Skip to main content
Download PDF

How have carbon stocks in central and southern Africa’s miombo woodlands changed over the last 50 years? A systematic map of the evidence

Environmental Evidence volume 7, Article number: 16 (2018) Cite this article

A Systematic Review Protocol to this article was published on 05 December 2014

Abstract

Background

Miombo woodlands cover ≈ 2.7 million km2 of central and southern Africa between dry (650 mm mean annual rainfall) and moist miombo (1400 mm) and are currently threatened by land use and land cover changes that have intensified over the last 50 years. Despite the miombo’s global significance for carbon (C) storage and sequestration, there has been no regional synthesis that maps carbon stocks and changes in the woodlands. This information is crucial to inform further research for the development of appropriate policies and management strategies to maintain and increase C stocks and sequestration capacity, for conservation and sustainable management. We assembled a systematic map to determine what evidence exists for (1) changes in carbon stocks in miombo woodlands over the period 1960–2015; (2) differences in carbon density in miombo with different conservation status; (3) trends in carbon stock recovery following human disturbance; and (4) fire management impacts on carbon stocks and dynamics.

Methods

We screened 11,565 records from bibliographic databases and grey literature sources following an a priori research protocol. For inclusion, each study had to demonstrate the presence of miombo-typical species (Brachystegia, Julbernardia and Isoberlinia) and data on above- or below-ground carbon stocks or plant biomass.

Results

A total of 54 articles met the inclusion criteria: 48 quantitative and eight qualitative (two of which included quantitative and qualitative) studies. The majority of studies included in the final analyses are largely quantitative in nature and trace temporal changes in biomass and carbon in the miombo woodlands. Studies reported a wide range (1.3–95.7 Mg ha−1) of above-ground carbon in old-growth miombo woodland. Variation between years and rainfall zones and across conservation area types was large.

Conclusions

An insufficient number of robust studies that met our inclusion criteria from across the miombo region did not allow us to accurately pool carbon stocks and trends in miombo old growth. Thus, we could not address the four questions originally posed in our protocol. We suggest that future studies in miombo woodlands take longer term observational approaches with more systematic, permanent sampling designs, and we identify questions that would further warrant systematic reviews, related to differences in C level recovery after disturbance in fallow and post-clearing re-growth, and the role of controlled fire management.

Background

Global atmospheric concentration of carbon dioxide (CO2) is on the rise. Changes in land use have contributed about 136 (± 55) Gt C or about 25% of total anthropogenic emissions of CO2 to the atmosphere over the past 145 years [1, 2]. The importance of CO2 to climate change has sparked much research on the global carbon cycle, with particular attention on carbon stocks in the main terrestrial compartments of ecosystems, mainly soils and plant biomass [3,4,5].

Miombo is seasonally dry deciduous woodland dominated by trees of the genera Brachystegia, Julbernardia and Isoberlinia and is the largest vegetation formation in central, eastern and southern Africa. This woodland type covers an estimated 2.7 million km2 across seven countries (Angola, Democratic Republic of Congo (DRC), Malawi, Mozambique, Tanzania, Zambia and Zimbabwe) and consists of both dry (650 mm mean annual rainfall) and moist miombo (1400 mm mean annual rainfall) woodlands [6, 7]. The region, also sometimes referred to as the miombo ecoregion [8], has a unimodal rainfall pattern with distinct and prolonged dry seasons that last from April to October/November and an edaphic environment of typically leached, shallow and impoverished soils [9]. It is the combination of these environmental factors, together with fire, that defines the limits of the miombo ecoregion from adjacent vegetation types.

Although the miombo region has been researched from the 1960s (see [10,11,12,13,14,15,16,17] among others), miombo woodlands are poorly documented and understood compared with other world biomes [18] and have only been recently recognised as a key ecosystem, for both their biodiversity values and ecosystem services (see [9, 19, 20]). The region is important for the production of valuable hardwood timber and supports the economic livelihood of millions of people [7, 15, 21, 22]. Miombo woodland is a socio-ecological system that has been maintained by humans through a long history of harvesting, cultivation and frequent dry season fires over more than 55,000 years [17, 23,24,25]. However, there are indications that the last 50 years have witnessed an intensification of these land use activities driven by increasing human and livestock populations, as well as the human-induced concentration of wildlife herbivores into small conservation areas [26,27,28,29]. In addition, colonial land tenure policies coupled with a post-colonial export-oriented focus have disenfranchised local communities and left them dependent on subsistence agriculture and the exploitation of forest resources [30,31,32,33].

Lately, a number of national and regional land-use policies have done little to change this negative history. Regional policy frameworks such as the Southern African Development Community (SADC) Protocol on Forestry [34], SADC Forestry Strategy (2010) [35], and Common Market for Eastern and Southern Africa (COMESA) programme, provided policy models on forestry and climate change that member countries could adopt and further develop [36]. These regional initiatives drew from Rio Earth Summit (United Nations Conference on Environment and Development (UNCED), Rio de Janeiro, June 1992) whose declaration focused on the need for nation states to create policy environments conducive to rural livelihoods. As a result, since the 1990s miombo states have pushed participatory land use programs in many woodland areas. Although these policy initiatives aimed to conserve natural resource and woodland use, many have remained largely ineffective [37]. This is for two reasons: first, because many community programmes remain underfunded and centrally driven; and second, because these initiatives have not comprehensively addressed issues pertaining to woodland access and ownership at the local level, which indirectly encourages more agricultural expansion in miombo [37, 38]. The private sector has also played a role in exploiting miombo areas through massive investments in agriculture. This has opened up more lands for crops such as tobacco, and in some instances, triggered land grabs from local woodland users [37, 39]. The combined effects from state and private land use policies have thus adversely affected miombo woodland stocks [38, 40,41,42,43].

Because seasonally dry tropical woodlands cover 40% of the tropical forest area and contain considerable carbon stocks [44, 45], deforestation and degradation of this biome has global significance [46]. The carbon stored in the above-ground living biomass of trees is a large pool directly impacted by deforestation and forest degradation [4, 47], while soil carbon represents another important carbon sink. Most soil carbon is derived from roots rather than shoots and leaf litter [48]. Plant root systems are therefore a major carbon sink and influence processes such as soil erosion and carbon cycling [49, 50]. Like many other seasonally dry forest and woodland species, miombo trees have extensive root systems that facilitate regeneration after harvesting [51, 52]. The availability of stump coppices, root suckers and suppressed saplings in the herb layer at the time of clearing enables these woodlands to recover rapidly, depending on fire and the intensity of subsequent land use (e.g. cultivation). Many studies have shown that miombo woodlands can recover rapidly from natural and anthropogenic disturbances [7, 12, 51,52,53,54,55,56,57,58,59,60].

Miombo woodlands now comprise a variety of land covers that range from woodlands composed of tall, almost closed-canopy stands, to areas with little tree cover due to clearing for cultivation, charcoal production [59], or mining and infrastructure development [22, 61]. Although the original extent of the woodlands is unknown, we do know that climate change has affected the spatial extent of miombo areas for some time [62,63,64]. For example, pollen studies show a more southerly presence of the Brachystegia pollen in 1–4 kyr BP sediments from near Naboomspruit and Pretoria in South Africa—areas that are well beyond the present southern limit of the genus [65]. Multiple land pressures on miombo continue today as many areas across southern Africa have rising populations and greater demands for arable land. These variations in land cover and human interference influence how much biomass and carbon the woodlands can hold, and indicate a clear need to study these shifting land dynamics. In spite of the global significance miombo woodlands may have for carbon storage and sequestration, current knowledge of their contribution to global pools is limited [66], and there has been no regional synthesis of carbon stocks and changes in miombo systems to inform policy and management strategies. This map seeks to address this gap by assembling an evidence base of research, and highlighting evidence gaps, which can contribute to agenda-setting for areas of further research which seek to understand the changing land dynamics in miombo woodlands.

Objective of the review

An a priori systematic review protocol [67] describes our objectives and methods in detail. A summary is presented here, and highlights amendments that we made when developing the systematic map.

The original objective of the systematic review, detailed in the protocol, was to assess the impact of land use and land cover change on carbon stocks in miombo woodlands since the 1950s [67]. However, after assessing the available data, we found many historical studies lacked measurements of carbon or biomass, and often that studies did not clearly describe the associated land use cover change, practice, or related land use policies. As such, it became necessary and important to survey the quantity and quality of evidence on land use practices and land use policy in the miombo with a systematic mapping approach. Consequently, we reformulated the review’s original questions to link land use practice and policy to land cover change and carbon stocks in miombo areas over the last 50 years. We used the following questions to guide our mapping of studies:

  1. (1)

    What evidence exists for changes in carbon stocks in miombo woodlands over the period 1960–2015?

