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Article

Conserving Carbon Stocks Under Climate Change: Importance of Trees Outside Forests in Agricultural Landscapes of Mongala Province, Democratic Republic of Congo

by
Jean Pierre Azenge
1,2,*,
Aboubacar-Oumar Zon
3,
Hermane Diesse
4,
Jean Pierre Pitchou Meniko
1,
Jérôme Ebuy
5,
Justin N’Dja Kassi
2 and
Paxie W. Chirwa
6
1
Filière Gestion des Ressources Naturelles Renouvellables, Institut Facultaire des Sciences Agronomiques de Yangambi (IFA-Yangambi), Kisangani P.O. Box 1232, Democratic Republic of the Congo
2
UFR Biosciences, Université Félix Houphouët-Boigny, Abidjan P.O. Box 582, Côte d’Ivoire
3
Département des Sciences de la Vie et de la Terre, UFR Sciences et Technologies, Université Lédéa Bernard OUEDRAOGO, Ouahigouya 01 BP 346, Burkina Faso
4
Department of Natural Resource Science, Namibia University of Science and Technology, 13 Jackson Kaujeua Street, Windhoek 13388, Namibia
5
Faculté de Gestion des Ressources Naturelles Renouvelables, Université de Kisangani (UNIKIS), Kisangani P.O. Box 2012, Democratic Republic of the Congo
6
Department of Plant and Soil Sciences, University of Pretoria, Pretoria 0022, South Africa
*
Author to whom correspondence should be addressed.
Earth 2025, 6(2), 19; https://doi.org/10.3390/earth6020019
Submission received: 26 February 2025 / Revised: 20 March 2025 / Accepted: 25 March 2025 / Published: 27 March 2025
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Abstract

:
This study aimed to evaluate the role of trees outside forests on agricultural land (TOF-AL) in preserving the initial aboveground biomass (AGB) of forests within the agricultural landscape of Mongala province in the Democratic Republic of Congo. In 2024, tree inventories were conducted over four months in the forests and agricultural lands of Mongala province to analyse AGB. The effects of artisanal logging and charcoal production activities on the AGB conservation rate were considered. This study indicates that 78.3% of the trees encountered in agricultural lands were large-diameter trees (diameter at breast height (DBH) ≥ 60 cm). In forest areas, large-diameter trees accounted for 55.9% of tree density. The average AGBs are 66.8 Mg ha−1 for TOF-AL and 373.5 Mg ha−1 for forest trees. The AGB of TOF-AL accounts for 17.9% of the AGB of the total forest trees. The AGB conservation rates vary by region, with Lisala having the highest at 22.1%, Bumba the lowest at 11.2%, and Bongandanga at 20.5%. Artisanal logging and charcoal production reduce the AGB conservation rate of TOF-AL. The AGB conservation rate is positively correlated with the distances to major cities. These results prove that conserving trees in agricultural landscapes can reduce the AGB losses associated with slash-and-burn agriculture and contribute to mitigating climate change effects.

