Forest Degradation in Tanzania: A Systematic Literature Review

Abstract

Forest degradation is a process in which the biological diversity of a forest area is permanently reduced due to one or more factors. Forest degradation continues at an alarming rate, contributing significantly to the loss of biodiversity around the world. This chapter presents the findings of a systematic literature review of forest degradation in Tanzania. The PRISMA method was employed in the study’s search, document selection, and data analysis. There were more studies more recently due to the increasing interest in forest degradation as an important aspect of forest management. Most terms are mentioned less frequently in the document title than in the document as a whole, indicating research gaps for the research topics represented by the research terms. Some terms are covered less than expected, given their significance in forest degradation. The estimated annual volume removals exceed the estimated mean annual increment, indicating forest management in Tanzania is not sustainable. The most mentioned region was Dar es Salaam, while the list mentioned was Rukwa. It is expected that forest stakeholders will find the analysis presented in this study useful. Furthermore, the stakeholders will find interest in addressing temporal, spatial, and thematic research gaps highlighted in this chapter.

1. Introduction

Tanzania’s forests cover approximately 45.7 million ha, or about 55 % of the country’s total land area, as of 2022 [1]. The forests provide essential goods and services [2]. In addition to providing economic benefits, Tanzanian forests and other woodlands serve as important habitats for a variety of animals and plant species. Over 10,000 plant species have been identified in these forests, with 305 being classified as threatened by the IUCN Red List and 276 being classified as endangered [3]. Despite the valuable goods and services that forests provide, high rates of deforestation and forest degradation persist [4, 5]. Forest degradation is a subset of the larger issue of land degradation. Deforestation and forest degradation continue at alarming rates, contributing significantly to the ongoing loss of biodiversity around the world. According to the last Global Forest Resources Assessment report (2020), deforestation has occurred at a rate of 469,000 ha per year. Forest degradation is defined as changes within the forest that have a negative impact on the structure or function of the stand and/or site, reducing the capacity to supply products and/or services [6]. Forest degradation alone contributed 25% of total emissions from deforestation and forest degradation even exceeding emissions from deforestation in some countries [7].

The major causes of forest degradation in the unreserved forest include heavy pressure from agricultural expansion, livestock grazing, development of human settlements, overgrazing, firewood and charcoal production, uncontrolled fires, timber extraction, development of infrastructure/industry, refugees, and most recently, the introduction of large-scale agriculture for bio-fuel production [5]. Several studies in Tanzania have investigated the various drivers, trends, and methods for assessing deforestation and forest degradation [4, 8, 9].

To sustainably provide goods and services from the forests, effective forest management initiatives are required. If these initiatives are implemented, Tanzania may ultimately be able to reduce emissions caused by deforestation and forest degradation. This would help it meet its emission reduction targets. This chapter discusses the findings of a systematic literature review of forest degradation in Tanzania, in terms of (1) trends in research on forest degradation, (2) research topics covered and gaps in research, (3) extent of forest degradation, (4) drivers of forest degradation, (5) methods of monitoring and assessment of forest degradation, (6) institutions involved in efforts to control forest degradation, and (7) spatial distribution of studies on forest degradation.

2. Methodology

2.1 Data sources

The research method used in this study was a systematic literature review. The PRISMA (Preferred reporting items for systematic reviews and meta-analyses) method was used to select the articles [10]. Scopus and Google Scholar were used to search for articles. Table 1 shows the keywords used to search and retrieve published articles in the Scopus and Google Scholar databases.

Keywords

Area restriction

Forest OR “Forest degradation” OR methods OR “forest quality” OR “qualitative OR quantity” OR quantitative OR “forest health” OR “land degradation” OR species OR tree species; animal species OR “forest species” OR “plant species” OR diversity OR “forest diversity” OR “species diversity” OR “plant diversity” OR “animal diversity” OR “ tree diversity OR forest harvesting OR “forest inventory” OR “remote sensing” OR “forest biomass” OR “tree biomass” OR “biomass assessment” OR extent OR factors OR Drivers OR “socioeconomic drivers” OR “financial resources” OR “ human resources” OR “physical resources” OR transportation OR infrastructure OR biophysical drivers OR “carbon stock” OR REDD+ OR “carbon sequestration “ OR “firewood extraction” OR “mining in forests” OR “forest grazing “ OR livestock OR “loss of biodiversity” OR “forest biodiversity” OR “species biodiversity” OR “plant biodiversity” OR “animal biodiversity”

AND (Tanzania)

Table 1.

