.

By assuming the idealized regular polygon shown on the right hand side of the figure, the radius of the circular bole was calculated using the law of cosines. Linear regression between measured diameter and circular bole diameter was used to find a correction factor. Sample calculation of diameter correction factor: Ficus species Measured dbh (cm) reading over buttress

Number of buttresses

Average buttress protrusion (cm)

Average distance between butresses (cm) side length of idealized polygon

Diameter of circular bole (cm)

19.6 39.3 22.7 49.5 62.1 75.2 165.7

3 4 3 5 6 6 9

1.0 2.6 1.0 6.5 9.4 13.7 28.8

13.0 30.5 18.3 31.4 26.0 34.8 56.1

13.0 37.9 19.2 40.5 33.2 42.3 106.4

Diameter correction factors for buttressed species Species

Samples

Diameter correction factor

Ficus species Aningeria altissima Celtis species

7 4 4

0.65 0.5 0.64

References Awiti, A., 2001. Linking soil functional capacity decline to land use and landuse change: a case study of Kakamega and South Nandi forest ecotones, Western Kenya. PhD thesis submitted to Department of Botany, University of Nairobi. Bass, S., Dubois, O., Moura Costa, P., Pinard, M., Tipper, R., Wilson, C., 2000. Rural livelihoods and carbon management. International Institute for Environment and Development Natural Resource Issues, Paper No. 1, IIED, London. Brower, J.E., Zar, J.H., von Ende, C.N., 1997. Field and Laboratory Methods for General Ecology. 4th ed. Dubuque, Iowa: Wm C. Brown Publishers.

J. Glenday / Forest Ecology and Management 235 (2006) 72–83 Brown, I.F., Martinelli, L.A., Thomas, W.W., Moreira, M.Z., Cid Ferreira, C.A., Victoria, R.A., 1995. Uncertainty in the biomass of Amazonian forests: an example from Rondoˆnia, Brazil. Forest Ecol. Manag. 75 (1–3), 175–189. Brown, S., Gaston, G., 1995. Use of forest inventories and geographic information systems to estimate biomass density of tropical forests: application to tropical Africa. Environ. Monit. Assess. 38, 157–168. Brown, S., 1997. Estimating Biomass and Biomass Change of Tropical Forest: A Primer. Food and Agriculture Association of the United Nations (FAO), Rome. 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. Cao, M.K., Zhang, Q.F., Shugart, H.H., 2001. Dynamic responses of African ecosystem carbon cycling to climate change. Clim. Res. 17 (2, Special Issue), 183–193. Chave, J., Condit, R., Lao, S., Caspersen, J.P., Foster, R.B., Hubbell, S.P., 2003. Spatial and temporal variation of biomass in a tropical forest: results from a large census plot in Panama. J. Ecol. 91 (2), 240–252. Clark, D.B., Clark, D.A., 1996. Abundance, growth and mortality of very large trees in neotropical lowland rain forest. Forest Ecol. Manag. 80 (1–3), 235– 244. Clark, D.B., Clark, D.A., Brown, S., Oberbauer, S.F., Veldkamp, E., 2002. Stocks and flows of coarse woody debris across a tropical rain forest nutrient and topography gradient. Forest Ecol. Manag. 164 (1–3), 237– 248. DeFries, R.S., Hansen, M.C., Townshend, J.R.G., Jentos, A.C., 2000. A new global 1-km data set of percentage tree cover derived from remote sensing. Global Change Biol. 6, 247–254. Delaney, M., Brown, S., Lugo, A.E., Torres-Lezama, A., Bello Quintero, N., 1997. The distribution of organic carbon in major components of forest located in five life zones of Venezuela. J. Trop. Ecol. 13, 697– 708. Emerton, L., 1994. Summary of the current value use of Kakamega forest. Unpublished report for Kenya Indigenous Forest Conservation Programme (KIFCON), Nairobi. Fearnside, P.M., 1997. Greenhouse gases from deforestation in Brazilian Amazonia: net committed emissions. Clim. Change 32, 321–360. FAO, 1999. State of the World’s Forests. Food and Agriculture Association of the United Nations (FAO), Rome. FAO, 2003. State of the World’s Forests. Food and Agriculture Association of the United Nations (FAO), Rome. Earlham University, 1999. Conservation and Management of Kakamega Forest. Unpublished report online at URL: http://www.earlham.edu/biol/kakamega/conservation.htm. Harmon, M.E., Sexton, J., 1996. Guidelines for Measurements of Woody Detritus in Forest Ecosystems. US LTER Publication No. 20. US LTER Network Office, University of Washington, Seattle, WA, USA. Houghton, J., 1997. Global Warming: The Complete Briefing, second ed. Cambridge University Press, Cambridge. Houghton, R.A., Lawence, K.T., Hackler, J.L., Brown, S., 2001. The spatial distribution of forest biomass in the Brazilian Amazon: a comparison of estimates. Global Change Biol. 7, 731–746. Kakamega Forest Department, 2003. Ministry of Environment and Natural Resources for the Government of Kenya, Plantation General Register. Ref.#10/1/1/vol III. Keller, M., Palace, M., Hurtt, G., 2001. Biomass estimation in the Tapajos National Forest Brazil: examination of sampling and allometric uncertainties. Forest Ecol. Manag. 154, 371–382.

