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Impact of decentralised forestmanagement on forest resourceconditions in TanzaniaL. Mbwambo a , T. Eid b , R. E. Malimbwi c , E. Zahabu c , G. C.Kajembe c & E. Luoga ca Tanzania Forestry Research Institute, P.O. Box 1854, Morogoro,Tanzaniab Department of Ecology and Natural Resource Management,Norwegian University of Life Sciences, P.O. Box 5003, NO-1432Ås,Norwayc Department of Forest Mensuration and Management, SokoineUniversity of Agriculture, P.O. Box 3013, Chuo Kikuu, Morogoro,TanzaniaVersion of record first published: 28 Jun 2012.

To cite this article: L. Mbwambo, T. Eid, R. E. Malimbwi, E. Zahabu, G. C. Kajembe & E. Luoga(2012): Impact of decentralised forest management on forest resource conditions in Tanzania,Forests, Trees and Livelihoods, 21:2, 97-113

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Impact of decentralised forest management on forest resourceconditions in Tanzania

L. Mbwamboa*, T. Eidb, R.E. Malimbwic, E. Zahabuc, G.C. Kajembec and E. Luogac

aTanzania Forestry Research Institute, P.O. Box 1854, Morogoro, Tanzania; bDepartment ofEcology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003,NO-1432As, Norway; cDepartment of Forest Mensuration and Management, Sokoine University ofAgriculture, P.O. Box 3013, Chuo Kikuu, Morogoro, Tanzania

Impacts of decentralised forest management on forest resource changes were assessed.Six contrasting forest reserves regarding management regimes, that is, Joint ForestManagement (JFM; in National Forest Reserves, owned by the State), CommunityBased Forest Management (CBFM; in village lands or general lands), and ordinarycentralized state management, were selected. The forest resources were assessed bymeans of systematic sample plot inventories. Number of stems, basal area, volume,biomass, and carbon ha21 were compared with results from previous studies in thesame reserves. Harvesting activities were also assessed as part of the sample plotinventories. In general, the results were somewhat ambiguous regarding the impacts ofdifferent management regimes. There was, however, some empirical evidenceindicating that JFM and CBFM performed better than the ordinary state management,although uncontrolled exploitation of the forest has continued under decentralisedforest management in the studied forests. The two regimes are promising forestdecentralisation models for Tanzania, but more research is needed to understand thefunctions of different governance structures and how they may facilitate sustainabilityin both forest use and livelihoods.

Keywords: Joint Forest Management; Community Based Forest Management;montane forest; miombo woodland; lowland forest; forest resource change

Introduction

Deforestation and forest degradation are major problems facing forest management in

Tanzania. As a result, Tanzania, like other developing countries, has in recent years

launched decentralized forest management initiatives (URT 1998). Ribot (2002) defined

decentralisation as any act in which central government formally cedes powers to actors

and institutions at lower levels in a political-administrative and territorial hierarchy.

Tacconi (2007) referred to decentralisation of forest management as the transfer of

authority and management functions related to resources from central to local

governments. It is assumed under the decentralisation theory that (i) democratic

decentralisation provide favourable conditions for institutionalisation and up-scaling of

community based forest management, (ii) benefits accrued from the forests make people

conserve the forests, and that (iii) reduced deforestation and forest degradation are

indicators for success of decentralisation (e.g., Ribot et al. 2006; Tacconi 2007).

ISSN 1472-8028 print/ISSN 2164-3075 online

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*Corresponding author. Email: lrmbwambo@yahoo.com; lawrence.mbwambo@taforitz.org

Forests, Trees and Livelihoods

Vol. 21, No. 2, June 2012, 97–113

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The umbrella term for decentralised forest management in Tanzania is Participatory

Forest Management (PFM), introduced in 1998, emphasising community empowerment

(URT 1998). PFM is legally supported by the Forest Act No. 14 of 2002, categorising

forests into reserved land, village land, private land, and forests in general lands (URT

2002). PFM is applied in two models (Table 1): Joint Forest Management (JFM) and

Community Based Forest Management (CBFM). JFM takes place in National Forest

Reserves, where communities are partners in management but the owner is the

government. Under this arrangement, the village enters into Joint Management

Agreements (JMAs) with the government for the management and use of the forest.

CBFM takes place in forests under village lands and general lands. Legal requirements for

setting CBFM includes registration of the village land, election of a Village Natural

Resource Committee, the development of management plans and bylaws, and the

declaration of the Village Land Forest Reserve. Under this institutional arrangement, the

villagers are the owners and managers of the forest (URT 2002).

Tanzania had 41.5 million ha of forests in 1990 and 37.5 million ha in 2000, while

currently, forests in Tanzania mainland cover 33.4 million ha (FAO 2010). About 18.3

million ha are reserved, while 17.0 million ha occur in general lands, where no proper

management is instituted and where a large part of degradation and deforestation is taking

place. There is also a significant forest degradation occurring even in the reserved forests

(Frontier—Tanzania 2005; Malimbwi et al. 2005). In the Eastern Arc Mountains for the

past 20 years, for example, carbon losses have been estimated at 34 million tons (Burgess

et al. 2009). Annual deforestation in Tanzania is estimated at 403,000 ha, corresponding to

an annual loss of 1.16% (FAO 2010), making Tanzania a source of about 100 million tons

of greenhouse gases annually (Milledge 2009).

