Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi

Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi

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Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi Shem Kuyah a,b,*, Gudeta W. Sileshi c, Joyce Njoloma c, Simon Mng’omba c, Henry Neufeldt a a

World Agroforestry Centre (ICRAF), P.O. Box 30677-00100, Nairobi, Kenya Jomo Kenyatta University of Agriculture and Technology (JKUAT), P.O. Box 62000-00200, Nairobi, Kenya c World Agroforestry Centre (ICRAF), P. O. Box 30798, Lilongwe 3, Malawi b

article info

abstract

Article history:

Trees outside forests support smallholder farmers’ livelihoods and play a critical role in the

Received 17 October 2013

global carbon cycle. However, their contribution to climate change mitigation through

Received in revised form

carbon storage is not obvious because of limited information regarding their extent, and

28 January 2014

inadequate methods for biomass quantification. This study evaluated the distribution of

Accepted 4 February 2014

aboveground biomass (AGB) in three 100 km2 benchmark sites in Kasungu, Salima, and

Available online xxx

Neno districts in Malawi. In 67 sample plots covering 37 cultivated fields and 30 woodland plots, a total of 2481 trees were inventoried over 6 ha. Tree species documented were 56 in

Keywords:

Kasungu, 35 in Salima and 33 in Neno. The corresponding values of the Shannon diversity

Allometric equations

index and its standard error (SE) were 3.45 (0.01) for Kasungu, 2.78 (0.01) for Salima and 2.73

Biomass estimates

(0.01) for Neno. The three most dominant species in terms of biomass were Faidherbia albida

Carbon stocks

(47.8%), Piliostigma thonningii (11%), and Mangifera indica (9%), all found in cultivated fields.

Southern Africa

Large trees with diameter at breast height (DBH) >40 cm formed only 3% of the total

Species diversity

population inventoried in Salima, but held over 80% of the biomass. These high biomass trees were hardly found in Kasungu and Neno. Smaller trees (DBH < 10 cm) dominated all the sites, representing 93% of all the trees measured. These stock 14, 1, and 67% of the biomass in Kasungu, Salima, and Neno, respectively. The biomass estimates established in this study provide a useful reference against which future estimates can be compared, and sets a baseline for calculating changes in carbon stocks over time. ª 2014 Elsevier Ltd. All rights reserved.

1.

Introduction

The miombo woodlands represent a significant portion of tree cover in Malawi, although a greater part of these have been heavily modified [1]. Modification of the miombo woodlands has been attributed to land use and land use change and

associated resource utilization [2,3]. The miombo woodlands diversify the income of the rural smallholders, improve nutrition and food security [4], and are a major source of fuelwood and building materials for households [2,5]. Fuelwood comprises over 90% of the primary energy supply in Malawi, mainly utilized as firewood and charcoal. The supply of fuelwood in Malawi has declined over the years, because of

* Corresponding author. Jomo Kenyatta University of Agriculture and Technology (JKUAT), P.O. Box 62000-00200, Nairobi, Kenya. E-mail addresses: [email protected], [email protected] (S. Kuyah), [email protected] (G.W. Sileshi), [email protected] (J. Njoloma), [email protected] (S. Mng’omba), [email protected] (H. Neufeldt). 0961-9534/$ e see front matter ª 2014 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biombioe.2014.02.005

Please cite this article in press as: Kuyah S, et al., Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi, Biomass and Bioenergy (2014), http://dx.doi.org/10.1016/j.biombioe.2014.02.005

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b i o m a s s a n d b i o e n e r g y x x x ( 2 0 1 4 ) 1 e9