1. Introduction

Covering an estimated area of around 42 million km2, forests account for a significant portion of the land and store approximately 45% of the terrestrial carbon stock [1,2]. Tropical forests provide approximately two-thirds of this carbon stock [2], making them crucial ecosystems for global climate balance [3,4,5,6]. Tropical forests are renowned for their exceptionally rich animal and plant biodiversity, which has long drawn the interest of scientists [7,8,9]. In addition, they provide habitats for millions of people who live there and rely on them for survival [10,11,12,13].
However, these forests are facing unprecedented levels of deforestation due to human activities [14,15,16,17]. Traditional and industrial agriculture are the main drivers of deforestation [18,19]. In tropical regions, where much of the population lives below the poverty line, wood energy is the primary domestic energy source and significantly contributes to deforestation [20,21,22,23,24]. Industrial logging also contributes to deforestation and forest degradation in tropical regions [17]. Current trends suggest that this deforestation may continue in the coming years [25,26].
The loss of tropical forests has numerous negative effects on the environment, biodiversity, and the survival of local populations. It results in the loss of biodiversity [27,28,29], a decline in forest carbon stocks [30,31,32], and a reduction in livelihoods for local populations [33].
Many options are being considered to mitigate the adverse effects of human activities on forests, biodiversity, climate, and people’s livelihoods. Among these options, forest plantations are considered an excellent means to provide forest goods and services while enhancing carbon sequestration [34,35]. Agroforestry and traditional practices of tree conservation on agricultural lands are also being encouraged [36,37]. Indeed, these practices hold great potential to contribute to biodiversity protection, provide wood energy, and offer many non-timber forest products necessary for local populations’ survival [38,39,40,41]. They can also be vital in carbon sequestration [42,43,44]. Therefore, thorough studies are essential to guarantee the effectiveness of these practices in addressing current challenges [45].
This research is of utmost importance in this context as it focuses on the role of trees outside forests on agricultural land (TOF-AL) in preserving carbon stocks in the Mongala province of the Democratic Republic of Congo (DRC). As is the case throughout the country, Mongala province faces significant deforestation, primarily due to slash-and-burn agriculture, logging, and wood energy [46,47]. Due to the province’s inaccessibility, deforestation is focused in its three main towns: Bumba, Bongandanga, and Lisala [47]. For the population of this province, predominantly poor and reliant on forests, this deforestation results in numerous socio-economic challenges [48]. Additionally, deforestation leads to the destruction of forest carbon stocks in this province [47]. However, there is also a recurring practice of conserving certain tree species on agricultural land. These trees, referred to as “trees outside the forest on agricultural land,” are generally preserved for the various socio-economic benefits that populations attribute to them [49]. Indeed, as demonstrated in many other tropical regions, retaining these trees on agricultural land, despite their dispersed distribution, can collectively form a substantial carbon sink. This may not only help mitigate carbon losses linked to deforestation in Mongala province but also contribute to broader climate change mitigation efforts [50,51]. Therefore, the main hypothesis of this research is that TOF-AL plays a significant role in conserving aboveground biomass (AGB) stock in the agricultural landscapes of Mongala province in the DRC. This conservation helps mitigate carbon losses associated with slash-and-burn agriculture and enhances resilience to climate change. However, the effectiveness of this conservation may be negatively affected by human pressures, such as artisanal logging and charcoal production. Additionally, it is positively correlated with the distance from major urban centres. This study seeks to assess the significance of TOF-AL in preserving carbon stocks within the agricultural landscapes of Mongala province. It aims to understand the impact of human activities and proximity to urban areas on this conservation function while also providing evidence that supports initiatives for the conservation and sustainable management of tree resources in the agricultural landscapes of this region.

2. Materials and Methods

2.1. Study Area

This research was conducted in Mongala province (Figure 1). Covering an estimated area of 58,141 km2, it is the smallest of the 26 provinces in the DRC. However, it is larger than some countries, such as Belgium, Burundi, Gambia, and Togo. Mongala province is divided into three territories, Bongandanga, Bumba, and Lisala, which are characterised by extensive forested areas [52,53]. From an administrative perspective, every territory is segmented into collectivities, with each collectivity further subdivided into groups that encompass a collection of villages. About two-thirds of the territory of Mongala province is covered by dense, humid forests or forests on hydromorphic soil. It is crossed by the Congo River from west to east, thus forming two more or less distinct physical entities. In the northern part (Bumba and Lisala), characterised by dense humid forests, there is a considerable number of agricultural complexes (about 22% of the area). The southern part is dominated by humid tropical forests associated with forests on hydromorphic soils along the hydrographic network [54]. There is a wide variety of soil types, but the most dominant are sandy soil, sandy clay soil, and lateritic soil. These are generally acidic soils, with a pH between 4 and 6. Its basic vegetation is the evergreen rainforest. However, numerous agricultural pressures have increased secondary forest areas, with a tendency towards savannah in some places [54]. The population is primarily rural and mainly engages in slash-and-burn agriculture for livelihood [55]. Bumba is the smallest of the three territories but the most highly populated [54], where agricultural activities are particularly intense, leading to substantial deforestation [44,48]. The Bongandanga territory retains much of its forest cover, primarily due to the lack of road infrastructure in the region. In this territory and the two adjacent ones, local communities rely on forests for their livelihoods [47].
The province experiences a hot and humid equatorial climate, with annual precipitation ranging from 1800 to 2000 mm [54]. The climate is characterised by two dry seasons and two rainy seasons. The average temperatures range from 24 °C to 25 °C, with the maximum temperatures reaching 30 °C and the minimums dropping to 19 °C.