  2. (2)

    What evidence exists for differences in carbon density in miombo with different conservation status?

  3. (3)

    What evidence exists of trends in carbon stock recovery following human disturbance?

  4. (4)

    What evidence exists of fire management impacts on carbon stocks and dynamics?

To answer these questions we classified miombo areas by their legally designated protection status based on the conservation categories of the International Union for Conservation of Nature (IUCN).Footnote 1 These include: national parks (NP), forest reserves (FR), game management areas (GMAs) and open areas (OA). In miombo countries, many protected forestry and wildlife areas fall under NPs and FRs, with GMAs also supporting NPs [68]. Forest reserves are split into national and local FRs, and include botanical gardens and sanctuaries. National wildlife and public forestry institutions jointly manage local FRs and GMAs in conjunction with local leadership councils (e.g. chiefs). Under the IUCN conservation categories, OAs have no conventional protection status; GMAs fall in the IUCN conservation area category VI (resource reserve) in which only classified fauna is protected; GMAs and FRs (local and national) fall in the IUCN conservation area category VIII (multiple use management area or managed resource area) which, depending on the establishment objective, may be protected from human activities such as settlements or where harvesting is legally permissible; and NPs fall in the IUCN conservation area category II or IV (nature conservation reserve or managed nature reserve or wildlife sanctuary) in which legal protection bars anthropogenic ecosystem disturbances [69].

While these IUCN categories have been followed in miombo countries, they often act as functional designations for national government forestry policies and vary in how well their protection status is upheld. Wildlife parks, for example, have received much more attention (in terms of restricted access as they fall under NPs) compared to FRs, which is why some authors described FRs as reservations for commercial timber rather than protected forests [70]. Lately, because of growing social and political pressures for access to land and other natural resources, NPs have also suffered from habitat loss and fragmentation, resulting in unprecedented threats to woody cover and wildlife [71]. Similarly, GMAs and local FRs have been affected by agricultural expansion and greater demands for natural resources. In Zambia, for example, these areas are contested, and over time many protected areas have been degazetted and given away to settlers [72, 73].

We then assessed the best available evidence to understand the impacts of major land use activities (which include fire, herbivory, wood harvesting, woodland conversion and cultivation) on above and below-ground carbon stocks in miombo woodlands. Figure 1 shows how we originally conceptualised the drivers and impacts of land use change on above- and below-ground carbon stocks in miombo woodlands [67].

Fig. 1
figure 1

(adapted from PGH Frost, personal communication, January 22, 2014)

Conceptual model of land use and cover change on above- and below-ground carbon stock in the miombo woodlands

Full size image

Methods

Literature searches

Our search strategy was implemented between April and November 2015. We consulted a wide range of historical, academic (peer-reviewed) and grey literature sources to locate studies that examined land use change in miombo woodlands since 1950 (Table 1; Additional file 1). The search for studies from the 1950s onwards was chosen for two main reasons: first, because we estimated this to be enough time to document land use change and impacts within the region; and second, because there was unlikely to be usable data before 1950 for the purposes of the map. To find historical, unpublished or highly relevant field data from miombo countries, we hand-searched library resources and contacted experts in the field. To identify relevant peer-reviewed literature, we developed and tested a search string that used search terms (and synonyms) from the population, exposure and outcomes of interest (Table 2). This search string (and shorter versions) were used across nine publication databases. Where using a long search string was not feasible, such as in Google Scholar and other academic and grey literature websites, the search string was shortened and simplified. Additional file 1 shows the search strings, keywords and literature sources used during the search, and Table 1 shows the original list of literature sources. One noted change from the original research protocol is that due to time and resource constraints, fewer government offices and private companies than planned were contacted.

Table 1 Databases and organizations searched
Full size table
Table 2 Search terms
Full size table

Article screening and study inclusion criteria

After the articles were compiled and duplicates removed, a review team (4–9 people) used a priori inclusion criteria to identify relevant studies (as shown in Table 3) [67]. The criteria were that studies had to be within the miombo region of sub-Saharan Africa, defined by the presence of species in the genera of Brachystegia, Julbernardia and Isoberlinia. Studies also had to demonstrate a land use practice that impacted above- or below-ground carbon or biomass (which included measurements of vegetation density and diameter at breast height). Qualitative studies had to demonstrate links between land use change and biomass or carbon change, including discussions of forest and land policies or tenure arrangements.

Table 3 The subjects, exposures, comparators, and outcomes of relevance
Full size table

Before screening began, a Kappa analysis [74] was done to compare reviewer agreement on a random sample of 100 titles and abstracts. This was to measure inter-rater agreement in applying the inclusion criteria to studies (where all reviewers must achieve a score of > 0.6 in agreement of 100 articles). Randolph’s free marginal multi-rater Kappa [75] was used to score agreement for 2 rounds of Kappa analysis between 9 and 8 reviewers, respectively. Both rounds had over 0.6 agreement, and reported 0.67 (9 reviewers) and 0.64 (8 reviewers) free marginal Kappa scores. During each round, disagreements in applying the inclusion criteria were discussed and these notes were saved in a screening guidance sheet that was shared with all reviewers. Once the second round of Kappa test was completed and discrepancies discussed, screening of articles began.

The review team screened articles through two main stages: title/abstract and full-text levels. During the first stage, seven reviewers screened 11,565 titles and abstracts simultaneously with the online software tool Abstrackr.Footnote 2 Abstrackr allows multiple reviewers to screen titles and abstracts independently and collates results. After the first round was completed, a smaller review team (five people) re-screened abstracts with Abstrackr. This was partly because of over-cautiousness by the larger review team in reviewing abstracts, but also because of the large number of records remaining and the limited resources of the team. The reviewers followed the same inclusion criteria but excluded articles that were highly likely to be ineligible for the map. These results were then imported into a Microsoft Excel file for full-text review.

Full text screening was the second stage of screening. A small review team (four people) screened 1014 full-texts online according to the inclusion criteria. Records which passed this stage were downloaded and included in the map for further eligibility screening in study validity assessment and data coding stages.

Study validity

Five reviewers were involved in assessing study validity and data coding processes as described in [67] and shown in Additional file 2. The study validity assessment tool, as proposed in the original protocol [67], was discussed with the Advisory Group and tested iteratively on selected papers to ensure uniformity of application. The principles outlined by Bilotta et al. [76] were used as the basis for the tool’s format, namely: it has “construct validity” (the included criteria measure what they purport to be measuring), it facilitates good inter-reviewer agreement, it can be applicable across study designs, and is relatively quick and easy to use. It used 14 questions to score records on study type and the strength of presentation of data. Because of the high variability of methods used between both qualitative and quantitative studies, the tool used key variables that focused on the relevance and clarity of data presentation but, at the same time, were flexible enough for the many types of study designs encountered. These questions were answered with a yes (1), unclear (0.5), or no (0) and scored with a quantitative ranking assessment (Table 4, Additional file 2). Important contextual social information (e.g. tenure arrangements and woodland management policies linked to land use change) and study site information (for plot measurement studies) were also similarly ranked. An overall cumulative quantitative score, based on the validity of data reporting, was given to each record. This collated studies into a relative ranking system of high (13–14); medium (9–12.5); low (6–8.5); and very low study validity (0–5.5). Records which scored very low were excluded from the map.

Table 4 Study validity assessment criteria
Full size table

Study coding strategy

After a study’s validity was assessed, a Microsoft Excel template was used to record as many study metadata elements as were available in the articles. This information included bibliographic information, study site information, details of evidence type and methodology, study context, and outcomes, as shown below. Before data coding began, five reviewers first tested the template with several studies and then discussed discrepancies. This was to ensure there was the same understanding in applying and answering the data coding categories. One reviewer collated the data for review and further analysis (Additional file 3, quantitative information).

  • Bibliographic information: author, year, title, publication type, place of publication/publisher.

  • Study site information: location of study, exposure(s), duration of the exposure(s).

  • Details of evidence type: source, study design, methodology, parameters used in the analysis, duration and year of study.

  • Context and relevant detail considered in the study: conceptual link between the exposure and biomass or carbon stock.

  • Evidence of outcomes: reported presence of data and effect on biomass and carbon, duration of impacts, scale and suitability of impacts. Note that, in a significant change to the protocol (and in keeping with all systematic maps), primary outcome data are not included; we indicate only the presence of such data in the studies, which will assist future systematic reviews that may explore related questions in more detail.