The decrease in forest area has been related to population growth, economic

development, increased poverty, inadequate institutional arrangements, and insecure

land tenure, which facilitate open access to the forests (Petersen and Sandhovel 2001).

Increased commercial use of firewood, expansion of cultivated land, and the

implementation of socialism policy (Ujamaa) are additional factors that have contributed

to deforestation and forest degradation in the country (e.g., Angelsen et al. 1999).

The shift of policy toward decentralisation as seen in Tanzania is part of a prevailing

international trend. There is a growing international body of literature indicating that

decentralisation initiatives lead to better forest management (e.g., Agrawal and Yadama

1997; Kumar 2002; Gibson et al. 2005; Kobbail 2010). However, some of these studies

are based on literature review only, some are based on large-scale national data sources

only, and some focus on limited parameters only (e.g., species diversity), that is, the

Table 1. Overview of decentralised forest management in Tanzania.

Area (ha)Number ofVillages

Villages withSigned JMAs/-

Plans

PFM Regime 2006 2008 2006 2008 2006 2008

JFM 1,612,246 1,777,000 719 871 149 155CBFM 2,060,608 2,345,500 1,102 1,457 382 395Total 3,672,854 4,122,500 1,821 2,328 531 550

Note: Source: URT (2008).

98 L. Mbwambo et al.

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appropriateness of the empirical data describing successes or failures is questionable. Such

concerns have also been expressed more explicitly (see, e.g., Blaikie 2006).

Furthermore, in Tanzania, empirical evidence is required to evaluate effects of the

institutional changes done in 1998, and, possibly, to reorient policymakers in future

decisions. The overall aim of this study was, therefore, to assess the impact of decentralised

forest management on the forest resource conditions. Six contrasting forest reserves

regarding management regimes, that is, JFM, CBFM, and ordinary state and forest types,

that is, montane and lowland/miombo, were selected to explore the impact on forest

resources. The forest resources were assessed for number of stems, basal area, volume,

biomass, and carbon stock ha21, and changes for these parameters over time. Moreover,

harvests were also assessed.

Materials and methods

Study sites

The selected sites (Table 2) were Shagayu Forest Reserve (388 18’ E, 48 30’ S) under JFM,

Shume-Magamba Forest Reserve (388 15’ E, 48 40’ S) under ordinary state management,

and Sagara Forest Reserve (388 30’ E, 48 50’ S) under CBFM, all with montane forest

vegetation. Handeni Hill Forest Reserve (388 30’ E, 58 27’ S) was under JFM, partly with

miombo woodland and lowland forest; Kiva Hill Forest Reserve (388 06’ E, 58 28’ S) was

under ordinary state management with lowland forest; and Mgori Forest Reserve (358 05’

E, 48 45’ S) was under CBFM with miombo woodland. The changes in management and

tenure regimes for the Shagayu, Sagara, Handeni, and Mgori Forest Reserves took place in

2002, 1999, 1999, and 1996, respectively, while the reserves under ordinary state

management, namely Shume-Magamba and Kiva, have remained unchanged regarding

tenure and management regimes. The montane forest reserves are located between 1475–

1800m above sea level and receive around 1000mm annual rainfall, while the miombo

and lowland forests are located between 700–1600m above sea level and receive around

800mm annual rainfall. Number of adjacent villages, number of inhabitants in these

villages, and number of inhabitants per ha of forest among the reserves are varying

considerably (Table 2).

The selected forests went through similar historical and political events shaping their

management and the current resource conditions. Little is documented on the management

of the forests during the precolonial period, but it is known from anecdotes that their use

was limited to hunting and gathering and that communities identified forests based on their

role as sanctuaries rather than on their economic value (Conte 1999). Big sawmills were

introduced in Shagayu and Shume-Magamba to satisfy colonial as well as empire timber

needs. Pit sawing was also prevalent. Major timber tree species exploited in the montane

forest sites included Ocotea usambarensis Engl., Podocarpus spp, Entandrophragma

excelsum Sprague and Juniperus procera Hochst. ex Endl. (Conte 1999). Tree species

exploited for nontimber forest products included Catha edulis Forsk, Warbugia spp, and

Prunus africana (Hook. f.) Kalkman (Msuya 1998). The forests in the semi-arid sites share

the same historical background as forests in the montane site. Handeni and Kiva forests

were subjected to heavy exploitation to satisfy sawmills during the colonial era and

thereafter. Mgori was on general land and thus under an open access regime, which led to

over exploitation until the early 1990s, when proposals to gazette the forest began. Weak

control and monitoring for timber harvesting licenses led to overexploitation, which called

for institutional changes in the 1990s. Currently, due to lack of raw materials, most

sawmills have either been closed or operate below their rated capacities. Major exploited

Decentralised forest management and forest resource conditions 99

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Table

2.

Descriptionoftheforestreserves.

Forest

Reserve

ForestType

Managem

ent

Management

Change

Tenure

Change

Area

(ha)

No.of

Villages

Inhabitants

Inhabitants

per

haforest

Shagayu

Montane

JFM

Ordinarystate-State

JFM

State-JMAs

7830

13

27400

3.5

Shume-

Magam

ba

Montane

Ordinarystate

Nochange

Nochange

9284

17

59000

7.4

Sagara

Montane

CBFM

Private-CBFM

Private-Villageland

256

11850

7.2

HandeniHill

Miombo/Lowland

JFM

Ordinarystate-State

JFM

State-JMAs

544

38800

16.2

KivaHill

Lowland

Ordinarystate

Nochange

Nochange

655

37970

12.2

Mgori

Miombo

CBFM

Open

access-CBFM

General

land-V

illage

land

39361

510440

0.3

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tree species for timber in the semi-arid sites included Pterocarpus angolensis DC,

Pterocarpus tinctorius Welw., Afzelia quanzensis Welw., Brachystegia spiciformis

Benth., and Brachylaena huillensis O. Hoffm. (Malimbwi et al. 2005).