83

Kendall, R.L., 1969. An ecological history of the Lake Victoria Basin. Ecol. Monogr. 39 (2), 121–176. Kenya Indigenous Forest Conservation Programme (KIFCON), 1994. Kakamega Forest: The Official Guide. Unpublished report for the Kenya Forest Department, Nairobi. Kokwaro, J.O., 1988. Conservation status of the Kakamega Forest in Kenya: the easternmost relic of the equatorial rain forests of Africa. Monogr. Syst. Bot. Missouri Bot. Garden 25, 471–489. Lovett, J.C., Wasser, S.K., 1993. Biogeography and Ecology of the Rain Forests of Eastern Africa. Cambridge University Press, Cambridge. MacDicken, K.G., 1997. A Guide to Monitoring Carbon Storage in Forestry and Agroforestry Projects. Unpublished report for Winrock Institute for Agricultural Development. Monteith, D., 1979. Whole-tree weight tables for New York. Applied Forestry Research Institute. Research Report 40. Nambiro, E, 2000. Socio-economic baseline survey of households around Kakamega Forest. Unpublished report for Kenya Indigenous Forest Conservation Programme (KIFCON), Nairobi. Potter, C.S., Davidson, E.A., Klooster, S.A., Nepstad, D.C., de Negreiros, G.H., Brooks, V., 1998. Regional application of an ecosystem production model for studies of biogeochemistry in Brazilian Amazonia. Global Change Biol. 4, 315–333. Putz, F., 1983. Liana biomass and leaf-area of a ‘‘Tierra Firme’’ forest in the Rio-Negro Basin, Venezuela. Biotropica 15 (3), 185–189. Reese, H., Nilsson, M., Sandstrom, P., Olsson, H., 2002. Applications using estimates of forest parameters derived from satellite and forest inventory data. Comput. Electron. Agric. 37 (1–3), 37–55. Sharp, A.F.G., 1993. Kakamega Forest Rural Development Report. Unpublished report for Kenya Indigenous Forest Conservation Programme (KIFCON), Nairobi. Shepherd, K.D., Walsh, M.G., 2002. ICRAF, Development of reflectance spectral libraries for characterization of soil properties. Soil Sci. Soc. Am. J. 66 (3), 988–998. Shepherd, K.D., Palm, C.A., Gachengo, C.N., Vanlauwe, B., 2003. Rapid characterization of organic resource quality for soil and livestock management in tropical agroecosystems using near-infrared spectroscopy. Agron. J. 95, 1314–1322. Schroeder, P.E., 1992. Carbon storage potential of short-rotation tropical tree plantations. Forest Ecol. Manag. 50 (1–2), 31–41. Specht, A., West, P.W., 2003. Estimation of biomass and sequestered carbon on farm forest plantations in northern New South Wales, Australia. Biomass Bioenergy 25, 363–379. Tsingalia, M.H., 1988. Animals and the Regeneration of an African Rainforest Tree. PhD thesis, University of California, Berkeley, California. UNESCO, 2002. Biosphere Reserves: on ground testing for sustainable development. Unpublished report online at URL: http://www.unesco.org/mab/ publications/BRbrochure.pdf. Wass, P., 1995. Kenya’s Indigenous Forest Status. Management and Conservation. IUCN, Gland, Switzerland and Cambridge, UK. Watson, R., Nobe, I.R., Bolin, B., Ravindranath, N.H., Verado, D.J., Dokken, D.J., 2000. IPCC Special Report on Land Use, Land-Use Change and Forestry. Cambridge University Press, Cambridge. World Resources Institute, 2002. Forest and land-use change carbon sequestration projects. Unpublished report online at URL: http://www.wri.org/climate/sequester.html. Zhang, Q., Justice, C.O., 2001. Carbon emissions and sequestration potential of central African ecosystems. Ambio 30 (6), 351–355.