Results

Study inclusion

We screened 11,565 records by title and abstract (after removing duplicates, Fig. 2). Many records were excluded because they did not show the presence of the defined miombo tree species, or failed to directly link land use change to impacts upon plant biomass or above or below-ground carbon. Title/abstract and full-text screening excluded 11,284 records, leaving 281 records (221 peer-reviewed articles and 60 additional sources) that were appraised for eligibility. During this process, 227 records were excluded because of low validity (see Additional file 4 for excluded studies). What particularly limited the number of included records was poor documentation of information on the Population—failure to demonstrate that studies were in miombo areas as defined by the species outlined in the protocol—and Outcomes—poor or missing information on how land use changes impacted carbon or biomass in defined measurements. In total, 54 records were found to have usable data for the map, comprising 48 quantitative and eight qualitative records (two records are double-counted as they reported both quantitative and qualitative data).

Fig. 2
figure 2

Study screening and inclusion process used for developing the systematic

Full size image

Descriptive statistics

Of the 54 records included, three records were dissertations, three records were reports, and 48 records were journal articles, covering the years between 1962–2016 (Fig. 3). All records contained only one unique study. Two studies were assessed as having high validity; 51 studies were assessed as medium; and one study was assessed as having low validity. These factors are discussed further below.

Fig. 3
figure 3

Distribution of included studies by year published (quantitative and qualitative studies)

Full size image

Qualitative studies

Eight qualitative studies directly related miombo land use practices to impacts on woody biomass and carbon. The study locations were as follows: Malawi (n = 2), Zambia (n = 2), Tanzania (n = 2), Zimbabwe (n = 1) and one global study. Most studies were journal articles (n = 7) and one study was a dissertation. Three studies used mixed methods (quantitative and qualitative approaches including interviews, and measured transects and plots), while five studies employed qualitative methods only. In the assessment of study validity, one had high study validity, and six studies were scored as medium and one low.

The majority of qualitative studies (n = 7) focused on exposures around the use of local forest resources for livelihoods. Only one study focused on agricultural practices in forest areas. One study made direct links to forest loss and carbon, and two studies attempted to measure forest resource extraction. The other studies (n = 5) used narrative evidence and description to describe land and woodland practices. All studies linked land and woodland uses (i.e. exposures) to impacts on woodland or biomass (i.e. outcomes) but were extremely site specific and therefore difficult to generalise.

All eight studies documented how the miombo woodlands were important for local livelihoods, and thus advocated local level forest management. For example, one study argued for the use of local knowledge in woodland management [77], and almost all studies (n = 7) called for decentralisation of management to local levels. Mbwambo et al. [78] most clearly demonstrated the critical issues affecting woodland governance at different scales, by showing that local forestry reserves (e.g. State) had as much tree loss as open (e.g. communal) woodlands in Tanzania. This reflects the finding by Roe et al. [79] that all miombo countries have some form of community forestry management scheme captured in forest policies, but that the failure to deploy these policies has not been fully understood. However, in terms of broader governance aspects of miombo woodland management, the evidence base was limited. No studies discussed the implications for adopting regional forestry management models (i.e. SADC and COMESA) or global models such as REDD+, and the necessary land use or forest policy changes needed [80].

Quantitative studies

Studies were from 87 locations in Malawi, Mozambique, Tanzania, Zambia and Zimbabwe (Fig. 4). Five of these sites lie outside the main miombo woodland range as it is typically known but were included because the flora of the sites include species of Brachystegia and Julbernardia as defined by the inclusion criteria [67]. The sites extend over a large geographical region and differ in some biophysical conditions. Notably, no sites were located in Angola, northern Mozambique or the DRC, and more of the studies occurred in dry (n = 31) compared to moist miombo (n = 18) (one article had plots in both dry and moist miombo) (see Fig. 5).

Fig. 4
figure 4

Distribution of miombo woodlands (shaded area based on [9]) and study sites/places (empty circles) included in the map. Some sites/places represent more than one study. An online geo-map of data is available here: https://oxlel.github.io/evidencemaps/miombo/

Full size image
Fig. 5
figure 5

Distribution of quantitative and qualitative studies by country and miombo woodland type

Full size image

Research design

Data in the relevant studies (Table 5) were collected using diverse sampling designs at sites with heterogeneous geographical, ecological and land use conditions. Few studies (n = 17) used permanent sampling designs; most used chronosequence study designs in old and re-growth plots (n = 494) (Fig. 6).

Table 5 The types of methods, data outcomes and studies included in the map
Full size table
Fig. 6
figure 6

The distribution of methods used in quantitative studies, organised by plots and types of miombo cover

Full size image

The majority of the data reported on above-ground biomass, basal area and soil organic matter (Table 5 and Fig. 7). In terms of woodland cover, the majority of study plots were in re-growth (n = 298) compared to old-growth (n = 213) woodland. Re-growth study sites were further classified into either fallow re-growth (n = 163) (i.e., woodland regenerating at a site/plot following abandonment of crop cultivation); or woodland re-growth (n = 135) (woodland regenerating at a site/plot following clearing, e.g. for charcoal production or tsetse fly control, without experiencing crop cultivation). In terms of conservation area status, the data were distributed as follows: 5 in GMAs, 15 in NPs, 72 in FRs and 419 in OAs (see Fig. 8).

Fig. 7
figure 7

The distribution of outcomes within quantitative studies by outcome and number of plot measurements

Full size image
Fig. 8
figure 8

Distribution of miombo study sites as classified by IUCN conservation status (quantitative studies only)

Full size image

Woodland management practices (harvesting, fire regime and herbivory) were not explicitly described for the majority of sites, which would constrain any analysis of effects of these practices on carbon stocks and sequestration (Fig. 9).

Fig. 9
figure 9

Distribution of included quantitative studies mentioning land use practice types (by plots/sites)

Full size image

Discussion

Carbon stocks in old-growth miombo woodlands

Average above-ground carbon stock in old-growth miombo woodland across all studies was 33.9 ± 1.3 Mg C ha−1, with a wide range (1.3–95.7 Mg ha−1). However, variation between years (i.e. different data sets) and between rainfall zones was large, and the wide range suggests that sampling occurred randomly across the region. Given this large variation, future assessments would need to differentiate by rainfall/aridity zones, history of land use and human interventions.

A number of studies have asserted that the level of disturbance in miombo woodlands increases with decreasing distance from human settlements and road infrastructure [17, 126,127,128], and that miombo woodlands are losing their potential to hold C stocks, rather becoming net emitters of CO2 into the atmosphere [102]. A global study found human pressure (measured through human population density, land transformation and electrical power infrastructure) on terrestrial ecosystems, including protected areas, to have increased by 64% since the 1990s [129].

However, there is scant evidence in the available literature of miombo biomass variation with distance from settlements. It is also known that biomass depends on the aridity levels [130], drainage and effective rooting depth [51], as well as rainfall [131]. Data for these factors in the studies we mapped are not available in a uniform way, and this would severely limit future meta analysis testing this hypothesis. A further limitation of studying biomass across sites and years from the a posteriori compilation of available data sets comes from the different sampling designs and spatial confounding factors between studies. Further, the studies do not represent any systematic sampling across the whole miombo region and its environmental and climatic gradients. Some countries have been completely omitted from the map because of the lack of data. Long-term observational data in the same stands under controlled intervention regimes of different intensity would be needed to address specifically the question of stand biomass degradation over time.

Below-ground carbon

Miombo woodland soils contain considerable amounts of organic matter, which forms a large below-ground pool of carbon [115]. Two soil qualities have an important bearing on soil carbon. First, total soil organic matter (SOM) in miombo soils is often concentrated in the top 30 cm of the soil, with contents ranging from 1 to 2% [89, 132, 133]. Second, soil bulk density (SBD), which ranges from 1.2 to 1.4 in miombo soils [89, 110]. Bulk density was not reported in all the studies reviewed [115].

Few studies have estimated below-ground plant biomass in the miombo. In general the root:shoot (R/S) ratio is used to estimate below-ground biomass and this ratio varies with tree size in miombo woodlands [25, 107, 108]. Since young and small trees tend to have higher below-ground to above ground biomass ratio than old and large ones, it is important to use different R/S ratios for re-growth miombo dominated by small trees and for old-growth miombo dominated by large trees. Appropriate ratios given in [107] of 0.77 and 0.54 for re-growth and old-growth miombo woodland, respectively, would need to be applied to raw data to estimate below-ground biomass and carbon effectively across studies.

Loss of miombo woodland area

Miombo woodland area losses have been observed to varying degrees of intensity at landscape level, largely driven by land clearing for agriculture and wood extraction for energy, both processes often going hand in hand [21]. Governments have been working to address tree loss in general as noted in the promulgation of new forest policies, acts and regulations in the wake of the developments after the Rio Earth Summit (UNCED, Rio de Janeiro, June 1992) [134, 135]. While regulations exist, the situation is not likely to change soon without sufficient budgetary support, enforced regulations, the participation of communities, and awareness and capacity building.