All studied forests with decentralised management (JFM or CBFM) had draft

management plans and bylaws awaiting signature by the central or local government

representatives for their implementation. Actors in JFM and CBFM arrangements include

the Forest and Beekeeping Division, District Forest Officers, as well as Village Executive

Officers and Village Assemblies that both play regulation and facilitation roles. Under

JFM, villagers are forest comanagers and users, while under CBFM they are owners,

managers, and users. Traders are users under both JFM and CBFM.

The management objectives for the JFM forests (Shagayu and Handeni Hill) focus on

biodiversity, conservation, and provision of environmental services such as water. Under

such management objectives, only collection of nontimber forest products is permitted in

accordance with some rules. The CBFM forest in the montane area (Sagara) is governed

by the same restrictions because of its ecological qualities. The objectives of the

management plan and bylaws for the CBFM forest in the semi-arid area (Mgori) are both

production and conservation. However, since the management plans and bylaws are not

approved, no harvesting is allowed here. The forests, under state ordinary management,

are under the sole management of the central (Shume-Magamba) and local (Kiva Hill)

governments, which are constrained with operational resources.

For all studied forest reserves, except Sagara, baseline forest data prior to the tenure

and management changes exist (Table 3). Baseline data for Shagayu and Shume-

Magamba were collected using a variable-area sampling technique where the nearest 20

trees to an objectively selected centre point with DBH $ 20 cm were identified and

measured for diameter as reported by Lovett (1996). In order to present data on a ha21

basis, the area was estimated by assuming the plot radius to be the distance from the plot

centre to a point located at half of the distance between the 20th and 21st tree (Lovett

1996). Lovett (1996) did not report on volume in Shagayu and Shume-Magamba. Baseline

data for Kiva and Handeni was derived from a conventional systematic sample plot

inventory where nested circular plots with radii of 5, 10, and 15m, arranged based on tree

diameter classes, were used (Malimbwi and Mugasha 2001; Malimbwi et al. 2005).

Baseline data for Mgori was collected using a systematic cluster design method with

circular nested plots similar to Kiva and Handeni (Malimbwi and Mwansasu 1994).

Despite the methodological differences, all studies reported forest parameters on a ha21

basis, enabling comparison with the current study.

Data collection

The data collection was finalised in 2010. Systematic sample plot inventories were carried

out for each forest reserve. The number of sample plots established in Shagayu, Shume-

Magamba, Sagara, Handeni, Kiva, and Mgori were 35, 36, 30, 44 (35 in miombo, 9 in

lowland), 30, and 72, respectively. In all the forests, the first plot was located randomly at

100m from the boundary of the forest, using a Global Positioning System (GPS).

Subsequent plots were located systematically at 200m intervals along transects, while the

distance between transects varied from 500m to 1,000m. The plot centre was located

using a GPS.

Concentric circular sample plots with radii 5, 10, and 15m were laid out. Tree

diameter at breast height (DBH) was measured as follows: in the 5m radius subplot all

trees with DBH of 5–9.9 cm; in the 10m subplot 10–19.9 cm and in the 15m subplot

Decentralised forest management and forest resource conditions 101

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Table

3.

Forestresourcebaselinedata.

ForestReserve

ForestType

Managem

ent

N(ha2

1)

G(m

2ha2

1)

V(m

3ha2

1)

Source

Shagayu

Montane

JFM

306^

56.7

55.0

^26.3

na

Lovett1996

Shume-Magam

ba

Montane

Ordinarystate

299^

86.7

53.3

^31.1

na

Lovett1996

Sagara

Montane

CBFM

na

na

na

–HandeniHill

Miombo

JFM

355^

144

11.2

^3.4

109.0

^44.6

Malim

bwiandMugasha2001

HandeniHill

Lowland

JFM

342^

103

10.9

^4.1

125.2

^64.9

Malim

bwiandMugasha2001

KivaHill

Lowland

Ordinarystate

1589^

1116

18.6

^22.9

176^

260.2

Malim

bwiet

al.2005

Mgori

Miombo

CBFM

988^

16

9.1

^0.2

43.0

^5.6

Malim

bwiandMwansasu

1994*

Note:Nisthenumberofstem

sha2

1,G

isthebasalarea

(m2ha2

1),andVisthevolume(m

3ha2

1);numbersafterþ

/-are95%

confidence

limits(productsofstandarderrorsofthemean

andt-valueat

95%

confidence

level).

*Confidence

limitswerecomputedfrom

tablesofplotmeansin

Malim

bwiandMwansasu

(1994).

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DBH $ 20 cm (e.g., Malimbwi and Mugasha 2001). Basal diameter (BD) of tree stumps

that were harvested within the 15m radius plot was also recorded. Three sample trees in

each plot from DBH classes of 5 # DBH , 10, 10 # DBH , 20, and DBH $ 20 cm,

respectively, were randomly selected and measured for BD, DBH, and height (H).