increasing population and the subsequent widespread overutilization of wood resources [5,6]. This has led to degraded forms of miombo woodlands that are characterized by land plowed for crop production, and unplowed sections used to provide fuelwood and other wood products [1]. At the same time farmers selectively retain or introduce trees of interest in fields cleared for agriculture [7]. Further, fast growing species such as eucalypts and pines have been grown in large scale plantations, woodlots and around homesteads in order to meet the increasing demand for fuelwood [7,8]. While clearing the woodlands for agriculture and settlement and extraction of wood for fuel are known to reduce tree cover, it is presently difficult to quantify losses or gains related to these processes, in terms of biomass and carbon stocks, due to lack of ground data and appropriate biomass equations. Awareness is growing about the potential to combat climate change by increasing trees in farming areas. Studies already show that adding trees to land traditionally used for agriculture raises productivity and secures both mitigation and adaptation benefits [9,10]. Further, trees in agricultural lands provide ecosystem services that support agricultural production, such as pollination, biological pest control, nutrient cycling, restoration fertility, and hydrological services [4]. These trees also sequester carbon from the atmosphere, contributing to climate change mitigation through reduction of atmospheric greenhouse gas concentrations. A study by Makumba et al. [11] showed that tree covered systems stock larger quantities of carbon than agricultural systems without trees. Despite these critical functions, the locations, cover, and nature of biomass of trees in agricultural landscapes are not well known in many countries [12]. This is contrary to the trees within forests, which are well described and their biomass is fairly documented. As a result, there is growing demand for information on the biomass content of trees outside forests. Allometric equations have been proposed for rapid assessments of tree biomass and these can be used without cutting down trees [13]. Allometric equations provide a useful link between field inventory and modeling, or remote sensingbased approximations and ground measurements. These equations have been widely used in forests assessment, such as those by IPCC [14] and few have been published for specific agricultural landscapes [15e18]. There exists also biomass equations developed for specific miombo ecoregions [19e23]. Considering the heterogeneous distribution of trees in miombo woodlands and associated land use systems, there is a need to identify suitable allometric equations that account for the diversity of trees in these mosaics prior to biomass estimation. This study aimed to establish landscape biomass and carbon stocks in three different miombo woodlands and associated land use systems in Malawi.

2.

Methodology

2.1.

Study site

The study was conducted in three 100 km2 benchmark sites located in (1) Kasungu and (2) Salima districts in the central region, and (3) Neno district in the southern region of Malawi.

The three locations (together with Ntchisi) constitute the Africa Soil Information Service (AfSIS) benchmark sites in Malawi build on the land degradation surveillance framework [24]. The AfSIS sites consist of 10 km  10 km blocks, each divided into 16 sub-blocks (clusters, 2.5 km  2.5 km) with 10 plots in each cluster. The blocks represent stratified random sample of landscapes in Africa south of the Sahara [24]. Va˚gen et al. [24] provide detailed information about the AfSIS sites. Kasungu block is located in the neighborhood of Kasungu National Park at latitude 12 480 S, longitude 33 210 E, with an elevation range of 1000e1200 m above sea level. Kasungu district receives mean annual rainfall of between 800 and 1600 mm. The rainfall is unimodal and occurs between November and April [25]. Mean annual temperature ranges from a maximum of 22e24  C to a minimum of 12e14  C. The vegetation around Kasungu consists of miombo woodland with trees of medium height and moderate grass cover. Marsh and dambo grasslands occupy poorly drained (wetland) areas. The major crops grown in the area are tobacco, maize and groundnuts. Salima block is located near the lakeshore plains at latitude 13 400 S, longitude 34 170 E, and an average elevation of 590 m. The district receives unimodal rainfall between October and May, w1000 mm per annum [25]. The mean annual temperature is 24.1  C with a mean maximum of 29.2  C and a mean minimum of 19.6  C. Most of the land in Salima is agricultural, dominantly used for production of maize, groundnuts, and cotton and rice. There are also patches of cassava, sorghum, sweet potatoes, and mangoes production fields. Tree cover is mainly Brachystegia, Faidherbia albida and interspersed fallows with dry grasslands. Neno district is located at latitude 15 310 S and longitude 34 410 E, with an elevation range of 250e500 m. The mean annual rainfall varies from 300 to 800 mm per annum, falling between November and March [25]. The mean temperature varies between 20 and 26  C. The topography of the district is largely mountainous and hilly. The natural vegetation is opencanopy miombo woodland interspersed with montane grassland [26]. There is low diversity of large canopy trees. The main cash crop in the area is citrus fruits, while maize is grown for subsistence.

2.2.

Field measurements

A systematic sampling design was adopted, where three plots (30  30 m) in each of the 16 clusters per site was selected for inventory (the plots are already randomized within the clusters) in Kasungu, two plots in each cluster in Salima, and one plot in each cluster in Neno. The variations arose after it became difficult to sample three plots in each of the clusters in each site due to terrain problems and absence of trees in selected plots. A total of 67 plots were sampled: 36 plots in Kasungu, 21 plots in Salima, and 10 plots in Neno. The systematic sampling design ensured an even spread of the sample plots throughout the woodland and associated farming areas and thus increased the chances of including all vegetation types in different land uses. The 30  30 m plot size was considered sufficient to capture floristic characteristics of the miombo vegetation, including the spatial variation of biomass in trees of different seizes and species.