Carbon stocks along a conservation gradient

Wegmann et al. [136] found that vegetation cover change is faster outside than inside protected areas, and Geldmann et al. [129] observed that human impact on protected areas is correlated to their location and IUCN management category. Cumming et al. [137] observed that woodland degradation in protected areas in southern Africa is largely caused by elephants and fire. Studies included in the map report varying figures for C stocks along a conservation gradient in miombo woodlands; future meta analysis could test whether the data are in line with findings by Banda et al. [88] in western Tanzania. Indeed, given the increasing number of reports of human encroachments into national parks located within miombo woodlands (see [121, 126, 138]), human and elephant pressure in protected areas may have made C stocks in conservation areas just as vulnerable to loss as in unprotected areas. This would present considerable challenges to efforts to conserve C stocks in the miombo region of central and southern Africa, e.g. for initiatives to reduce greenhouse gas emissions and increase carbon sequestration that are under way in parts of the region [139,140,141]. These initiatives include REDD+ (Reduced Emissions from Deforestation and forest Degradation plus enhancement of forest stocks), which creates incentives for developing countries to protect, better manage and wisely use their forest resources and thereby contribute to the global fight against climate change while providing development benefits to communities on the ground. Countries like Tanzania, Mozambique and Zambia have gone through REDD+ readiness and planning phases, and are moving towards the development and adoption of national REDD+ strategies [142,143,144]. It is important therefore that such initiatives take cognisance of the current dynamics in C stocks in miombo woodlands during their development phase. To some extent, data and information generated in the planning phases have been incorporated in country reports to UNFCCC and in some cases used to bolster regional climate change negotiations (see [145]).

Carbon stock recovery in re-growth woodland

The studies report sufficient data on trends in C stock recovery in re-growth miombo that could be analysed to assess possible differences in recovery trajectories on post-clearing re-growth and fallow re-growth sites. In shifting cultivation systems most fallow re-growth is converted back to cropland before the age of 30 years [96, 146,147,148]; reported declines in C stocks after 20 years would need further research.

Studies report C stock increments in young re-growth (< 15 years old) and for intermediate ages (15–40 years), but few data points for older trees. Chidumayo [84] found that stem density in re-growth miombo of 49 years was 13–25% of that in re-growth of 11 years and the decrease in stem density was attributed to competition in the intervening period. These changes often indicate a socio-economic portrait of the landscape born out of the interplay between land use measures and decisions by people and governments [149, 150] and in the case of southern Africa, land contestations of 1990s and encroachments in response to Structural Adjustment Programmes (SAPs) of the same period have been behind forest loss in general [151]. New policy and management strategies are therefore required to reverse this negative trend if new initiatives, such as REDD+, are to achieve their objectives.

Effects of fire management on carbon stocks and dynamics

Wild fires affect carbon stocks and dynamics in miombo woodlands [25, 98] and there were studies reporting this effect. Previous findings indicate that production and C accumulation in miombo woodland is higher under fire protection and early burning than under late burning [11, 56, 84, 152]. However, complete fire protection is risky because accidental fires occurring under fire protection can find more fuel to burn and undo the gains made in carbon stocks accumulated over long periods. Chidumayo [56] reported that in a 36-year old fire protected re-growth plot, an accidental fire reduced carbon stocks to 13.9 Mg ha−1 compared to a stock of 72.5 Mg ha−1 that had accumulated under early burning. However, after 36 years of the experiment, both these carbon stocks were higher than that of 2.3 Mg ha−1 under late burning.

Limitations of the map

While our search strategy aimed to minimise publication bias by consulting a wide range of peer-reviewed and grey literature sources, we did not have the time nor resources to review studies in languages other than English. As such, we are likely to have missed relevant studies in Portuguese and other languages from the southern African region. The lack of systematic biomass assessments across the regions’ climatic and environmental gradients, while accounting carefully for human intervention, is another important limiting factor.

Most of the data were chronosequence studies that used space for time and may have spatial confounding factors. In addition to conservation area status, data reported included age and type of re-growth (i.e. fallow re-growth or woodland re-growth) and cultivation period that provides information indicative of temporal trends in carbon stocks and sequestration.

Studies reported above- and below-ground carbon stocks in miombo woodlands in a wide variety of ways. Most studies estimated above-ground wood biomass using allometric models based on tree diameter measurements at breast height. These models ranged from non-linear power functions to linear logarithmic models and therefore the estimates may have contained biases due to the allometric model used. This would be a serious challenge for future anlaysis of reported data. Meta analysis of the data in these studies would require that all the data be converted to carbon units at the same spatial scale (e.g. Mg C ha−1) using an appropriate equation [68].

Conclusions

Implications for research in the miombo and future systematic review topics

The systematic map reveals an inadequate evidence base to address the four questions originally posed in our protocol. An insufficient number of robust studies was found that meet our inclusion criteria from across the miombo region. There is a clear knowledge gap for Angola, northern Mozambique and the Democratic Republic of Congo, at least from the wide sources we consulted, and while we acknowledge that this may due in large part to language bias in the mainstream academic literature, it points to a need to focus on this region in future primary research. There was more literature on dry miombo sites compared with moist sites, and the latter may also be a useful focus for future research. The relative dearth of qualitative studies highlights a potential source of future work to develop policy based on community engagement in the miombo region.

Much further research is needed to understand C stocks and their trends in miombo old-growth woodland, because pooling the data is not sufficiently informative for localised management suggestions, given the variability (i.e., rainfall, aridity, soil depth and stand age) of miombo across the region. Understanding miombo biomass stocks, losses and gains and carbon sequestration in light of the variability across the regional gradients is crucial for future studies. It would require better assessment of both above- and belowground biomass across the region in a systematic way, accounting for spatial, climatic and land use differences.

In light of these findings, we suggest that future studies in miombo woodlands take longer term observational approaches with systematic, permanent sampling designs (versus chronosequence study designs). We also identify a few important phenomena pertinent to miombo woodland land use policy and management that would benefit from systematic review with meta analysis:

  • Do carbon stocks in miombo woodlands return to previous levels after disturbance?

  • What are the differences between carbon stocks in fallow re-growth sites and those in post-clearing re-growth sites, which appear to take longer to re-establish but reach higher levels?

  • Is regrowth after agricultural fallowing slower than after clearing and are conservation areas adequately protecting miombo woodlands?

  • Is controlled fire management, especially with early burning, a favourable management strategy for miombo woodlands to sequester and store carbon?

Notes

  1. Many of the protected areas (e.g., forestry reserves) in miombo countries have not been widely studied. Burgess et al. [70] have observed the link between these reserves and the global reservation system (as outlined in IUCN protected area categories) is weak especially since they seem to be nationally driven rather than globally.

  2. Abstrackr is a simple software tool developed by Brown University to enable quick screening of study titles and abstracts. See https://www.brown.edu/academics/public-health/research/evidence-based-medicine/research-initiatives/software-0.

Abbreviations

AGB:

above-ground biomass

AIC:

Akaike’s information criterion

BGB:

below-ground biomass

C:

carbon

CO2 :

carbon dioxide

COMESA:

Common Market for Eastern and Southern Africa

FR:

forest reserve

FRG:

fallow re-growth (woodland regenerating at a site/plot following abandonment of crop cultivation)

GtC:

gigatonnes of carbon

GMA:

game management areas

IUCN:

International Union for Conservation of Nature

MCAI:

mean c annual increment

Mg ha−1 :

mega grams per hectare

NP:

national parks

NTFPs:

non-timber forest products

OA:

open areas

PCRG:

post-clearing re-growth miombo (after cultivation)

REDD+:

Reduced Emissions from Deforestation and forest Degradation plus enhancement of forest stocks

R/S:

root:shoot

SADC:

Southern African Development Community

SBD:

soil bulk density

SOM:

soil organic matter

References

  1. Houghton R, House J, Pongratz J, Van der Werf G, DeFries R, Hansen M, et al. Carbon emissions from land use and land-cover change. Biogeosciences. 2012;9:5125–42.

    CAS  Google Scholar 

  2. Le Quéré C, Moriarty R, Andrew R, Canadell J, Sitch S, Korsbakken J, et al. Global carbon budget 2015. Earth Syst Sci Data. 2015;7:349–96.

    Google Scholar 

  3. Henry M, Valentini R, Bernoux M. Soil carbon stocks in ecoregions of Africa. Biogeosci Discuss. 2009;6:797–823.

    Google Scholar 

  4. Friedlingstein P, Houghton R, Marland G, Hackler J, Boden T, Conway T, et al. Update on CO2 emissions. Nat Geosci. 2010;3:811–2.