Keywords used in the Scopus and Google scholar database to search published articles.

A total of 437 records were retrieved after searching Scopus and Google Scholar for articles. To avoid bias in the search, we conducted hand searches by following prominent scholars in the field, resulting in the collection of 33 papers. The collected documents were loaded into Zotero (reference manager software). During the screening process, duplicate records were removed. Rayyan was then used to screen the article titles and abstracts (a web-based program). There were 334 papers screened for titles and abstracts, as well as records that did not include any of the search criteria in their title, abstract, or keywords, as well as research conducted outside of Tanzania and those deemed irreverent. As a result, 162 records were chosen for the subsequent stages. The eligibility phase entailed assessing a range of article attributes in order to choose the most relevant articles for further research. Records that were not concerning forest degradation did not have a PDF, or depended primarily on secondary data were excluded. Finally, an in-depth review of 71 papers was carried out. Table 2 outlines the inclusion criteria that were considered.

Inclusion criteria	Exclusion criteria
Text in English	Text in other languages other than English
Research articles, or book chapter	Publication type is other than article, review, or book chapter
Addressing forest degradation and deforestation	Not addressing forest degradation and deforestation
Study done in Tanzania	Study done outside Tanzania
Time frame: 1985 to 2022	Before 1985
Study includes primary data	Study includes secondary data

Table 2.

Inclusion and exclusion criteria.

2.2 Data analysis

Data analysis was performed by examining the frequency of mention of a research term either as keywords in titles, abstracts, or in the whole document. The analysis was done in VOSviewer software supplemented by analysis using Zotero and MS Excel. Results were summarized as a VOSviewer item map, tables, and graphs showing the frequency of mention of a research term or administrative region of Tanzania (Figure 1).

Figure 1.

The PRISMA framework used in article screening.

3. Results and discussion

3.1 Trend in research on forest degradation in Tanzania

There were more studies more recently, especially increasing sharply after the year 2000 (Figure 2). However, there is also an obvious pattern of decline in the number of studies after peaking in 2016 and 2017. The increasing interest in forest degradation may be related to more recognition of forest degradation as an important aspect of forest management, especially in comparison to deforestation, which received and continues to receive more attention than forest degradation. Even REDD+ began initially as RED and then became REDD and finally REDD+ as forest degradation represented by the second D and its correlates represented by the + in REDD+ became more appreciated [11]. The increase in a number of publications toward 2016 and the decline after 2017 may also be related to a big research program that funded many projects related to forest management in general and forest degradation in one way or another. This was the Climate Change Impacts, Adaptation and Mitigation (CCIAM) program between 2009 and 2014. Before and after this program the fewer number of publications may indicate the significance of a focused research program that enhances the number of country-specific projects that get funded. This is unlike the case where researchers have to compete for funds from international research funding baskets.

Figure 2.

Trend in research in forest degradation in Tanzania indicated by a number of publications per year.

3.2 Research topics covered and gaps in research in forest degradation in Tanzania

The most frequent keywords taken from titles and abstracts were Tanzania, trees, and REDD+ (Figure 3). Forest transition model, drivers, and monitoring were among the least frequent keywords (Figure 3). Eight clusters were formed by the mapping of keyword occurrences (Figure 3). These keyword clusters appear to be based on numerical correlations without a meaningful interpretation of research themes. Such research themes could have been, for instance, research study area, method of data collection, and method of data analysis. However, from the item map terms related to methods, such as monitoring, spatial analysis, and forest transition model, are in three different clusters (Figure 3).

Figure 3.

VOSviewer item map showing keywords that were frequently used to describe clusters in research studies on forest degradation in Tanzania. Larger circles represent a higher frequency of occurrence. Circles in the same color are in the same cluster and have a higher statistical similarity than others. No meaningful research themes could be assigned to the clusters.

Most terms are mentioned less frequently in the document title than in the document as a whole (Table 3). This indicates that more studies are general on forest degradation than specific on the research terms. This represents research gaps for the research topics represented by the research terms. The most covered research term is human, while the least covered is biophysical (Table 3). Some terms are covered less than expected, given their significance in forest degradation. This includes the term mining. The number of studies one may find on a research topic may depend on the term used to search the studies. For example, agriculture returns more research documents than cultivation. In some cases, this is affected by small differences in the spelling out of the research term. For example, spelling socioeconomic returns only 13 documents, whereas spelling socioeconomic returns 43 documents, a difference of 42% of the total number of documents (Table 3).

