**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

**Figure 1.** *The PRISMA framework used in article screening.*

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

**Figure 2.** *Trend in research in forest degradation in Tanzania indicated by a number of publications per year.*

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.

#### **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**).

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

#### **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.*

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**).

#### **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 ± 0.54m3 ha−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,


### *Forest Degradation in Tanzania: A Systematic Literature Review DOI: http://dx.doi.org/10.5772/intechopen.107157*


**Table 3.** *Research topics covered and gaps in research on forest degradation are indicated by number and percent of documents mentioning a research term.*