    CAS  Google Scholar 

  5. Mackey B, Prentice I, Steffen W, House J, Lindenmayer D, Keith H, et al. Untangling the confusion around land carbon science and climate change mitigation policy. Nat Clim Chang. 2013;3:552–7.

    CAS  Google Scholar 

  6. White F. The vegetation of Africa. Paris: UNESCO; 1983.

    Google Scholar 

  7. Frost P. The ecology of miombo woodlands. In: Campbell B, editor. The miombo in transition: woodlands and welfare in Africa. Bogor: Center for International Forestry Research (CIFOR); 1996. p. 11–57.

    Google Scholar 

  8. Byers AB. Conserving the miombo ecoregion. Reconnaissance summary. WWF, Southern Africa Regional Program Office. Harare, Zimbabwe; 2001.

  9. Timberlake J, Chidumayo EN. Miombo ecoregion vision report. Occasional publications in biodiversity No. 20. Bulawayo, Zimbabwe; 2011.

  10. Endean F. Experiments in silvicultural techniques to improve the indigenous savanna woodland of Northern Rhodesia. In: Paper to the 8th commonwealth forestry conference. Nairobi; 1962.

  11. Endean F. The productivity of miombo woodland in Zambia. Lusaka: Forest Research Bulletin; 1967.

    Google Scholar 

  12. Boaler S, Sciwale K. Ecology of a miombo site, Lupa North Forest Reserve, Tanzania III. Effects on the vegetation of local cultivation practices. J Ecol. 1966;54:577–87.

    Google Scholar 

  13. Malaise F. Phenology of the Zambezian woodland area with emphasis on the miombo ecosystem. In: Jacobs J, Lange V, Olson J, Leith H, editors. Ecological studies No. 8. Phenology and seasonality modeling. London: Chapman and Hall; 1974. p. 269–86.

    Google Scholar 

  14. Malaise F. The miombo system. In: Natural resources research XIV: tropical forest ecosystems. Paris; 1978.

  15. Campbell B, Byron N. Miombo woodlands and their use: overview and key issues. In: Campbell B, editor. The miombo in transition: woodlands and welfare in Africa. Bogor: CIFOR; 1996. p. 221–30.

    Google Scholar 

  16. Malmer A. General ecological features of miombo woodlands and considerations for utilization and management. In: MITMIOMBO—management of indigenous tree species for ecosystem restoration and wood production in semi-arid miombo woodlands in Eastern Africa. Proceedings of the First MITMIOMBO Project Workshop. Morogoro, Tanzania; 2007. p. 34–42.

  17. Jew EK, Dougill AJ, Sallu SM, O’Connell J, Benton TG. Miombo woodland under threat: consequences for tree diversity and carbon storage. Forest Ecol Manag. 2016;361:144–53.

    Google Scholar 

  18. Jeltsch F, Weber G, Grimm V. Ecological buffering mechanisms in savannas: a unifying theory of long-term tree-grass coexistence. Plant Ecol. 2000;150:161–71.

    Google Scholar 

  19. Clarke J, Cavendish W, Coote C. Rural households and miombo woodlands: use, value and management. In: Campbell B, editor. The miombo in transition: woodlands and welfare in Africa. Bogor: CIFOR; 1996. p. 101–35.

    Google Scholar 

  20. Ribeiro N, Syampungani S, Matakala N, Nangoma D, Ribeiro-Barros A. Miombo woodlands research towards the sustainable use of ecosystem services in southern Africa. In: Lo Y, Blanco J, Roy S, editors. Biodiversity in ecosystems—linking structure and function. 2015.

    Google Scholar 

  21. Campbell B, Angelsen A, Cunningham A, Katerere Y, Sitoe A, Wunder S. Miombo woodlands—opportunities and barriers to sustainable forest management. Bogor. Indonesia: CIFOR; 2007.

    Google Scholar 

  22. Dewees P, Campbell B, Katerere Y, Sitoe A, Cunningham AB, Angelsen A, et al. Managing the miombo woodlands of southern Africa: policies, incentives and options for the rural poor. J Nat Resour Policy Res. 2010;2:57–73.

    Google Scholar 

  23. Lawton R. A study of the dynamic ecology of Zambian vegetation. J Ecol. 1978;66:175–98.

    Google Scholar 

  24. Bond W, Woodward F, Midgley G. The global distribution of ecosystems in a world without fire. New Phytol. 2005;165:525–38.

    CAS  Google Scholar 

  25. Ryan CM, Williams M, Grace J. Above- and belowground carbon stocks in a miombo wooodland landscape of Mozambique. Biotropica. 2011;43:423–32.

    Google Scholar 

  26. Anderson D, Grove R. Introduction: The Scramble for Eden: past, present, and future in African conservation. In: Anderson D, Grove R, editors. Conservation in Africa: people, policies, and practice. Cambridge: Cambridge University Press; 1987. p. 1–12.

    Google Scholar 

  27. Adams JS, McShane TO. The myth of wild Africa: conservation without illusion. Berkeley: University of California Press; 1996.

    Google Scholar 

  28. UNEP. Millennium Ecosystem Assessment: Ecosystems and human well-being: synthesis. United Nations Environment Programme. Washington, DC; 2005.

  29. Tarimo B, Dick OB, Gobakken T, Totland O. Spatial distribution of temporal dynamics in anthropogenic fires in miombo savanna woodlands of Tanzania. Carbon Balance Manag. 2015;10:18.

    Google Scholar 

  30. Tucker RP, Richards JF, editors. Global deforestation and the nineteenth-century world economy. Durham: Duke University Press; 1983.

    Google Scholar 

  31. Neumann R. Imposing wilderness: struggles over livelihood and nature preservation in Africa. Berkeley: University of California Press; 1998.

    Google Scholar 

  32. Adams M, Sibanda S, Turner S. Land tenure reform and rural livelihoods in southern Africa. London: Overseas Development Institute; 1999.

    Google Scholar 

  33. Palmer R. Land policy in Africa: lessons from recent policy and implementation processes. In: Toulmin C, Quan J, editors. Evolving land rights, policy and tenure in Africa. London: International Institute for Environment and Development (IIED); 2000. p. 267–88.

    Google Scholar 

  34. SADC. Protocol on forestry. Southern African Development Community. Gaberone; 2002.

  35. SADC. Forestry strategy 2010–2020. Making forests work for the economic development of the region. Gaberone; 2010.

  36. Programme on Climate Change Adaptation and Mitigation in the Eastern and Southern Africa (COMESA-EAC-SADC) Region. COMESA Secretariat. Lusaka; 2011.

  37. German L, Mandondo A, Paumgarten F, Mwitwa J. Shifting rights, property and authority in the forest frontier: “stakes” for local land users and citizens. J Peasant Stud. 2014;41:51–78.

    Google Scholar 

  38. Gumbo D, Mumba K, Kaliwile M, Moombe K, Mfuni T. Agrarian changes in the Nyimba District of Zambia. In: Deakin L, Kshatriya M, Sunderland T, editors. Agrarian change in tropical landscapes. Bogor: CIFOR; 2016.

    Google Scholar 

  39. German L, Gumbo D, Schoneveld G. Large-scale investments in chitemene farmland: exploring the marginal lands narrative in Zambia’s Northern Province. QA-Riv dell’Associazione Ross. 2013;2:109–35.

    Google Scholar 

  40. McCracken J. Colonialism, capitalism and the ecological crisis in Malawi: a reassessment. In: Anderson D, Grove R, editors. Conservation in Africa, people, policies and practice. Cambridge: Cambridge University Press; 1987. p. 63–77.

    Google Scholar 

  41. MacKenzie J. Empire and the ecological apocalypse: the historiography of the imperial environment. In: Griffiths T, Robin L, editors. Ecology and empire: environmental history of settler societies. Seattle: University of Washington Press; 1997. p. 176–9.

    Google Scholar 

  42. AUC-ECA-AFDB Consortium Land Policy Initiative. Land policy in Africa: southern African Regional Assessment. 2010.

  43. Kwashirai VC. Green colonialism in Zimbabwe, 1890–1980. Amherst: Cambria Press; 2009.

    Google Scholar 

  44. Grainger A. Constraints on modelling the deforestation and degradation of tropical open woodlands. Glob Ecol Biogeogr. 1999;8:179–90.

    Google Scholar 

  45. Ciais P, Piao S, Cadule P, Friedlingstein P, Chédin A. Variability and recent trends in the African terrestrial carbon balance. Biogeosciences. 2009;6:1935–48.

    CAS  Google Scholar 

  46. Hulme M, editor. Climate change and southern Africa: an exploration of some potential impacts and implications for the SADC region. UK: Norwich; 1996.