Research term	Number of mentions in title	Number of mentions in all document	Percent of documents mentioning the research term	Research term	Number of mentions in title	Number of mentions in all document	Percent of documents mentioning the research term
Biophysical	0	8	11	biomass	2	30	42
Quantity	0	10	14	carbon	0	31	44
Qualitative	0	11	15	REDD+	7	31	44
Sequestration	0	12	17	REDD	8	31	44
Fuelwood	1	12	17	health	0	32	45
Mining	0	12	17	harvesting	0	32	45
Socioeconomic	1	13	18	forest diversity	0	33	46
Infrastructure	0	13	18	species diversity	0	33	46
Forest inventory	0	14	20	tree diversity	0	33	46
Inventory	0	15	21	fire	1	33	46
Quantitative	0	18	25	extent	1	35	49
Remote sensing	1	18	25	social	0	35	49
Physical	0	20	28	land degradation	0	39	55
Firewood	0	22	31	plant	2	39	55
Cultivation	0	24	34	animal	3	40	56
Financial	0	24	34	diversity	0	40	56
Grazing	0	24	34	Degradation	7	41	58
livestock	0	24	34	economic	2	42	59
Transport	0	25	35	socioeconomic	1	43	61
Quality	0	26	37	Agriculture	0	45	63
Fuel	2	27	38	factors	2	45	63
Plant diversity	0	28	39	Tree	4	49	69
Tree biomass	1	28	39	Forest	9	51	72
Drivers	4	28	39	Biodiversity	3	51	72
animal diversity	0	29	41	human	3	52	73
Forest biomass	0	30	42

Table 3.

Research topics covered and gaps in research on forest degradation are indicated by number and percent of documents mentioning a research term.

3.3 Extent of forest degradation in Tanzania

One study estimated the extent of forest degradation in miombo woodlands for the whole country [12]. Miombo woodlands represent more than 90% of forest cover in Tanzania, and hence, may give a country-wide picture of the extent of forest degradation. On the basis of that study, annual volumes, aboveground biomass removed, and belowground biomass removed were 1.71 ± 0.54m³ha⁻¹ year⁻¹, 1.23 ± 0.37 t ha⁻¹ year⁻¹, and 0.43 ± 0.12 t ha⁻¹ year⁻¹, respectively. The corresponding aboveground and belowground carbon removed were found to be 0.6 ± 0.18 tC ha⁻¹ year⁻¹ and 0.21 ± 0.05 tC ha⁻¹ year⁻¹, respectively. The estimated annual volume removals exceed the estimated mean annual increment of 1.6 ± 0.2 m³ ha⁻¹ year⁻¹ in miombo woodlands. This indicates forest management in Tanzania is not sustainable.

3.4 Drivers of forest degradation in Tanzania

Various studies have found a variety of causes of forest degradation, including deforestation and forest degradation, agricultural expansion, wood extraction and settlement area agriculture, fuel wood production, unsustainable timber extraction, and pasture expansion [4, 13, 14]. Demand for land and forest resources, as well as the combination of social, political, cultural, and technological variables, can all contribute to deforestation and forest degradation [4]. Specifically on forest degradation due to stem removals, eleven drivers were identified, namely, forest fires, firewood collection, grazing by wildlife, domesticated animals, carving, poles, shifting cultivation, timber, and mining activities [15]. A higher number of stems/ha/year were removed by shifting cultivation, followed by charcoal, natural death, firewood collection, and poles. In terms of carbon, however, higher above-ground carbon removals were caused by timber followed by fire, shifting cultivation, charcoal, and natural death.

When agriculture leads to the permanent conversion of forest into agricultural land use that is deforestation. However, when there is shifting cultivation within a landscape that is defined primarily as a forest, that is, forest degradation. Forest degradation is also defined when there is agriculture under the forest canopy. This is practiced for some crops that are shade-tolerant or even shade-demanding, such as some types of spice crops. Agriculture is the primary cause of deforestation and forest degradation worldwide, accounting for over 80% of total deforestation in developing nations [2]. Agricultural expansion in form of shifting cultivation and encroachment, which is characterized by a low level of technology and o low agricultural yield [16]. A variety of factors, including cultivation techniques, soil fertility, market potential, population expansion, and perhaps pertinent policies, will influence the length of the fallow and cultivation periods [17]. When the fallow period is shortened for shifting cultivation, carbon stocks are degraded. Agricultural expansion rather than intensification was due to inadequate farming technology, low productivity that resulted from it, and low returns on inputs [18].