Please cite this article in press as: Kuyah S, et al., Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi, Biomass and Bioenergy (2014), http://dx.doi.org/10.1016/j.biombioe.2014.02.005

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Diameters at breast height (DBH), measured at 1.3 m above the ground level was determined over-bark to the nearest 0.1 cm with a diameter tape, having the tape held horizontal and tight to the trunk. Trees forking long before breast height had each stem measured and recorded separately. Individuals of multi-stemmed trees that grow together as a clump were measured for DBH as separate trees. Trees forking around/just below 1.3 m were measured at breast height and the overall DBH of the bifurcations determined as the square root of the sum of squares of individual stems [27]. The DBH of heavily deformed and twisted trees was recorded just above the buckle. Stems with protruding dead wood, ant formations or climbing plants that would distort measurement were cleared prior to measurements. The species name was recorded and where scientific names could not be established in the field, the local name was provided by the local people who participated in data collection. The influence of management on trees through pruning, and selective harvesting, and presence of fire damage was noted. Abnormal stem formation including the presence of air layering, tree parasitism, stem forking, and multi-stems arising from coppicing or regeneration were recorded. The dominant land use category was deduced by observation and recorded as either woodland or crop land (cultivated or fallow land), with additional notes on the presence of disturbance in woodlands and the dominant crop in the farms.

2.3.

Data analysis

Tree data were grouped into nine DBH classes of <5, and above 5, 10, 15, 20, 25, 30, 35 and above 40 cm to provide the frequency of trees in each diameter class. The basal area (BA), expressed as the cross-sectional area of the stem at breast height was calculated for individual trees using the formula: BA ¼ p  DBH2/(10,000) [27]. The basal area for all the trees in each plot was summed and divided by the size of the plot to give basal area per hectare (m2 ha1). Tree density was calculated as the number of trees per unit area and reported as the number of stems per hectare. The relative frequency of tree species was calculated as the percentage of the frequency of one species over the sum of all frequencies documented. The ShannoneWeaver diversity

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index was used to describe the alpha diversity of tree species in each of the site and land use category. The different species were counted and diversity index calculated based on counts and proportions of individuals using GenStat 13th (VSN International Ltd.). Through a review of the literature allometric equations potentially suitable for estimating AGB in Malawi were identified, and the performance of published equations using harvested data was assessed. The mixed species equations by Mugasha et al. [19], AGB ¼ 0.1027  DBH2.4798, Chidumayo [20] AGB ¼ 0.0446  DBH2.765, and Kuyah et al. [15], AGB ¼ 0.0905  DBH2.4718, and the global equation by Brown [13], AGB ¼ 0.1359  DBH2.32 were considered useful, although noting that differences in ecological conditions of source data could be a major cause of bias in the application. The equation AGB ¼ 0.0446  DBH2.765 by Chidumayo [20] was chosen based on the conservativeness principle [28]. The equations, together with a mixed species equation developed for Malawi, AGB ¼ 0.1428  DBH2.271 in this study Kuyah et al. (unpublished) were applied to the inventory data to estimate AGB at a tree level. The different equations were used because trees in Salima exhibit a different geometry and size range compared to those in Kasungu and Neno. Estimates from these models were evaluated for bias (the average relative error) as: Bias (estimated  measured/measured)  100. Model selection was based on Akaike information criterion, AIC [29]. Tree level estimates were then up scaled to plot and landscape level estimates. The estimates of all the individual masses of each tree in a plot were summed in each plot, and then divide the plot area to express total biomass in megagrams per hectare (Mg ha1). Biomass estimates obtained were converted to carbon stocks using the default IPCC carbon fraction value of 0.47 [14].

3.

Results

3.1.