Data analysis

Data analyses involved computation of forest variables in terms of number of stems (N),

basal area (G), volume (V), biomass, and carbon ha21 for each plot. Number of stems and

basal area ha21 were computed using standard formulae. Volume for individual trees (v)

was based on equations with DBH and H as independent variables. In order to estimate

height for the trees that were not measured for height, height-diameter equations were

developed from the sample trees for Shagayu, Shume-Magamba, Sagara, and Mgori

(general form: ln(H) ¼ a þ b*ln(DBH), R2 from 0.65 to 0.85). For Handeni, existing

equations for miombo and lowland developed by Malimbwi and Mugasha (2001) were

applied. The same equations were also applied for Kiva, because of its nearby

geographical location.

Individual tree volumes (m3) for the miombo were calculated using the equation,

v ¼ 0.000048DBH1.445H1.7026 (R2 ¼ 0.97), developed by Chamshama et al. (2004).

Individual tree volumes for the lowland and montane forests were computed using the

equation, v ¼ 0.5gH; where g is tree basal area in m2. A form factor of 0.5, without

distinction of forest types, was applied.

Individual tree biomass (kg) for the miombo was estimated using the equation,

biomass ¼ 0.0625DBH2.533 (R2 ¼ 0.97), developed by Chamshama et al. (2004).

Individual tree biomass for montane and lowland forests was obtained by multiplying

tree volume with average tree basic density. According to Munishi and Shear (2004),

average basic density for montane forest tree species in Tanzania is 0.58 g cm23. This

average density was also applied for the lowland forests due to the ecological similarities

and the fact that no figures for lowland forests were available. Carbon was estimated by

multiplying tree biomass with a biomass-carbon ratio of 0.49 for all forest types.

Computation of volume (V), biomass, and carbon ha21 in each plot followed similar

procedures as for number of stems and basal area. Harvested tree volumes were estimated

by first developing DBH-basal diameter equations from sample trees. Second, heights of

harvested trees were estimated using equations described previously. Individual tree

volumes for montane, lowland, and miombo harvesting were estimated using the same

equations as for standing trees.

Results

The number of stems varied considerably among the forest reserves, ranging from 599 to

2028 ha21 for the montane forests, from 462 to 1155 ha21 for miombo woodland, and

from 630 to 677 ha21 for the lowland forests (Table 4). Although the number of stems

ha21 (N) varied among the forest reserves, their distribution in DBH classes in general

portrayed inverse J-shaped curves, a typical characteristic of naturally regenerated forests

(Figure 1). The large number of trees in the smaller diameter classes is a clear indication of

appropriate regeneration conditions in Shagayu (montane forest, JFM) and in Mgori

(miombo woodland, CBFM). Basal area was generally higher for the montane forests

(ranging from 17.4 to 37.6 m2 ha21) than for miombo/lowland forests (ranging from 12.0

Decentralised forest management and forest resource conditions 103

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Table

4.

Presentforestresources.

ForestReserve

ForestType

Managem

ent

N(ha2

1)

G(m

2ha2

1)

V(m

3ha2

1)

Biomass(tonsha2

1)

Carbon(tonsha2

1)

Shagayu(n

¼35)

Montane

JFM

2028^

287

37.5

^7.1

506.4

^189

293.7

^109.5

143.9

^53.7

Shume-Magam

ba(n

¼36)

Montane

Ordinarystate

599^

123

17.4

^5.1

171.0

^64.7

99.2

^37.6

48.6

^18.4

Sagara(n

¼30)

Montane

CBFM

633^

149

37.6

^5.7

572.0

^108.6

331.8

^62.9

162.6

^30.9

Handeni(n

¼35)

Miombo

JFM

462^

97

12.0

^1.4

143.2

^25.9

61.0

^9.3

29.91^

9.3

Handeni(n

¼9)

Lowland

JFM

677^

315

14.9

^3.2

152.2

^38.9

88.3

^22.5

43.3

^11.1

KivaHill(n

¼30)

Lowland

Ordinarystate

630^

141

16.6

^2.9

157.8

^49.7

93.1

^29.3

45.6

^14.4

Mgori(n

¼72)

Miombo

CBFM

1155^

208

15.1

^1.8

90.8

^10.6

59.1

^7.3

29.0

^3.6

Note:Nisthenumber

ofstem

sha2

1,Gisthebasalarea

(m2ha2

1),andVisthevolume(m

3ha2

1);Numbersafterþ

/-are95%

confidence

limits(productsofstandarderrorsofthe

meanandt-valueat

95%

confidence

level).

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to 16.6 m2 ha21) (Table 4). Similar patterns were also observed for volume, biomass, and

carbon stock.

Table 5 compares forest resource changes. For the montane forest reserve in Shagayu

(JFM), there was a decrease in the number of stems for trees with DBH $ 20 cm of 5 stems

ha21 yr21, while the corresponding decrease in Shume-Magamba (Ordinary State) was 13

stems ha21 yr21. For the basal area, the decrease was 2.3 and 2.8m2 ha21 yr21,

Shagayu

0100200300400500600700800

Diameter class (cm)

Stem

s pe

r ha

5-9.9

10-1

9.9

20-2

9.9

30-3

9.9

40-4

9.9 ≥50

Stem

s pe

r ha

0100200300400500600700800 Handeni Hill miombo

Diameter class (cm)

5-9.9

10–1

9.9

20–2

9.9

30–3

9.9

40–4

9.9 ≥50St

ems

per

ha

0100200300400500600700800 Kiva

Diameter class (cm)