2.2. Sampling Design and Data Collection

As defined by [56], trees outside forests (TOFs) can be conceptualised as trees located neither within forests nor on other wooded lands. According to de Foresta (2017) [57], trees outside forests on agricultural lands (TOF-AL) constitute one of the three primary categories of TOFs. This category encompasses all trees and/or shrubs on lands predominantly utilised for agricultural purposes, irrespective of their spatial arrangement (linear, clustered, dispersed), area, strip width, or vegetation cover [57].
A stratified random sampling approach was employed to ensure the representative sampling of trees outside forests on agricultural lands (TOF-AL) across the province. An inventory was conducted in 45 villages, distributed across nine collectivities, with three collectivities selected per territory. Collectivities were chosen to encompass the province’s ecological and socio-economic conditions. Five villages were selected within each collectivity using a spatially balanced random sampling design, ensuring even geographical distribution and minimising clustering bias. This design considered the villages’ relative distance from major urban centres, specifically Bumba, Bongandanga, and Lisala, without predetermining specific distance thresholds.
A linear transect was systematically established in each selected village, extending from the village periphery to the nearest intact forest edge. TOF-AL were inventoried within 20 agricultural fields along each transect, utilising a systematic sampling method. Fields were assigned sequential numbers from the village to the forest edge to mitigate selection bias. Only fields with even-numbered assignments containing TOF-AL were included in the inventory, ensuring objectivity and preventing subjective field selection. This consistent approach was maintained across all 45 villages.
In total, 900 fields containing TOF-AL were inventoried. The area of each field was accurately determined using a Garmin 62s GPS device. Diameter at breast height (DBH) measurements for all trees within each field were taken. Only trees with a DBH of 10 cm or greater were included in the analysis to maintain comparability with existing tropical forest carbon storage studies. Qualitative data on charcoal production and artisanal logging presence were collected through semi-structured interviews with field owners in each village, ensuring consistent data collection protocols were followed. The distance from each village to the nearest major city was meticulously recorded.
A standardised one-hectare inventory plot was established in the nearest intact forest to each village to establish a comparative baseline with forest data. All trees with a DBH of 10 cm or greater were identified and measured within these plots, ensuring consistency with the TOF-AL inventory. This resulted in 45 hectares of forest inventory, representing the full range of tropical forest types within the central Congolese basin, including monodominant and mixed forests. The application of consistent methodologies across all sampling points ensured the robustness and reliability of the data collected.

2.3. Data Processing

The tree density in the field is determined by the corresponding tree counts and area of each field, as outlined below:
T d e n s = N S .
T d e n s : tree density in the field; N: number of all trees over 10 cm; S: area in hectares.
To estimate the aboveground biomass of trees, we used the two-way allometric equation from Fayolle et al. [58]:
A G B = ρ × e − 1.183 + 1.940 ×   ln ( D ) + 0.239 × ( ln ( D ) ) 2 − 0.0285 × ( ln ( D ) ) 3 .
This equation estimates the aboveground biomass (AGB, in kg) of each tree based on the diameter at breast height (DBH, represented as D in cm) and the specific wood density (represented as ρ in g/cm3). Wood density data were sourced from the Global Wood Density Database [59]. The average density values for each species were used. If there was no correspondence at the species level, the average values for the genus level were employed [60]. According to their diameter, trees were classified into small (DBH 10–40 cm), medium (DBH 40–60 cm), and large (DBH ≥ 60 cm) to allow for a comparison with other pan-tropical studies [61,62].
The calculated AGB in kg was then converted to megagrams (Mg) by dividing by 1000. By knowing the area of each field, the total AGB for the field was adjusted to express it in AGB per hectare (AGB ha−1), and the average was calculated for each village. In total, 45 observations of AGB ha−1 were obtained for the trees in question. The AGB conservation rate (AGBCR) of trees outside forests on agricultural land, expressed as a percentage, was then calculated using the following formula:
A G B C R = A G B T O F A G B F × 100 .
AGBTOF represents the average AGB per hectare of TOF-AL, while AGBF indicates the AGB per hectare of trees in undisturbed forests.
The Kruskal–Wallis test was employed to compare the aboveground biomass conservation rates (AGBCRs) among the three territories. The Wilcoxon and Mann–Whitney test was used to analyse the effects of charcoal production and artisanal logging on the conservation of AGB. These analyses were conducted using the “ggbetweenstats ()” function from the “ggstatsplot” package [63]. A linear regression model was employed to assess the relationship between the distance from major urban centres and the aboveground biomass conservation rate (AGBCR). Prior to model evaluation, the normality of the model residuals was examined using the Shapiro–Wilk test. The initial analysis revealed a deviation from normality in the residuals’ distribution. Consequently, a square root transformation was applied to the AGBCR variable, implemented via the “sqrt ()” function within the R statistical software environment. The linearity assumption of the linear regression model was subsequently validated using the Ramsey RESET test. To ensure the robustness of the model, diagnostic tests were conducted to evaluate potential violations of key assumptions. Specifically, the Durbin–Watson test was utilised to detect autocorrelation in the residuals, and the Breusch–Pagan test was employed to assess the presence of heteroscedasticity. The Ramsey RESET, Durbin–Watson, and Breusch–Pagan tests were performed using the “lmtest” package [64]. The visualisation of the model was achieved using the “ggplot2” package [65]. All statistical analyses were executed within the R 4.4.3 software environment (R Core Team, 2025).