Tree sizes

A total of 2481 trees were inventoried over an area of 6 ha in 67 location covering of 37 cultivated fields and 30 woodland plots across the three sites. Tree size-class distribution profiles showed greater proportion of stems in the smallest size class,

Fig. 1 e The proportion of trees measured per diameter class and their share of biomass in (a) Kasungu, (b) Neno, and (c) Salima. Please cite this article in press as: Kuyah S, et al., Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi, Biomass and Bioenergy (2014), http://dx.doi.org/10.1016/j.biombioe.2014.02.005

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Table 1 e Summaries of plots surveyed, tree inventoried, the average plot basal area [SE] and tree species diversity (Shannon diversity index) in Kasungu, Salima and Neno. Site

Land use

No. of plots

Area (ha)

No. of trees

Mean(SE) DBH (cm)

Tree density

Basal area (m2 ha1)

Shannon index[SE]

Kasungu

Cropland Woodland All Cropland Woodland All Woodland

21 15 36 16 5 21 10

1.89 1.35 3.24 1.44 0.45 1.89 0.9

311 874 1185 139 182 321 975

7.1(0.38) 5.0(0.14) 5.6(0.15) 16.7(1.9) 6.7(0.46) 10.9(0.91) 4(0.07)

165 647 366 97 404 170 98

0.11[0.03] 0.19[0.05] 0.15[0.03] 0.53[0.16] 0.23[0.07] 0.46[0.13] 0.16[0.04]

3.26[0.01] 3.48[0.01] 3.45[0.01] 2.37[0.02] 2.58[0.02] 2.78[0.01] 2.73[0.01]

Salima

Neno

<5 cm and fewer stems in the intermediary and leading size classes (Fig. 1a, b, c). Trees found in farms (in Salima) were relatively bigger than those in the woodlands. Kasungu and Neno were dominated by smaller sized trees, generally. About 71% of the trees measured in Kasungu were multi-stemmed, composed of several clumped individuals. Plot basal area varied across the three sites (Table 1) ranging from 0.01 to 2.05 m2 ha1 with a mean (SE) of 0.46 (0.13) m2 ha1 in Salima. Over 50% of this was contributed by 3 plots with basal area 1 m2 ha1. In Neno, the plot basal area ranged between 0.02 and 0.42 m2 ha1 with a mean (SE) of 0.16 (0.04) m2 ha1. Similar to Salima, over 60% of the plot basal in Neno was contributed by 3 plots with basal area 2 m2 ha1. For Kasungu plots, basal area ranged from below 0.002 to 0.68 m2 ha1, with a mean (SE) of 0.15 (0.03) m2 ha1; 33% of which were contributed by 3 plots with BA >0.5 m2 ha1. The basal area in the woodland in Kasungu was higher compared to that of croplands, corresponding to the high tree density (Table 1). Conversely, the basal area in the cultivated plots in Salima was high, attributed to presence of large diameter trees in crop land than in the woodland plots. All plots surveyed in Neno were woodland, differing from natural to intensively disturbed plots.

3.2.

Tree species diversity

The number of species documented to species level totaled 56 in Kasungu, and 35 in Neno and 33 in Salima, indicating rich species diversity. Twenty-one species comprising 141 individuals were only identified by local names. The distribution of species with more than 10 individual per species: 30, 17 and 11 species in Kasungu, Salima and Neno, respectively, is presented in Fig. 2. Shannon diversity index shows that tree diversity was higher in Kasungu than Salima and Neno (Table 1). Shannon diversity index for the woodland was higher (3.58) compared to the crop land (3.38). Extrapolated species richness for the entire surveyed area using the first order and second order Jackknife and Bootstraps methods ranged between 105 and 137 species. Bootstraps estimate suggests that sampling captured about 88% of the species which Jackknife second order estimate indicate that the sampling effort captured 67% of the species. In terms of abundance, Julbernardia paniculata (9.9%), Diplorhynchus condylocarpon (7.3%) and Acacia macrothyrsa (7.1%) were the most frequent trees species in Kasungu; Commiphora spp (19%), Bauhinia petersiana (16.7%) and Margaretta rosea (9%) contributed the most number of stems in Neno; while Combretum fragrans (14.5%), Albizia harveyi (12.3%) and D.

condylocarpon (9.2%) dominated Salima (Fig. 2). In Kasungu, Syzygium cordatum (11.8%), Mangifera indica (8.8%), and Piliostigma thonningii (8.8%) were the most common trees encountered in farms, while woodlands were dominated by J. paniculata (12.5%) and D. condylocarpon (8.4%). In Salima, Cassia spectabilis (18.7%), F. albida (15.1%) and P. thonningii (15.1%) occurred frequently in farms whereas woodlands were dominated by A. harveyi (21.9%), C. fragrans (21.9%), and D. condylocarpon (15.1%).

3.3.