5–9.9

10–1

9.9

20–2

9.9

30–3

9.9

40–4

9.9 ≥50

Stem

s pe

r ha

0100200300400500600700800

Handeni Hill lowland

Diameter class(cm)

5–9.9

10–1

9.9

20–2

9.9

30–3

9.9

40–4

9.9 ≥50

Stem

s pe

r ha

0100200300400500600700800 Sagara

Diameter class (cm)

5-9.9

10–1

9.9

20–2

9.9

30–3

9.9

40–4

9.9 ≥50

Stem

s pe

r ha

0100200300400500600700800 Mgori

Diameter class (cm)

5–9.9

10–1

9.9

20–2

9.9

30–3

9.9

40–4

9.9 ≥50

Stem

s pe

r ha

Shume

0100200300400500600700800

Diameter class (cm)

5-9.9

10-1

9.9

20-2

9.9

30-3

9.9

40-4

9.9 ≥50

Figure 1. Distribution of stems ha21 by diameter classes.

Decentralised forest management and forest resource conditions 105

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Table

5.

Forestresourcechanges.

FirstandLast

Annual

Change

ForestReserve

ForestType

Managem

ent

Inventory

Year

N(ha2

1)

G(m

2ha2

1)

V(m

3ha2

1)

Shagayu*

Montane

JFM

1996–2010

25

22.3

na

Shume-Magam

ba *

Montane

Ordinarystate

1996–2010

213

22.8

na

Sagara

Montane

CBFM

22010

na

na

na

Handeni

Miombo

JFM

2001–2010

12

0.1

3.8

Handeni

Lowland

JFM

2001–2010

37

0.4

3.0

KivaHill

Lowland

Ordinarystate

2005–2010

2192

20.4

23.6

Mgori

Miombo

CBFM

1994–2010

10

0.4

3.0

Note:*Only

treeswithDBH$

20cm

areusedforcomparisonwithbaselinedata.

106 L. Mbwambo et al.

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respectively. There was an increase in the number of stems in Handeni (JFM) and Mgori

(CBFM). An annual increase in the number of stems was higher in Handeni, especially for

the lowland forest. For Kiva Hill (Ordinary State), there was a very large decrease in the

number of stems of 192 ha21 yr21, which translated into a slight decrease in basal area and

volume, meaning that the decrease in the number of stems mainly affected small stems.

In montane forests, more stems were harvested (Table 6) in Sagara (CBFM) and

Shume-Magamba (Ordinary State) forests than in Shagayu (JFM). However, for basal area

and volume ha21, harvests were higher in Shume-Magamba than in Shagayu and Sagara.

In the miombo and lowland forests, more stems were harvested from Handeni (JFM) than

in Kiva Hill (Ordinary State) and Mgori (CBFM). Just like removed stems, there were

more basal area and volume removals in Handeni than in Kiva and Mgori. The lowest

number of stems, basal area, and volume harvested were recorded in Mgori.

Discussion

The overall aim of this study was to assess the impact of decentralised forest management

on the forest resource base in Tanzania. Contrasting forest reserves regarding management

regimes, that is, JFM, CBFM, and ordinary state, were selected. The differences in the

results among the forest reserves, however, could be attributed to several factors:

ecological site specific conditions like productivity, forest conditions before the

management and tenure changes that took place, site specific socioeconomic conditions

over the past years and, finally, a possible impact of the actual management regime

changes. In general, it is hard to distinguish among these factors.

Current resource conditions and changes

Although the number of stems varied considerably among the forest reserves (Table 4),

they were in line with previously reported results in Tanzania for montane forest (e.g.,

Maliondo et al. 2000; Mpanda et al. 2011), miombo woodland (e.g., Chamshama et al.

2004), and lowland forest (e.g., Zahabu 2008). Basal area, volume, biomass, and carbon

ha21 were, as expected, generally higher for the montane forests than for the

miombo/lowland forests, but these parameters also were in line with previous studies.

The differences among the forest reserves regarding changes seen in Table 5 were

large. There are very few previous forest growth studies in Tanzania, and usually

quantifications of changes over time include both growth and harvests, like in the present

study. Mpanda et al. (2011) reported on overall positive changes for the number of trees

and basal area over a period of eight years in the Amani Forest Reserve (montane forest).

For both montane forests with baseline data in the present study, there were negative

changes for these parameters. The growth rates in the miombo forest sites, except for Kiva,

were within the range of what could be expected based on previous studies; Malimbwi

et al. (1994) and Temu (1979) reported annual volume growth rates of 7.4m3 ha21 yr21

and 1–2m3 ha21 yr21, respectively, for miombo.

Impact of tenure changes

As stated earlier, it is hard to distinguish among different factors influencing the forest

conditions of the reserves. However, among the montane forests, Shagayu (under JFM

since 2002) and Sagara (under CBFM since 1999) seem to perform better than Shume-

Magamba (under ordinary state regime). In Shagayu the changes, although negative, were

Decentralised forest management and forest resource conditions 107

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Table

6.

Harvests.