    Google Scholar 

  47. Gibbs H, Brown S, Niles J, Foley J. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett. 2007;2:1–13.

    Google Scholar 

  48. Kell D. Large-scale sequestration of atmospheric carbon via plant roots in natural and agricultural ecosystems: why and how. Philos Trans R Soc Lond B. 2012;367:1589–97.

    CAS  Google Scholar 

  49. Richardson A, Zu Dohna H. Predicting root biomass from branching patterns of Douglas-fir root systems. Oikos. 2003;100:96–104.

    Google Scholar 

  50. Smith P. Soils as carbon sinks: the global context. Soil Use Manag. 2004;20:212–8.

    Google Scholar 

  51. Grundy I. Regeneration and management of Brachystegia spiciformis and Julbernardia globiflora in Miombo woodlands of Zimbabwe. University of Oxford; 1995.

  52. Geldenhuys C. Basic guidelines for silvicultural and management practices in Mozambique. South Africa: Pretoria; 2005.

    Google Scholar 

  53. Fanshawe D. The Vegetation of Zambia. Lusaka: Government Printer; 1971.

    Google Scholar 

  54. Strang R. Some man-made changes in successional trends on the Rhodesian Highveld. J Appl Ecol. 1974;111:249–63.

    Google Scholar 

  55. Chidumayo EN. Wood used in charcoal production in Zambia. Report prepared for the World Wildlife Fund. Washington, D.C.; 1993

  56. Chidumayo EN. Effects of accidental and prescribed fires on miombo woodland, Zambia. Commonw For Rev. 1997;76:268–72.

    Google Scholar 

  57. Chidumayo EN. Development of Brachystegia–Julbernardia woodland after clear-felling in central Zambia: evidence for high resilience. Appl Veg Sci. 2004;7:237–42. https://doi.org/10.1111/j.1654-109X.2004.tb00615.

    Google Scholar 

  58. Luoga EJ, Witkowski ETF, Balkwill K. Regeneration by coppicing (resprouting) of miombo (African savanna) trees in relation to land use. For Ecol Manag. 2004;189:23–35. https://doi.org/10.1016/j.foreco.2003.02.001.

    Google Scholar 

  59. Chirwa P, Syampungani S, Geldenhuys C. The ecology and management of the miombo woodlands for sustainable livelihoods in southern Africa: the case for non-timber forest products. South For J For Sci. 2008;70:237–45.

    Google Scholar 

  60. Syampungani S. Vegetation change analysis and ecological recovery of the Copperbelt miombo woodlands. Ph.D. Thesis. University of Stellenbosch, South Africa; 2008.

  61. Vinya R, Malhi Y, Brown N, Fisher J. Functional coordination between branch hydraulic properties and leaf functional traits in miombo woodlands: implications for water stress management and species habitat preference. Acta Physiol Plant. 2012;34:1701–10.

    CAS  Google Scholar 

  62. Vincens A. Late quaternary vegetation history of the South-Tanganyika basin. Climatic implications in South Central Africa. Palaeogeogr Palaeoclimatol Palaeoecol. 1991;86:207–26.

    Google Scholar 

  63. Desanker P, Frost P, Frost C, Justice C, Scholes R. The miombo network: framework for a terrestrial transect study of land-use and land-cover change in the miombo ecosystems of central Africa. Sweden: Stockholm; 1997.

    Google Scholar 

  64. Mistry J. Ecology and Human Use., Woodland SavannasEdinburgh: Pearson Education Limited; 2000.

    Google Scholar 

  65. Scott L. Reconstruction of late quaternary paleoenvironments in the transvaal region, South Africa, on the basis of palynological evidence. In: Vogel J, editor. Late Cainozoic Palaeoclimates of the Southern Hemisphere. Rotterdam: Balkema; 1984. p. 317–27.

    Google Scholar 

  66. Woollen E, Ryan C, Williams M. Carbon stocks in an African woodland landscape: spatial distributions and scales of variation. Ecosystems. 2012;15:804–18.

    CAS  Google Scholar 

  67. Syampungani S, Clendenning J, Gumbo D, Nasi R, Moombe K, Chirwa P, et al. The impact of land use and cover change on above and below-ground carbon stocks of the miombo woodlands since the 1950s: a systematic review protocol. Environ Evid. 2014;3:25.

    Google Scholar 

  68. Chidumayo EN, Gumbo DJ. The dry forests and woodlands of Africa. Bogor: CIFOR; 2010.

    Google Scholar 

  69. IUCN. IUCN protected area categories. https://www.iucn.org/theme/protected-areas/about/protected-areas-categories. Accessed 19 Sep 2017.

  70. Burgess N, Butynski T, Cordeiro N, Doggart N, Fjeldsa J, Howell K, et al. The biological importance of the Eastern Arc mountains of Tanzania and Kenya. Biol Conserv. 2007;134:209–31.

    Google Scholar 

  71. Maxwell S, Fuller R, Brooks T, Watson J. The ravages of guns, nets and bulldozers. Nature. 2016;536:143–5.

    CAS  Google Scholar 

  72. Hansungule M, Feeny P, Palmer R. Report on land tenure, insecurity on the Zambian Copperbelt. Oxford: Oxfam; 1998.

    Google Scholar 

  73. Adams W. Nature and the colonial mind. In: Adams W, Mulligan M, editors. Decolonizing nature: strategies for conservation in a post-colonial era. London: Earthscan; 2003.

    Google Scholar 

  74. Viera A, Garrett J. Understanding interobserver agreement: the kappa statistic. Fam Med. 2005;37:360–3.

    Google Scholar 

  75. Randolph J. Online kappa calculator [Computer software].

  76. Bilotta G, Milner A, Boyd I. Quality assessment tools for evidence from environmental science. Environ Evid. 2014;3:14.

    Google Scholar 

  77. McGregor J. Woodland resources: ecology, policy and ideology; an historical case study of woodland use in Shurugwi communal area. Zimbabwe: Loughborough University; 1991.

    Google Scholar 

  78. Mbwambo L, Eid T, Malimbwi RE, Zahabu E, Kajembe GC, Luoga E. Impact of decentralised forest management on forest resource conditions in Tanzania. For Trees Livelihoods. 2012;21:97–113.

    Google Scholar 

  79. Roe D, Nelson F, Sandbrook C. Community management of natural resources in Africa: impacts, experiences and future directions. In: Natural resource issues. 18th edition. London: International Institute for Environment and Development (IIED); 2009.

  80. UNDP. Fact sheet: about REDD+. 2016.

  81. Lees H. Working plan for the forests supplying the Copperbelt, Western Province. Lusaka, Zambia; 1962.

  82. Chidumayo EN. A survey of wood stocks for charcoal production in the miombo woodlands of Zambia. For Ecol Manag. 1987;20:105–15. https://doi.org/10.1016/0378-1127(87)90153-8.

    Google Scholar 

  83. Chidumayo E. Woodland structure, destruction and conservation in the Copperbelt area of Zambia. Biol Conserv. 1987;40:89–100.

    Google Scholar 

  84. Chidumayo EN. A re-assessment of effects of fire on miombo regeneration in the Zambian Copperbelt. J Trop Ecol. 1988;4:361–72. https://doi.org/10.1017/S0266467400003011.

    Google Scholar 

  85. Chidumayo EN. Early post-felling response of Marquesia woodland to burning in the Zambian Copperbelt. J Ecol. 1989;77:430–8.

    Google Scholar 

  86. Kwibisa L. The effects of indigenous cultivation practices on the recovery of dry miombo woodland in Central Zambia. M.Sc., Dissertation, University of Zambia, Zambia; 2000.

  87. Chamshama S, Mugasha A, Zahabu E. Stand biomass and volume estimation for Miombo woodlands at Kitulangalo, Morogoro, Tanzania. South African For Journal. 2004;200:59–70.

    Google Scholar 

  88. Banda T, Schwartz MW, Caro T. Woody vegetation structure and composition along a protection gradient in a miombo ecosystem of western Tanzania. For Ecol Manag. 2006;230:179–85.

    Google Scholar 

  89. Williams M, Ryan CM, Rees RM, Sambane E, Fernando J, Grace J. Carbon sequestration and biodiversity of re-growing miombo woodlands in Mozambique. For Ecol Manag. 2008;254:145–55.

    Google Scholar 

  90. Ryan CM, Williams M. How does fire intensity and frequency affect miombo woodland tree populations and biomass? Ecol Appl. 2011;21:48–60.

    Google Scholar 

  91. Chomba C, Nyirenda V, Silengo M. Selective use patterns of woody plant species by local communities in Mumbwa Game Management Area: a prerequisite for effective management of woodland resources and benefit sharing. Open J Ecol. 2013;3:532–50. https://doi.org/10.4236/oje.2013.38062.