Tree biomass and carbon stocks

Large trees (DBH > 40 cm) were scarce in the landscape, though they stock most of the estimated biomass (Fig. 1aec). Trees with >40 cm DBH formed only 3.4% of the total population inventoried in Salima, but held over 80% of the biomass. These high biomass trees were hardly encountered in Kasungu and Neno. Conversely, low biomass trees (DBH < 10) dominated all the sites, representing 92, 70, and 99% of the trees surveyed in Kasungu, Salima and Neno, respectively. They hold 14, 1, and 67% of the biomass estimated in Kasungu, Salima and Neno, respectively. Table 2 shows estimates of biomass and carbon stocks and the respective 95% confidence intervals. Tree biomass was disproportionally distributed across the landscape with high biomass estimated in Salima and low biomass estimated in Neno. Only five woodland plots were found in Salima and it became difficult to calculate accurately the CI. The biomass estimates for this land use category are 10.5 (4.4e10.2), 8.7 (4.4e13), 8.9 (4.3e13.6), 8 (4.2e11.9), 10.4 (4.9e15.8) and 8.1 (3.7e12.6) Mg ha1 as determined by Chidumayo [20], Brown [13], Kuyah et al. [15], the equation developed in this study for Malawi, Mugasha et al. [19], and Ryan et al. [23], respectively. Plot tree biomass estimates ranged from below 1 Mg ha1 in all the three sites to slightly over 1 Mg ha1 in Kasungu and Neno, and 22.4 Mg ha1 in Salima. The AGB estimates were highest in the farmed plots in Salima and woodlands in Kasungu. Although fewer woodlot plots were surveyed in Kasungu, these had high tree density, while in Salima more farmed plots were encountered and these had the highest number of larger diameter individuals. The three most dominant species in terms of biomass across the landscape are F. albida (47.8%), P. thonningii (10.5%), and M. indica (8.9%). Although these trees occurred at low frequency: 0.8, 4.2 and 2.6%, respectively, they stock 67.2% of the total AGB estimated across the landscape. Site-wise, the three most dominant species in terms of biomass are: M.

Please cite this article in press as: Kuyah S, et al., Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi, Biomass and Bioenergy (2014), http://dx.doi.org/10.1016/j.biombioe.2014.02.005

b i o m a s s a n d b i o e n e r g y x x x ( 2 0 1 4 ) 1 e9

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Fig. 2 e The distribution of tree species with more than 10 individuals inventoried in cultivated fields (cropland) and woodlands found in (a) Kasungu, (b) Neno and (c) Salima.

indica, S. cordatum and A. macrothyrsa in Kasungu; F. albida, P. thonningii, and M. indica in Salima; and Commiphora spp, Pterocarpus rotundifolius, B. petersiana in the woodlands of Neno (Fig. 3aec); each of these group contributing 41.9, 88.1 and 48.2% of the biomass estimated in respective sites. The high biomass of these species is attributed both to large number of individuals and presence of bigger trees of these species in the landscape. Biomass and carbon across the landscape and in the two dominant land use categories are influenced by the type and size of tree species. Representative mean (and 95% CI) biomass and subsequent carbon stock estimated by the different equations varied across the different sites, and in Kasungu, across the two land use systems. Variance between estimates (indicated by wider 95% CI) was higher in Salima, where larger trees dominate the landscape and lower in Kasungu and Mwanza. The equation by Chidumayo [20] had the highest estimates in Kasungu and Salima, while the equation by Mugasha et al. [19] gave the highest estimates in Neno. Conservative estimates were obtained from the equation developed in this study, AGB ¼ 0.1428  DBH2.2471, and the equation by Brown [13] and Ryan et al. [23]. All the equations estimated

more carbon stocks for the woodlands in Kasungu and cultivated land in Salima. The equation developed for Malawi, AGB ¼ 0.1428  DBH2.2471 had lower AIC in most cases highlighting its suitability for the system. The mean relative errors across the landscape show that bias is small (<1) in the equation developed for Malawi, followed by the equation by Brown [13].

4.