ForestReserve

ForestType

Managem

ent

N(ha2

1)

G(m

2ha2

1)

V(m

3ha2

1)

Shagayu(n

¼35)

Montane

JFM

52^

42

0.5

^0.4

4.3

^4.6

Shume-Magam

ba(n

¼36)

Montane

Ordinarystate

76^

28

3.4

^1.4

35.6

^17.6

Sagara(n

¼30)

Montane

CBFM

83^

31

1.2

^0.9

15.2

^14.0

Handeni(n

¼35)

Miombo

JFM

95^

28

1.5

^0.4

12.2

^6.4

Handeni(n

¼9)

Lowland

JFM

158^

83

2.5

^1.6

22.0

^16.2

KivaHill(n

¼30)

Lowland

Ordinarystate

84^

26

1.3

^0.6

13.2

^8.8

Mgori(n

¼72)

Miombo

CBFM

42^

39

0.6

^0.3

2.8

^1.7

Note:Numbersafterþ

/-are95%

confidence

limits(productsofstandarderrors

ofthemeanandt-valueat

95%

confidence

level).

108 L. Mbwambo et al.

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rather small (Table 5), the harvest level was relatively low (Table 6), and there was a large

number of trees in the smaller diameter classes indicating appropriate regeneration

conditions (Figure 1). The Shume-Magamba Forest Reserve clearly showed poorer

performance than Shagayu, that is, the negative changes were larger (Table 5), the harvest

level was higher (Table 6), and the diameter distribution pattern was less appropriate for

regeneration (Figure 1). In Sagara, although there was no baseline data, there is no doubt

the harvesting level was much lower than in Shume-Magamba.

The general decrease in forest resources observed for Shagayu and Shume-Magamba

may be seen as an indication of forest degradation, especially for Shume-Magamba under

ordinary state management. In general, this has probably been contributed by forest

disturbances following an inadequate capacity by the government to monitor the forest due

to shortage of staff and financial resources in recent years. In addition, the Shume-

Magamba Forest Reserve has been frequently affected by forest fires, and by illegal

logging, especially after a proposal in 2010 to change its status into a nature reserve

(UNESCO 2010). To the people living around Shume-Magamba, a nature reserve is like a

national park, which normally is protected by using guns. People, therefore, envisage very

limited benefits from the forest in the future, and as such, they decided to overharvest

before the forest becomes a nature reserve. This behaviour is confirmed by the results from

this study (Table 6).

The two montane forests with decentralized management (Shagayu and Sagara) were

under intensive selective harvesting of Ocotea usambarensis until 1989, when the

government imposed a logging ban in all catchment forests in the country (Persha and

Blomley 2009). The fact that Shagayu had more trees in the smallest diameter classes

(Figure 1) as compared to Shume-Magamba may be attributed to high regeneration after

the logging ban. A similar pattern was not seen in Sagara, however, probably due to the

existence of large canopy trees suppressing regeneration underneath.

For the forest reserves with lowland forest and miombo, the best general performance

was observed in Mgori, under CBFM, that is, the changes were positive (Table 5), the

harvest level was low (Table 6), and the number of trees in the smallest diameter classes

was high (Figure 1). Mgori was previously under general lands, and thus under an open

access regime, but it has been under CBFM since 1996. The fact that there are

improvements here is an indication of a successful forest management program.

Discussions with the forest committee members revealed that even the number of wild

animals has increased and that elephants in particular have been visiting the villages

several times.

Likewise, the Handeni Forest Reserve under JFM performed relatively well, showing

positive changes (Table 5). The harvesting level, however, was high, especially in the

lowland forest part (Table 6), despite the positive change in stocking parameters. Lack of

financial resources to conduct patrols was generally claimed to be the reasons for increased

illegal tree harvesting even under the decentralized management regime. This challenge,

coupled with high human population density and accordingly a high number of persons per

ha of forest (see Table 2), probably explains the high harvesting level in Handeni. Kiva,

under ordinary state management, was generally performing relatively poorly with large

negative changes in stocking parameters (Table 5) and a relatively high harvesting level

(Table 6).

Based on the present study, it is not possible to distinguish between JFM and CBFM in

terms of impact of management regime. Shagayu under JFM andMgori under CBFMwere

performing much better than Sagara and Handeni, which are under CBFM and JFM,

respectively. The fact that the population density (Table 2) varies considerably among the

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forests under decentralized management also indicates that one should be careful in

generalizing the results. The population density is obviously a factor that may influence

the results.

Policy implications

Based on these six forests, there is some evidence indicating that the two decentralised

approaches (JFM and CBFM) perform slightly better than the centrally based approach.

Other scholars have previously reported positive impacts of these approaches on forest

conditions in Tanzania (Kajembe et al. 2003; Blomley et al. 2008; Lund and Treue 2008;

Zahabu 2008; Persha and Blomley 2009), but all of them with limited empirical evidence.

The two forests under ordinary state management, Shume-Magamba and Kiva, are

practically under open access. Under such a situation, according to Ostrom (2000),

resource users are short-term profit maximizers, and anyone may enter the forest and

utilize forest products. Users in this way obtain property rights particular to the products

harvested and sold in open competitive markets. The availability of markets and high

demand on wood products around these forests were found to be incentives for the

continued exploitation of the forests.

In general, illegal harvesting activities have occurred in all study forests, regardless

of management regime. As previously stated, lack of financial resources to conduct

patrols were claimed to be a reason for increased illegal tree harvesting, also in the

forests under the two decentralized regimes. There are no budgets from the districts to

support village forest management activities. The only source of revenues is from fines

instituted to offenders. This is in contradiction with the decentralisation by devolution

rhetoric, where the central government must transfer authority over forest resource

management and benefits to local actors (Campbell et al. 2003; Tacconi 2007).