3. Results

3.1. Tree Density and Aboveground Biomass (AGB)

In the forest, there are approximately 372.2 ± 46.3 trees per hectare. This represents an aboveground biomass of 373.5 ± 41.9 Mg ha−1. In the field, the density of TOFs is 7.7 ± 5.1 trees per hectare, representing an aboveground biomass of 66.8 ± 52.9 Mg ha−1. In the medium class, the density of trees (DBH 40–60 cm) is 316 stems per hectare, and the aboveground biomass is 88.5 Mg ha−1 (Table 1) for forest trees. In forests, large-diameter trees (DBH ≥ 60 cm) account for 6.1% of the tree density (22.8 stems per hectare) and 55.9% of the aboveground biomass (208.7 Mg ha−1). Small-diameter trees (DBH 10–40 cm), most abundant at 316 stems per hectare in forests, account for only 23.7% of the aboveground biomass, totalling 88.5 Mg ha−1. In agricultural lands, the average tree density of TOFs is 7.7 stems per hectare. Large-diameter trees contribute to 78.3% of the total stem density, which amounts to 6.1 stems per hectare. These large-diameter trees account for 95.5% of the AGB, totalling 63.8 Mg ha−1. Small-diameter trees comprise only 6.3% of the stems and contribute just 0.6% to the total AGB in agricultural lands.

3.2. AGB Conservation Rate by Region

In the study area, the contribution of TOF-AL for the preservation of the initial forest AGB is 17.9% (66.8 Mg ha−1 out of 373.5 Mg ha−1). However, as shown in Figure 2, the conservation rate of the initial forest AGB varies from territory to territory. The highest AGB conservation rate was observed in the territory of Lisala, where TOF-AL preserved approximately 22.1% of the initial forest AGB. This is followed by the territory of Bongandanga, with 20.5% of the AGB conservation rate. The lowest AGB conservation rate was registered in the territory of Bumba, where TOF-AL only preserved 11.2% of the initial forest AGB.
The Kruskal–Wallis test confirms that the AGB conservation rate significantly differs in these tree territories (χ2 = 118.4, df = 2, p-value = 1.1 × 10−26). The Dunn test confirms that the AGB conservation rate in Lisala is significantly higher than that in Bongandanga (PHolm-adj. = 7.60 × 10−3) and significantly higher than that in Bumba (PHolm-adj. = 3.56 × 10−25). The Dunn test also confirms that the AGB conservation rate in Bongandanga is significantly higher than that in Bumba (PHolm-adj. = 1.23 × 10−14).

3.3. The Impact of Artisanal Logging on the Conservation Rate of AGB

Overall, artisanal logging in the village significantly reduces the AGB conservation rate of TOF-AL in the three territories (Figure 3). In villages where artisanal logging is not practised, the average AGB conservation rate is 22.5%, equating to 83.97 Mg ha−1 of biomass preserved. However, where artisanal logging is practised, the average conservation rate of the initial AGB drops to 8.8% (32.81 Mg ha−1). As confirmed by the Mann–Whitney test (WMann–Whitney = 31,146.00, p = 1.10 × 10−57), artisanal logging significantly negatively affects the conservation of AGB by TOF-AL in the province of Mongala.