    Google Scholar 

  92. Kashindye A, Mtalo E, Mpanda M, Liwa E, Giliba R. Multi-temporal assessment of forest cover, stocking parameters and above-ground tree biomass dynamics in miombo woodlands of Tanzania. African J Environ Sci Technol. 2013;7:611–23.

    Google Scholar 

  93. Sawe TC, Munishi PKT, Maliondo SM. Woodlands degradation in the southern highlands, miombo of Tanzania: implications on conservation and carbon stocks. J Biodivers Conserv. 2014;6:230–7.

    Google Scholar 

  94. Treue T, Ngaga YM, Meilby H, Lund JF, Kajembe G, Iddi S, et al. Does participatory forest management promote sustainable forest utilisation in Tanzania? Int For Rev. 2014;16:23–38.

    Google Scholar 

  95. Guy P. Changes in the biomass and productivity of woodlands in the Sengwa Wildlife Research Area, Zimbabwe. J Appl Ecol. 1981;18:507–19. https://doi.org/10.2307/2402412.

    Google Scholar 

  96. Stromgaard P. Biomass, growth, and burning of woodland in a shifting cultivation area of South Central Africa. For Ecol Manag. 1985;12:163–78.

    Google Scholar 

  97. Chidumayo EN. Woody biomass structure and utilisation for charcoal production in a Zambian miombo woodland. Bioresour Technol. 1991;37:43–52. https://doi.org/10.1016/0960-8524(91)90110-6.

    CAS  Google Scholar 

  98. Chidumayo EN. Forest degradation and recovery in a miombo woodland landscape in Zambia: 22 years of observations on permanent sample plots. For Ecol Manag. 2013;291:154–61. https://doi.org/10.1016/j.foreco.2012.11.031.

    Google Scholar 

  99. Grundy IM, Campbell BM, Balebereho S, Cunliffe R, Tafangenyasha C, Fergusson R, et al. Availability and use of trees in Mutanda Resettlement Area, Zimbabwe. For Ecol Manag. 1993;56:243–66. https://doi.org/10.1016/0378-1127(93)90116-5.

    Google Scholar 

  100. Smith P. A reconnaissance survey of the vegetation of the North Luangwa National Park, Zambia. Bothalia. 1998;28:197–211.

    Google Scholar 

  101. Sambane E. Above ground biomass accumulation in fallow fields at the Nhambita Community, Mozambique. A dissertation presented for the degree of Master of Science. Edinburgh, University of Edinburgh; 2005.

  102. Kutsch WL, Merbold L, Ziegler W, Mukelabai MM, Muchinda M, Kolle O, et al. The charcoal trap: miombo forests and the energy needs of people. Carbon Balance Manag. 2011;6:5. https://doi.org/10.1186/1750-0680-6-5.

    CAS  Google Scholar 

  103. Ando K, Shinjo H, Noro Y, Takenaka S, Miura R, Sokotela S, et al. Short-term effects of fire intensity on soil organic matter and nutrient release after slash-and-burn in Eastern Province, Zambia. Soil Sci Plant Nutr. 2015;2014(60):173–82. https://doi.org/10.1080/00380768.2014.883487.

    Google Scholar 

  104. Ryan CM, Berry NJ, Joshi N. Quantifying the causes of deforestation and degradation and creating transparent REDD+ baselines: a method and case study from central Mozambique. Appl Geogr. 2014;53:45–54. https://doi.org/10.1016/j.apgeog.2014.05.014.

    Google Scholar 

  105. Hofstad O, Araya MM. Optimal wood harvest in miombo woodland considering REDD+ payments—a case study at Kitulangalo Forest Reserve, Tanzania. For Policy Econ. 2015;51:9–16. https://doi.org/10.1016/j.forpol.2014.11.002.

    Google Scholar 

  106. Shirima DD, Totland Ø, Munishi PKT, Moe SR. Relationships between tree species richness, evenness and aboveground carbon storage in montane forests and miombo woodlands of Tanzania. Basic Appl Ecol. 2015;16:239–49. https://doi.org/10.1016/j.baae.2014.11.008.

    Google Scholar 

  107. Chidumayo EN. Estimating tree biomass and changes in root biomass following clear-cutting of Brachystegia–Julbernardia (miombo) woodland in central Zambia. Environ Conserv. 2013;41:54–63. https://doi.org/10.1017/S0376892913000210.

    Google Scholar 

  108. Mugasha W, Eid T, Bollandsås O, Malimbwi R, Chamshama S, Zahabu E, et al. Allometric models for prediction of above- and belowground biomass of trees in the miombo woodlands of Tanzania. For Ecol Manag. 2013;310:87–101.

    Google Scholar 

  109. Araki S. Effect on soil organic matter and soil fertility of the chitemene slash-and-burn practice used in northern Zambia. In: Mulongoy K, Merckx R, editors. Soil organic matter dynamics and sustainability of tropical agriculture. Leuven: The Netherlands; 1993. p. 367–75.

    Google Scholar 

  110. Chidumayo EN, Kwibisa L. Effects of deforestation on grass biomass and soil nutrient status in miombo woodland, Zambia. Agric Ecosyst Environ. 2003;96:97–105. https://doi.org/10.1016/S0167-8809(02)00229-3.

    Google Scholar 

  111. Mapanda F, Mupini J, Wuta M, Nyamangara J, Rees RM. A cross-ecosystem assessment of the effects of land cover and land use on soil emission of selected greenhouse gases and related soil properties in Zimbabwe. Eur J Soil Sci. 2010;61:721–33.

    CAS  Google Scholar 

  112. Mapanda F, Munotengwa S, Wuta M, Nyamugafata P, Nyamangara J. Short-term responses of selected soil properties to clearing and cropping of miombo woodlands in central Zimbabwe. Soil Tillage Res. 2013;129:75–82. https://doi.org/10.1016/j.still.2013.01.008.

    Google Scholar 

  113. Muposhi V, Ndlovu N, Gandiwa E, Muvengwi J, Muboko N. Vegetation dynamics prior to wildlife reintroductions in southern Umfurudzi Park, Zimbabwe. J Anim Plant Sci. 2014;24:1680–90.

    Google Scholar 

  114. Jenkinson DS, Meredith J, Kinyamario J, Warren GP, Wong MTF, Harkness DD, et al. Estimating net primary production from measurements made on soil organic matter. Ecology. 1999;80:2762–73.

    Google Scholar 

  115. Walker S, Desanker P. The impact of land use on soil carbon in miombo woodlands of Malawi. For Ecol Manag. 2004;203:345–60. https://doi.org/10.1016/j.foreco.2004.08.004.

    Google Scholar 

  116. Anonymous. The impact of agriculture activities on soil carbon in miombo woodlands of Mozambique. Edinburgh, University of Edinburgh; 2005.

  117. Rossi J, Govaerts A, De Vos B, Verbist B, Vervoort A, Poesen J, et al. Spatial structures of soil organic carbon in tropical forests—a case study of southeastern Tanzania. CATENA. 2009;77:19–27.

    CAS  Google Scholar 

  118. Ando K, Shinjo H, Kuramitsu H, Miura R, Sokotela S, Funakawa S. Effects of cropping and short-natural fallow rotation on soil organic carbon in the Eastern Province of Zambia. Agric Ecosyst Environ. 2014;196:34–41. https://doi.org/10.1016/j.agee.2014.06.012.

    Google Scholar 

  119. Shelukindo H, Semu E, Msanya B, Singh B, Munishi P. Soil organic carbon stocks in the dominant soils of the miombo woodland ecosystem of Kitonga Forest Reserve, Iringa, Tanzania. Int J Agric Policy Res. 2014;2:167–77.

    Google Scholar 

  120. Winowiecki L, Vågen T-G, Huising J. Effects of land cover on ecosystem services in Tanzania: a spatial assessment of soil organic carbon. Geoderma. 2016;263:274–83. https://doi.org/10.1016/j.geoderma.2015.03.010.

    CAS  Google Scholar 

  121. Abbot JIO, Homewood K. A history of change: causes of miombo woodland decline in a protected area in Malawi. J Appl Ecol. 1999;36:422–33.

    Google Scholar 

  122. Hogan R, Mwambeso PA, Nandi RXL, Elbariki R, Chande MO. Rural community capacity-building through increasing local control over forest resources—the case of Tanzania through the example of village communities in Rufiji district. In: Wall S, editor. Small-scale forestry and rural development: the intersection of ecosystems, economics and society; Proceedings from the small-scale forestry and rural development conference held in Galway, Ireland. Galway, Ireland; 2006. p. 164–77

  123. Grace J, Jose JS, Meir P, Miranda HS, Montes RA. Productivity and carbon fluxes of tropical savannas. J Biogeogr. 2006;33:387–400. https://doi.org/10.1111/j.1365-2699.2005.01448.x.