Discussions

The landscape is dominated with small (low biomass) individuals. Large trees are scarce due to removal through regular harvesting for fuelwood and clearing land for crops [6,30]. This is evidenced by the abrupt drop in trees numbers above DBH of 5 cm, and the gradual decline of individuals with increase in size, especially in Kasungu and Neno. Kasungu is reputed for tobacco farming; farms are cleared of trees in favor of tobacco production and wood is abstracted from the woodlands for curing. Exotic species such as Eucalyptus were deliberately introduced in Kasungu to help in tobacco curing and making barns [8]. Neno is predominantly charcoal

Please cite this article in press as: Kuyah S, et al., Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi, Biomass and Bioenergy (2014), http://dx.doi.org/10.1016/j.biombioe.2014.02.005

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Site Biomass Kasungu

Salima Neno Carbon Kasungu

Salima Neno Bias (%)

Land use

Brown

Chidumayo

Kuyah

Malawi

Mugasha

Ryan

Crop land Woodland AIC Crop land AIC Woodland AIC

5.6 (1.9e9.2) 8.7 (4.4e13.0) 247.3 32.9 (10.7e55.2) 217.4 5.4 (2.0e8.9) 58.5

7.7 (2.5e15.8) 10.5 (4.4e16.6) 271.3 73.7 (13.9e133.5) 256.9 4.4 (1.3e7.5) 56.2

6.0 (2.1e9.9) 8.9 (4.3e13.6) 252.7 42.1 (11.6e72.5) 229.9 4.9 (1.8e8.1) 56.6

5.0 (1.8e8.3) 8.0 (4.2e11.9) 240.2 28.2 (9.6e46.8) 210.2 5.2 (2.0e8.4) 57.0

7.0 (2.4e11.6) 10.4 (4.9e15.8) 263.1 49.1 (13.4e84.7) 236.2 5.6 (2.0e9.3) 59.1

5.7 (1.9e9.4) 8.1 (3.6e12.6) 249.6 45.5 (10.8e80.2) 235.2 4.0 (1.3e6.6) 53.3

Crop land Woodland AIC Crop land AIC Woodland AIC

2.6 (0.9e4.3) 4.1 (2.1e6.1) 196.0 15.5 (5.0e25.9) 187.2 2.6 (1.0e4.2) 44.3 17.5

3.6 (1.2e6.0) 4.9 (2.1e7.8) 219.9 34.6 (6.5e62.7) 226.7 2.1 (0.6e3.5) 42.6 72.5

2.8 (0.9e4.7) 4.2 (2.0e6.4) 201.4 19.8 (5.5e34.1) 154.3 2.3 (0.8e3.8) 43.0 29.6

2.4 (0.8e3.9) 3.8 (2.0e6.0) 188.9 13.3 (4.5e22.0) 180.0 2.5 (0.9e4.0) 43.4 5.5

3.3 (1.1e5.4) 4.9 (2.3e7.4) 211.8 23.1 (6.3e39.8) 206.0 2.6 (0.9e4.4) 45.5 50.1

2.7 (0.9e4.4) 3.8 (1.7e5.9) 198.3 21.4 (5.1e37.7) 205.0 1.9 (0.6e3.1) 39.7 24.3

b i o m a s s a n d b i o e n e r g y x x x ( 2 0 1 4 ) 1 e9

Please cite this article in press as: Kuyah S, et al., Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi, Biomass and Bioenergy (2014), http://dx.doi.org/10.1016/j.biombioe.2014.02.005

Table 2 e Estimates (and 95% confidence intervals) of above-ground biomass and carbon stocks in Kasungu, Salima and Neno predicted by the allometric models of Brown [13], Chidumayo [20] and Kuyah et al. [15], Malawi (this study), Mugasha et al. [19] and Ryan et al. [23].

b i o m a s s a n d b i o e n e r g y x x x ( 2 0 1 4 ) 1 e9

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Fig. 3 e The distribution of tree species with more than 10 individuals measured in (a) Kasungu, (b) Neno, and (c) Salima and the proportion of estimated biomass stocked by the species.

producing area. Tobacco and charcoal production are a major cause of loss of trees in the miombo ecosystem, through clearing, harvesting and bushfire [1,2,20]. In contrast, Salima is dominated by a mix of large diameter (high biomass) trees in the farms and low biomass trees in the woodlands. The low biomass trees are widespread in other miombo eco-regions, attributed to depletion of large sized trees by harvesting, again for firewood and charcoal [20,23,31,32]. In Kasungu and Salima districts where croplands (cultivated fields) were surveyed, fruit trees such as M. indica and S. cordatum were among the most dominant tree species in terms of biomass. This indicates that fruit tree species are deliberately spared for income and food sources. Mango matures earlier in Salima than in high altitude areas (such as Lilongwe, Dedza etc.) and as such, there is a good market in Lilongwe for mangos from Salima. As such they are allowed to increase (in size and number) on crop fields. In Salima, F. albida has been the most dominant species in terms of biomass and this could be attributed to the fact that the species is known to improve soil fertility, and hence better crop yield. Therefore, farmers spare F. albida in their crop fields for better crop yields. This study reports generally high species diversity across the landscape. The diversity of tree species in the woodlands