3.4. Effects of Charcoal Production on AGB Conservation Rate

Charcoal production, particularly in areas close to cities, diminishes the capacity of TOF-AL to maintain the aboveground biomass (AGB) of primary forests in agricultural lands (Figure 4). In villages where trees are not felled for charcoal production, the initial AGB conservation rate following slash-and-burn agriculture is 23.4%, roughly equivalent to 87.4 Mg ha−1. However, in areas where local people are involved in charcoal production as a source of income, the AGB conservation rate is 8.9%, equating to 33.3 Mg ha−1. The statistical analysis conducted confirms the negative impact of charcoal production on the ability of TOF-AL to sustain the initial forest AGB (WMann–Whitney = 29,525.00, p = 1.40 × 10−67).

3.5. The AGB Conservation Rate and the Distance to Cities

The conservation rates of aboveground biomass (AGB) increase with the distance from major cities (Figure 5). This suggests that farmers situated further from Bumba, Bongandanga, and Lisala are more likely to leave additional trees standing in their fields, leading to a higher conservation rate of the AGB.
The correlation index indicates that the AGB conservation rate is correlated with the distance from major cities (Cor: 0.76, R2: 0.6, p-value: 4.2 × 10−10) in Mongala province.

4. Discussion

4.1. Tree Density and Aboveground Biomass

In Mongala province, the average density of trees in the forest is 372 trees ha−1, representing 373.5 Mg ha−1 of AGB. Bradford and Murphy [3] found similar results in Australian forests, where the AGB stock ranges from 307 to 909 Mg ha−1. In the eastern Amazon, Sist et al. [61] found that the average density of trees (DBH ≥ 20 cm) is about 219 trees ha−1, which represents 378 Mg ha−1 of AGB. The average AGB observed in Mongala province is close to that found by Slik et al. [60], who showed that the average AGB in the tropics is about 418.3 Mg ha−1. In Mongala forests, large-diameter trees (DBH ≥ 60 cm) represent about 6.1% of the tree density and 55.9% of the forest’s AGB. Slik et al. [60] and Sist et al. [61] found similar results for tropical forests. They showed that, in tropical forests, large-diameter trees account for 69.8 and 49% of AGB, respectively. This study also showed that, in Mongala province, farmers tend to keep large-diameter trees in their fields. This may be explained by the difficulty of cutting these trees since farmers use rudimentary tools such as axes or machetes to cut trees. In addition, most of the trees kept belong to species of large trees of African forests, such as Erythrophleum suaveolens, Petersianthus macrocarpus, Ricinodendron heudelotii, Pycnanthus angolense, Piptadeniastrum africanum, Entandrophragma sp., etc. Thus, given the role of large-diameter trees in tropical forest biomass, conserving TOF-AL offers a great opportunity to reduce greenhouse gas emissions in Mongala province despite slash-and-burn agriculture. To achieve this, future research must explore the long-term carbon storage potential of TOF-AL in other provinces of the DRC. It is also important to compare AGB conservation rates in different TOF-AL systems (e.g., varying tree densities, species compositions, and management practices). To understand the dynamics of the AGB conservation rate in the agricultural landscape of Mongala province, future research should monitor the growth and mortality rates of large-diameter TOF-AL.

4.2. Aboveground Biomass Conservation Rate by Territory

The AGB conservation rate in Mongala province is 17.9%. This is very relevant information in the context of reducing emissions from deforestation and forest degradation. However, this study also showed that the role of trees outside forests in conserving the initial AGB of forests varies from territory to territory. In the three territories of Mongala province, the aboveground biomass conservation rate is higher in Lisala (22.1%), followed by Bongandanga (20.5%), and Bumba has the lowest AGB conservation rate (11.2%). Their intrinsic characteristics can explain this variation in the initial AGB conservation rate between the three territories. Indeed, the low aboveground biomass conservation rate in Bumba is consistent with information on deforestation and forest degradation in this territory. In a report, OSFAC [47] showed that Bumba is the territory with the highest deforestation rate in Mongala province. Enabel [54] explains the causes of this high deforestation rate, showing that agricultural activities are more intense in Bumba than in the other two territories. Furthermore, Bumba has the highest population density, resulting in a significant demand for agricultural products and wood energy [47,54]. The other two territories, although primarily agricultural, are less densely populated. The Bongandanga territory is even more landlocked than the others, which reduces pressure on forests and trees. In Bongandanga and Lisala, economic activities are less intense than in Bumba. Artisanal logging and charcoal production are also less developed in Lisala and Bongandanga than in Bumba [54], explaining why the AGB conservation rates are higher in these territories. A detailed and spatially explicit analysis of land-use change should be conducted in each territory (Bumba, Bongandanga, Lisala), using remote sensing and ground-truthing for a better understanding of land-use change’s impact on the AGB conservation rates per territory. Market-oriented research should consider the dynamics of agricultural products and wood energy in each territory to understand the economic incentives for deforestation and forest degradation.