Fuelwood and charcoal production are major contributors to forest degradation in Tanzania. This is owing to the fact that the primary source of energy is in the majority of Tanzanian households. Wood biomass is collected for use as fuel or charcoal for both home and commercial purposes. The extraction of adequate fuelwood stocks to damage the forest is a significant aspect. Bailis et al. [19] created a geographical supply and demand technique to quantify greenhouse gas emissions associated with wood harvesting for fuel consumption. Woodfuel Integrated Supply/Demand Overview Mapping (WISDOM). Their findings revealed that the distance to the road had a substantial impact on the occurrence of stumps cut for charcoal. According to field observations and interviews, the geology of the area (slope, soil, presence of stones, and availability of drinking water) and preferred species are important variables in determining the location of charcoal production.

Selective timber harvesting can degrade forest carbon stocks, especially in humid tropical forests [7]. Biomass is lost during harvesting operations due to a number of factors, including wood tree felling and damage to trees surrounding the felled trees. There is both illegal and legitimate timber harvesting. Biomass loss has the potential to deplete humid tropical forest carbon stocks [17].

The influence of fire on forests is multifaceted; fires might be ground fires that burn smaller trees and understory or major stand-replacing fires. Fires reduce the rate of forest regrowth and succession from grassland to forest, resulting in continuous grassland and bushland [17].

When grazing animals are allowed on forest land, they both browse and trample young and regenerating trees, killing or damaging them. This causes forest degradation because young seedlings do not survive, and tree girdling causes the eventual death of larger trees (Table 4).

Region	Forest degradation driver
Iringa, Morogoro, Tabora	Agriculture (Crop and livestock production)
Shinyanga, Singida	Farming (food crops and cash crops), firewood
Lindi, Mtwara, Pwani	Logging, charcoal
Iringa, Morogoro, Tanga	Illegal logging, fire
Kagera, Mwanza	Farming (food crops and cash crops) subsistence (food crops production), charcoal
Manyara, Morogoro, Tabora	Charcoal

Table 4.

Main regions for forest degradation in Tanzania.

Adapted from [20].

3.5 Methods of monitoring and assessment of forest degradation

The effects of forest degradation are expected to vary depending on location, forest type, and degree of degradation, making it challenging to detect with medium resolution remote sensing, such as Landsat. There are two types of accounting: land-based and activity-based. Activity-based accounting analyzes emissions independently for each activity and evaluates numerous human activities that cause forest degradation. Regardless of the activities that occur, land-based accounting determines the change in carbon stocks in a specific area of land [17].

Estimates of subsistence wood extraction may be obtained using indirect remote sensing methods. Biomass sampling yields zero-inflated continuous data that challenges conventional statistical approaches. To predict biomass loss as a function of distance to the nearest settlement, Dons et al. [21] employed Tweedie Compound Poisson distributions from the exponential dispersion family in conjunction with GLM. Estimating removals is commonly done by measuring the diameter at breast height (D). The calculated D is then used to calculate biomass and volume using allometric formulae used SD to generate equations for calculating volume, aboveground biomass, and belowground biomass in Tanzanian miombo woods. They analyzed land use and cover maps over a 15-year period by dividing the estimated individual tree volume by the estimated age of the stump.

Distance to populations, urban centers, and roadways are examples of proxy variables that can be used to measure forest degradation [13]. Spatial models that imply a relationship between forest degradation and distance from populated areas, highways, and the forest boundary can be a valuable tool for determining the degree of forest deterioration. In this case, spatial models that imply a relationship between forest degradation and distance from populated areas, highways, and the forest boundary can be a useful tool for estimating the degree of forest deterioration [13]. Focusing on remotely sensed deforestation might miss significant declines in forest quality. Ahrends et al. [8] recommend the use of fast field assessments in conjunction with remote sensing to provide early warning and to allow for prompt and adequately focused conservation and policy responses.