    Google Scholar 

  124. Culas R. Impact of credit policy on agricultural expansion and deforestation. Trop Agric Res. 2003;15:276–87.

    Google Scholar 

  125. Davies G, Pollard L, Mwenda M. Perceptions of land-degradation, forest restoration and fire management: a case study from Malawi. L Degrad Dev. 2010;21:546–56.

    Google Scholar 

  126. Chidumayo EN. Changes in miombo woodland structure under different land tenure and use systems in Central Zambia. J Biogeogr. 2002;29:1619–26.

    Google Scholar 

  127. Luoga EJ, Witkowski ETF, Balkwill K. Harvested and standing wood stocks in protected and communal miombo woodlands of eastern Tanzania. For Ecol Manag. 2002;164:15–30.

    Google Scholar 

  128. Ahrends A, Burgess NDMS. Predictable waves of sequential forest degradation and biodiversity loss spreading from an African city. Proc Natl Acad Sci. 2010;107:14556–61.

    Google Scholar 

  129. Geldmann J, Joppa L, Burgess N. Mapping change in human pressure globally on land and within protected areas. Conserv Biol. 2014;28:1604–16.

    Google Scholar 

  130. Chidumayo EN. Miombo ecology and management: an introduction. London: Intermediate Technology Publications Ltd (ITP); 1997.

    Google Scholar 

  131. Frost P, Nasi R. What are the prospects for reducing deforestation and increasing carbon storage in African dry forests and savannas? In: Session 54 (African Ecology). Bali: Association for Tropical Biology and Conservation; 2010.

  132. Trapnell CG, Friend MT, Chamberlain GTBH. The effects of fire and termites on a Zambian woodland soil. J Ecol. 1976;64:577–88.

    CAS  Google Scholar 

  133. Chidumayo EN. Responses of miombo to harvesting: ecology and management. Stockholm: Sweden; 1993.

    Google Scholar 

  134. Southern African Development Community (SADC). Forests, rangelands and climate change in southern Africa. Forests and Climate Change Working Paper 12. Gaberone; 2002.

  135. Gumbo D, Moombe K, Kandulu M, Kabwe G, Ojanen M, Ndhlovu E, et al. Dynamics of the charcoal and indigenous timber trade in Zambia: a scoping study in Eastern, Northern and Northwestern provinces. Occasional Paper 86. CIFOR. Bogor; 2013.

  136. Wegmann M, Santini L, Leutner B, Safi K, Rocchini D, Bevanda M, et al. Role of African protected areas in maintaining connectivity for large mammals. Philos Trans R Soc Lond B Biol Sci. 2014;369:20130193.

    Google Scholar 

  137. Cumming D, Fenton M, Rautenbach I, Taylor R, Cumming G, Cumming M, et al. Elephants, woodlands and biodiversity in southern Africa. S Afr J Sci. 1997;93:231–6.

    Google Scholar 

  138. Gandiwa P, Matsvayi M, Ngwenya M, Gandiwa E. Assessment of livestock and human settlement encroachment into northern Gonarezhou National Park, Zimbabwe. J Sustain Dev Afr. 2011;13:19–33.

    Google Scholar 

  139. GIZ. Development of integrated MRV systems for REDD+ in the SADC region. 2012.

  140. COMESA. COMESA climate change initiative: progress and prospects 2008–2011. Lusaka; 2011.

  141. Southern African Development Community (SADC). SADC support programme on Reducing Emissions from Deforestation and Forest Degradation (REDD). Gaborone: SADC Secretariat; 2011.

  142. Nhantumbo I, Izidine S. Preparing for REDD in dryland forests: investigating the options and potential synergy for REDD payments in the miombo eco-region: Mozambique country study. London: International Institute for Environment and Development (IIED); 2009.

  143. Bond I, Chambwera M, Jones B, Chundama M, Nhantumbo I. REDD+ in dryland forests: issues and prospects for pro-poor REDD in the miombo woodlands of southern Africa (No. 21). IIED. London; 2010.

  144. GRZ. Zambia national strategy to reduce emissions from deforestation and forest degradation (REDD+). Ministry of Lands, Natural Resources and Environmental Protection/Forest Department. Lusaka; 2015.

  145. COMESA. The African climate solution: unlocking African’s potential in the global climate regime. An initiative by the Regional Economic Commission COMESA, EAC and SADC. Lusaka; 2011.

  146. Chidumayo EN. A shifting cultivation land use system under population pressure in Zambia. Agrofor Syst. 1987;5:15–25.

    Google Scholar 

  147. Araki S. The role of miombo woodland ecosystem in chitemene shifting cultivation in northern Zambia. Jpn InforMAB. 1992;11:8–15.

    Google Scholar 

  148. Luoga EJ, Witkowski ETF, Balkwill K. Subsistence use of wood products and shifting cultivation within a miombo woodland of eastern Tanzania, with some notes on commercial uses. S Afr J Bot. 2000;66:72–85.

    Google Scholar 

  149. Turner B, Skole D, Sanderson S, Fischer G, Fresco L, Leemans R. Land-use and land-cover change (LUCC): science/research plan, HDP Report No. 7. Laxenburg; 1995.

  150. Geist H, Lambin E. Proximate causes and underlying driving forces of tropical deforestation: tropical forests are disappearing as the result of many pressures, both local and regional, acting in various combinations in different geographical locations. Bioscience. 2002;52:143–50.

    Google Scholar 

  151. Mkandawire P, Soludo C. Our continent, our future: African perspectives on structural adjustment. IDRC; 1999.

  152. Trapnell C. Ecological results of woodland burning experiments in northern Rhodesia. J Ecol. 1959;47:129–68.

    Google Scholar 

Download references

Authors’ contributions

Emmanuel Chidumayo (EC) carried out quantitative data analysis and drafted the manuscript, with additional inputs from JC, who coordinated the work, and IG, CM and GP. JC, DG, KYM, KBM, IG, RN, GK, CM and RN screened studies; and EC, JC, DG, KYM, KBM appraised the studies and extracted data. All authors read and approved the manuscript.

Acknowledgements

EC assisted CIFOR Zambia office in Lusaka in undertaking this study.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

All data generated or analysed during this study are included in this published article (and Additional files 1, 2, 3, 4) and most are publicly accessible. Some data that support the findings of this study are from older studies and are not publicly available. Other information and data needed are available from the corresponding author upon reasonable request.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Funding

This research review was financed from the United Kingdom’s Department for International Development (DfiD) through CIFOR’s Evidence-Based Forestry Initiative. CM gratefully acknowledges funding support from the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) with financial support from the donors contributing to the CGIAR Fund, from the Norwegian Agency for Development Cooperation (Norad), and from the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB).

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Authors and Affiliations

  1. Center for International Forestry Research (CIFOR), Lusaka, Zambia

    Davison Gumbo

  2. Center for International Forestry Research (CIFOR), Bogor, Indonesia

    Jessica Clendenning, Christopher Martius & Robert Nasi

  3. Independent Consultant, Lusaka, Zambia

    Kaala Moombe & Kondwani Y. Mumba

  4. Institute of Environmental Studies, University of Zimbabwe, Harare, Zimbabwe

    Isla Grundy

  5. Oxford Long-term Ecology Lab, Department of Zoology, University of Oxford, Oxford, UK

    Gillian Petrokofsky

  6. Department of Forest Engineering, Faculty of Agronomy and Forest Engineering, Eduardo Mondlane University, Maputo, Mozambique

    Natasha Ribeiro

  7. Department of Plant and Environmental Sciences, School of Natural Resources, Copperbelt University, Kitwe, Zambia

    Gillian Kabwe

Authors
  1. Davison Gumbo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jessica Clendenning
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christopher Martius
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaala Moombe
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Isla Grundy
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Robert Nasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kondwani Y. Mumba
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Natasha Ribeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Gillian Kabwe
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Gillian Petrokofsky
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jessica Clendenning.

Additional files

Additional file 1.

Literature search; showing literature sources, search strings, and keywords used.

Additional file 2.

Study validity table; showing critical appraisal process.

Additional file 3.

Additional information; showing the quantitative data used in the analysis.

Additional file 4.

Excluded studies at full text and study appraisal levels.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gumbo, D., Clendenning, J., Martius, C. et al. How have carbon stocks in central and southern Africa’s miombo woodlands changed over the last 50 years? A systematic map of the evidence. Environ Evid 7, 16 (2018). https://doi.org/10.1186/s13750-018-0128-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13750-018-0128-0

Keywords

Environmental Evidence

ISSN: 2047-2382