According to Acheson (2006), in cases where central government officials are reluctant

to cede powers to local communities, local level forest management efforts are likely

to fail. This is evident from this study, where JFM and CBFM can hardly be

considered as working appropriately given the capabilities of forest management

institutions at the village level. Lack of legitimate local forest management institutions

is also a problem noted in the management of woodlands elsewhere in southern Africa

(Campbell et al. 2003).

A reason why the forest reserves with decentralised management in this study have

shown ambiguous results regarding the impacts of the management regime could be that

they still are in the initial stages of decentralised management. This follows the argument

by Dev et al. (2003) that analysing impacts is also time dependent, as degraded forests

need some years of controlled use to become productive again, and that communities have

their own pace of institutional development, that is, it may take some years to be

adequately organized to manage forests successfully. It is, therefore, important that the

present study is followed up in the future to deal with the possible impacts of such

temporal aspects.

The documentation of the forest resource changes related to different forest

management systems provided in the present study, which was lacking in Tanzanian as

well as in international literature, may still be valuable to policymakers. Apart from

studying potential forest resource improvements, there is also a need to undertake in-depth

forest governance and livelihoods research related to different management and tenure

regimes in Tanzania. Although JFM and CBFM are promising forest decentralisation

models for the country, more knowledge is needed to understand the functions of different

110 L. Mbwambo et al.

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governance structures and how they may facilitate sustainable forest use and an

appropriate livelihood for the people.

Conclusions

The results of the present study were ambiguous regarding the impacts of different

management and tenure regimes on forest resource changes. There was, however, some

evidence indicating that the two decentralised approaches (JFM and CBFM) performed

better than the ordinary centralised state approach. The two decentralised approaches have

the potential to meet the general Participatory Forest Management goal of improving the

forest resource conditions. More research is still needed to understand the functions of

governance structures and how they may facilitate sustainable forest use and livelihoods.

Acknowledgements

This study was funded under the project “Assessing the Impact of Forestland Tenure Changes onForest Resources and Rural Livelihoods in Tanzania” (NUFUTZ-2007/10226) under the Tanzania-Norway NUFU Programme (2007–2011). Our study would not have been possible without thecooperation of the Handeni, Lushoto, and Singida District Council Natural Resources Offices staff,Village Governments, and Village Forest Committees. We are grateful to Messrs C. Balama,W. Mugasha, and I. Hussein of Tanzania Forestry Research Institute (TAFORI) for their assistanceduring data collection. Finally, we are thankful to the editor and two anonymous reviewers for theiruseful comments and suggestions.

References

Acheson JM. 2006. Institutional failure in resource management. Ann Rev Anthro. 35:117–134.Agrawal A, Yadama GN. 1997. How do local institutions mediate market and population pressures

on resources? Forest Panchayats in Kumaon. India. Dev Change. 28:435–466.Angelsen A, Katemansimba Shtindi EF, Aarrestad J. 1999. Why do farmers expand their land into

forests? Theories and evidence from Tanzania. Env Dev Econ. 4:313–331.Blaikie P. 2006. Is small really beautiful? Community-based natural resource management in

Malawi and Botswana. World Dev. 34:1942–1958.Blomley T, Kerstin P, Isango J, Zahabu E, Ahrends A, Burgess N. 2008. Seeing the wood for trees:

An assessment of the impact of participatory forest management on forest condition in Tanzania.Oryx. 42:380–391.

Burgess ND, Clairs T, Hagelberg N, Haule C, Kilahama F, Kilawe E, Lyatuu G, Sekhram N. 2009.Saving forests to reduce global warming: The United Nations REDD programme in Tanzania.Arc Journal. 24:11–14.

Campbell BM, Shacleton S, Wollenberg E. 2003. Overview: Institutional arrangements formanaging woodlands. In: Kowero G, Campbell BM, Sumalia UR, editors. Policies andmanagement structures in woodlands of southern Africa, Jakarta 10065, Indonesia: Centre forInternational Forestry Research (CIFOR). p. 9–15.

Chamshama SAO, Mugasha AG, Zahabu E. 2004. Stand biomass and volume estimation for miombowoodlands at Kitulangalo. Morogoro, Tanzania. South African For J. 200:49–60.

Conte CA. 1999. The forest becomes desert: Forest use and environmental change in Tanzania’sWest Usambara Mountains. Land Deg Dev. 10:291–309.

Dev OP, Yadav NP, Springate-Baginski O, Soussan J. 2003. Impacts of communal forestry andlivelihoods in the middle hills of Nepal. J For Live. 3:64–77.

Food and Agriculture Organisation of the United Nations (FAO). 2010. Global forest resourcesassessment 2010. Main report. Rome (Italy): Food and Agriculture Organisation of the UnitedNations, Forestry Paper No. 163. 378 p.

Frontier—Tanzania. 2005. Uluguru component biodiversity survey 2005 (Volume III): UluguruNorth Forest Reserve. Society for Environmental Exploration and the University of Dar Es

Decentralised forest management and forest resource conditions 111

Dow

nloa

ded

by [

Nor

ges

Lan

dbru

ksho

egsk

ole]

at 1

3:55

31

Oct

ober

201

2

Salaam; CARE—Tanzania; Conservation and Management of the Eastern Arc Mountain Forests(CMEAMF) Uluguru Component, Forestry and Beekeeping Division of the Ministry of NaturalResources and Tourism, URT/01/G32, Dar es Salaam, Tanzania.

Gibson CC, Williams JT, Ostrom E. 2005. Local enforcement and better forests. World Dev.33:273–284.