3.6 Institutions involved in efforts to control forest degradation

To properly manage forest resources, it is not sufficient to comprehend different tree species, their regeneration strategies and extraction techniques and rates, for instance. Institutions must be involved in determining who has access to environmental resources, how much can be extracted, when, and how. Tanzania has made major advancements in the management of its forest resources since the early 1990s. Community-based forest management (CBFM) and joint forest management have been implemented as part of the phases (JFM). The two strategies are collectively known as participatory forest management (PFM) [22].

Community-based forest management (CBFM) in Tanzania has contributed to improving forest conditions and in some cases improving livelihoods. PFM has been demonstrated to reinforce forest bureaucrats’ and other specialists’ dominance at the expense of local autonomy and decision-making, which was both a normative objective in and of itself and a major premise underlying its promises of enhancing local livelihoods and forest conservation [23]. Tanzania’s Forest Act of 2002 transferred forest resource ownership and management responsibilities to local communities. The objectives of community-based forest management (CBFM) include enhancing rural livelihoods, protecting and regenerating forest resources, and fostering good governance. State-owned forests on the reserved property are managed jointly by community organizations that rely on the forests for their livelihood [23].

3.7 Spatial distribution of studies on forest degradation in Tanzania

The most mentioned region was Dar es Salaam, while the list mentioned was Rukwa when the names of the regions were searched from the whole document (Figure 4). However, when the names of the regions were searched only from the titles of the documents, only Kigoma had two mentions. The other mentioned regions were only mentioned once, while the rest had no mention in the title. The spatial distribution of studies on forest degradation may be influenced by a number of factors pertaining to a region, including existing forests and their characteristics, drivers of forest degradation, and proximity and accessibility of the region for research.

Figure 4.

The spatial distribution of studies on forest degradation in Tanzania is indicated by number of times a region is mentioned in the title or the number of documents mentioning the region.

4. Conclusions and recommendations

There were more studies more recently, especially increasing sharply after the year 2000, peaking in 2016 and 2017, and then declining. The increasing interest in forest degradation may be related to more recognition of forest degradation as an important aspect of forest management, especially in comparison to deforestation, which received and continues to receive more attention than forest degradation. The increase in the number of publications toward 2016 and the decline after 2017 may also be related to a big research program that funded many projects related to forest management in general and forest degradation in one way or another. Before and after this program the fewer number of publications may indicate the significance of a focused research program that enhances the number of country-specific projects that get funded. The most frequent keywords taken from titles and abstracts were Tanzania, trees, and REDD+. Forest transition model, drivers, and monitoring were among the least frequent keywords. Keyword clusters formed by VOSviewer appear to be based on numerical correlations without meaningful interpretation of research themes.

Most terms are mentioned less frequently in the document title than in the document as a whole. This indicates that more studies are general on forest degradation than specific on the research terms. This represents research gaps for the research topics represented by the research terms. Some terms are covered less than expected, given their significance in forest degradation. The number of studies one may find on a research topic may depend on the term used to search the studies. For example, agriculture returns more research documents than cultivation. In some cases, this is affected by small differences in the spelling out of the research term. The estimated annual volume removals exceed the estimated mean annual increment indicating forest management in Tanzania is not sustainable. Various studies have found a variety of causes of forest degradation, including deforestation and forest degradation, agricultural expansion, wood extraction and settlement area agriculture, fuel wood production, unsustainable timber extraction, and pasture expansion. The effects of forest degradation are expected to vary depending on location, forest type, and degree of degradation, making it challenging to detect with medium resolution remote sensing, such as Landsat. There are two types of accounting: land-based and activity-based. Activity-based accounting analyses emissions independently for each activity and evaluates numerous human activities that cause forest degradation. Regardless of the activities that occur, land-based accounting determines the change in carbon stocks in a specific area of land. Tanzania has made major advancements in the management of its forest resources since the early 1990s. Community-based forest management (CBFM) and joint forest management (JFM) have been implemented. The most mentioned region was Dar es Salaam, while the list mentioned was Rukwa when the names of the regions were searched from the whole document. However, when the names of the regions were searched only from the titles of the documents, only Kigoma had two mentions. The other mentioned regions were only mentioned once, while the rest had no mention in the title. It is expected that forest stakeholders will find the analysis presented in this study useful. Furthermore, the stakeholders will find interest in addressing temporal, spatial, and thematic research gaps highlighted by this chapter. Temporally, the declining number of publications reported needs to be addressed. Spatially, some administrative regions are underrepresented in the literature. Thematically, more specific research on topics related to forest degradation needs to be carried out.