was high but not significantly different from that of trees in farms, similar to findings in Mozambican miombo [33]. Both the woodlands and farms have a greater number of species present, and the individual trees present are also equitably distributed among the species. The diversity values reported in this study are higher than those given for open woodlands in Tanzania [34], Mozambique [33] and Zambia [20]. However, the values are lower than 4.27 reported in protected woodlands in Tanzania [35]. The higher number of diverse species in the farms and disturbed woodlands indicates presence of earlier regeneration. Field observations showed that regeneration occurs in the form of stump shoots (re-sprouts), root suckers, and seedlings. This is characteristic of agricultural landscapes, where fields are not completely cleared, and resprouts are selectively retained, and farmers manage natural regenerations or plant trees of interest in the landscape [7]. The disproportionate distribution of biomass and carbon stocks across the sites and the plots evaluated is attributed to the heterogeneity of trees in terms of species diversity and tree size. The high biomass in farms in Salima is attributed to presence of high biomass trees while the woodlands are dominated by moderate to low biomass trees, which occur in high numbers. The low biomass plots are mainly farms and

Please cite this article in press as: Kuyah S, et al., Estimating aboveground tree biomass in three different miombo woodlands and associated land use systems in Malawi, Biomass and Bioenergy (2014), http://dx.doi.org/10.1016/j.biombioe.2014.02.005

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b i o m a s s a n d b i o e n e r g y x x x ( 2 0 1 4 ) 1 e9

woodlands with past high level of disturbance (due to high human population), and shrub land with low biomass trees, particularly in Kasungu and Neno. Comparatively, there are other agricultural options as income sources in Salima such as fishing (the study area close to the lake), rice production as there is a valley where rice is produced. These may reduce heavy reliance on charcoal as a main income source. Tree carbon storage determined for the Malawian miombo woodlands is lower than figures given for the miombo in Mozambique and Tanzania [23,31,33]. The differences are attributed to the level of human disturbances, and partly to methodological differences. Landscape estimates of biomass can be significantly biased by the choice of allometric model, in this case, by up to 1.2, 2.7 and 20.9 Mg ha1 in Neno, Kasungu and Salima, using the equations Brown [13], Chidumayo [20], Kuyah et al. [15] and Mugasha et al. [19] and Ryan [23]. This is characteristic of tier II approach, where models are sourced from the literature and these may not be specific to the area of study [14]. For example the equations by Brown [13] and Chidumayo [20] includes DBH up to 40 cm. These equations overestimated the biomass of large trees which abound in Salima. In terms of the model fit and adequacy (based on AICc), Brown’s models was better than Chidumayo’s model that tended to over-estimate biomass stocks for the Kasungu and Salima sites. On the other hand for the Neno site, which was woodland, Ryan’s model appears to be the best (smaller AICc), followed by Chidumayo’s model. Although mean values of biomass and carbon stocks seem to differ between models, the 95% confidence intervals significantly overlap. Hence the true values could be within the confidence band specified by any of the models. This highlights the fact that reporting of mean values alone can be misleading as it does not take into account uncertainty in both measurement and model selection.

5.

Conclusion

A total of 92 species were documented to species level in the study. Estimated species diversity index was high in the woodlands than in cultivated fields, indicating that farming affects the diversity and abundance of tree species in the landscape. Tree biomass was disproportionally distributed across the landscape. Trees with diameter over 40 cm are fewer in the landscape, but hold most of the biomass in the landscape. These trees are mainly kept in cultivated fields because of their ecologic and economic importance. The biomass estimates established in this study provide a useful benchmark against which future estimates can be compared, and sets a baseline for calculating changes in carbon stocks over time.

Acknowledgment We would like to acknowledge financial support for this work from ICRAF. We thank Daniel Manduwa and Chisomo Gunda for coordinating data collection and the farmers who permitted us to work on their fields. We appreciate assistance from John Nyaga (ICRAF) with additional statistical analyses.

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