Kajembe GC, Monela GC, Mvena ZSK. 2003. Making community-based forest management work:A case study of Duru-Haitemba Village Forest Reserve, Babati Tanzania. In: Kowero G,Campbell BM, Sumalia UR, editors. Policies and management structures in woodlands ofsouthern Africa. , Jakarta 10065, Indonesia: Centre for International Forestry Research (CIFOR).p. 16–27.

Kobbail AAR. 2010. Collaborative management for sustainable development of natural forests inSudan: Case study of Elrawashed and Elain natural forests reserves. Int J Soc For. 3:10–133.

Kumar S. 2002. Does “participation” in common pool resource management help the poor? A socialcost-benefit analysis of joint forest management in Jharkhand. India. World Dev. 30:763–782.

Lovett JC. 1996. Elevation and latitudinal changes in tree associations and diversity in the EasternArc Mountains of Tanzania. J Trop Ecol. 12:629–650.

Lund JF, Treue T. 2008. Are we getting there? Evidence of decentralised forest management fromthe Tanzanian miombo woodlands. World Dev. 36:2780–2800.

Malimbwi RE, Mugasha AG. 2001. Reconnaissance inventory of Handeni Forest Reserve in Tanga,Tanzania. Dar Es Salaam (Tanzania): Forest and Beekeeping Division, Ministry of NaturalResources and Tourism. p. 28.

Malimbwi RE, Mwansasu S. 1994. Mgori Forest Reserve mini inventory. Dar Es Salaam (Tanzania),Report submitted to ORGUT p. 24.

Malimbwi RE, Solberg B, Luoga E. 1994. Estimate of biomass and volume in miombo woodland atKitulangalo Forest Reserve. Tanzania. J Trop For Science. 2:230–242.

Malimbwi RE, Shemweta DTK, Zahabu E, Kingazi SP, Katani JZ, Silayo D. 2005. Dar Es Salaam(Tanzania): Forestry and Beekeeping Division Ministry of Natural Resources and Tourism.Forest inventory report for Handeni/Kilindi Districts Tanga p. 97.

Maliondo SMS, Malimbwi RE, Constantine E, Zahabu E. 2000. Fire impact on population structureand diversity of tree species in West Usambara Camphor zones forest. J Trop For Science.12:472–481.

Milledge S. 2009. Getting REDDy in Tanzania: Principles, preparations and perspectives. Arc J.24:3–6.

Mpanda MM, Luoga EJ, Kajembe GC, Eid T. 2011. Impact of forestland tenure changes on forestcover, stocking and tree species diversity in Amani Nature Reserve, Tanzania. For Trees Live.20:215–230.

Msuya TS. 1998. Uses and indigenous conservation methods of wild plants: A case of WestUsambara Mountains, Tanzania [MSc thesis (MNRSA)]. NORAGRIC: Agricultural Universityof Norway, Aas Norway. p. 98.

Munishi PTK, Shear TH. 2004. Carbon storage in afromontane rain forests of Eastern Arc Mountainsof Tanzania: Their net contribution to atmospheric carbon. J Trop For Science. 16:78–93.

Ostrom E. 2000. Reformulating the commons. Swiss Pol Science Rev. 6:29–52.Persha L, Blomley T. 2009. Management decentralisation and montane forest conditions in

Tanzania. Conserv Bio. 23:1485–1496.Petersen L, Sandhovel A. 2001. Forestry policy reform and the role of incentives in Tanzania. For

Pol Econ. 2:39–55.Ribot JC. 2002. Democratic decentralisation of natural resources: Institutionalising Popular

Participation. World Resource Institute, p. 30.Ribot JC, Agrawal A, Larson A. 2006. Recentralising while decentralising: How national

Governments re-appropriate forest resources. World Dev. 34:1864–1886.Tacconi L. 2007. Decentralisation, forests and livelihoods: Theory and narrative. Global Env

Change. 17:338–348.Temu AB. 1979. Estimation of millable timber volume in miombo woodlands. Dar es Salaam

(Tanzania): University of Dar Es Salaam, Division of Forestry Record No. 7. 11 p.United Nations Education, Scientific and Cultural Organisation (UNESCO). 2010. Nomination of

properties for inclusion on the World Heritage list serial nomination: Eastern Arc MountainsForests of Tanzania. Dar es Salaam (Tanzania). United Republic of Tanzania, Ministry ofNatural Resources and Tourism. p. 135.

112 L. Mbwambo et al.

Dow

nloa

ded

by [

Nor

ges

Lan

dbru

ksho

egsk

ole]

at 1

3:55

31

Oct

ober

201

2

United Republic of Tanzania (URT). 1998. The National Forest Policy. Dar-es-Salaam (Tanzania):Government Printer. p. 59.

United Republic of Tanzania (URT). 2002. The National Forest Act No. 14 of 2002. Dar-es-Salaam(Tanzania): Government Printer, Ministry of Natural Resources and Tourism. p. 174.

United Republic of Tanzania (URT). 2008. Participatory Forest Management: Facts and figures.Dar es Salaam (Tanzania): Forest and Beekeeping Division, United Republic of Tanzania. p. 12.

Zahabu E. 2008. Sinks and sources: A strategy to involve forest communities in Tanzania in GlobalClimate Policy [PhD dissertation], Enschede, The Netherlands: University of Twente. p. 235.

Decentralised forest management and forest resource conditions 113

Dow

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