4.3. The Impact of Artisanal Logging on the AGB Conservation Rate

In Mongala province, the AGB conservation rate is three times higher in areas where artisanal logging is not practised than in areas where it is practised. Where trees are not cut for timber, the AGB conservation rate is about 22%. However, the AGB conservation rate drops to 8% where artisanal logging is practised. This result confirms, in general, the negative effects of artisanal logging on forest resources, as demonstrated by Kranz et al. [46] and Shapiro et al. [66]. Indeed, artisanal sawyers, who do not have the means to organise large-scale prospecting campaigns in the forest, take advantage of the accessibility of agricultural lands to identify interesting timber tree species. These trees preserved by farmers when opening their fields are then logged to supply the urban markets of Bumba, Lisala, and even Kinshasa [67]. Given that, in the DRC, most of the wood production of the industrial sector is intended for export [17], artisanal logging, primarily conducted on agricultural land, serves as the primary source of timber for Congolese cities like Bumba and Lisala. This unsupplied local demand for timber explains the low AGB conservation rates in areas where artisanal logging is practised. To ensure the success of AGB conservation on agricultural landscapes, it is important to assess the impacts of artisanal logging on local communities’ livelihoods and investigate the potential for alternative livelihood opportunities that can reduce reliance on artisanal logging.

4.4. The Impact of Charcoal Production on the AGB Conservation Rate

It was observed in this study that in Mongala province, the aboveground biomass conservation rate is low in villages where slash-and-burn agriculture is practised simultaneously with charcoal production. Indeed, several studies have shown that in tropical regions, firewood and charcoal supply about 90% of household energy needs [23,68,69]. While in other regions, people cut down forest trees to make charcoal [22,24], in Mongala province, charcoal is generally produced from TOF-AL. After sowing the crops, farmers cut down the remaining trees in their fields to make charcoal [70]. Thus, charcoal production decreases the AGB conservation rate of TOF-AL when practised in an area. This research underlines the necessity to conduct longitudinal studies to track the temporal dynamics of TOF-AL use, from initial clearing to charcoal production and subsequent agricultural activities. Other research should analyse the profitability of charcoal production and its contribution to household income. Investigating the potential for alternative energy sources (e.g., biomass briquettes) to reduce reliance on charcoal is also important. On the other hand, it is crucial to analyse the feasibility and acceptability of these alternatives to local communities.

4.5. AGB Conservation Rate and Distance to Cities

It was observed in this study that in Mongala province, there is a positive correlation between the distance from major cities and the AGB conservation rate of TOF-AL. This means that in villages located further from cities, farmers keep more trees in their fields, which leads to a high AGB conservation rate. These results are consistent with those found by Xiong et al. [71], who showed that the deforestation rate in Zhejiang province (China) was very high within a 3 km radius of major cities. However, it gradually decreased as people moved away from cities. They argue that communication routes, whether roads or rivers, have a positive effect on deforestation. Consequently, the rate of deforestation decreases as the distance from these communication routes increases.
Several mechanisms can explain this. First, several studies have shown that in tropical regions, the deforestation rate is higher around large cities [68,71]. Indeed, proximity to cities has two effects: for rural populations, it improves market access [72,73], and for urban populations, it increases forest access [73], consequently increasing deforestation. On the other hand, increasing population density around large cities shortens the fallow period, compromising forest recovery [68]. So, if pressures on forests are higher near cities, it is logical that the AGB conservation rate is lower near cities, as observed in this study.
In Mongala province, the quality of road infrastructure can explain the low AGB conservation rate near major cities, the high population density near cities, and the presence of artisanal sawmilling and charcoal production in villages near cities. Indeed, as discussed above, artisanal logging and charcoal production attacks trees preserved in farmers’ fields. Artisanal loggers trade these trees with farmers to make timber; some are cut down to produce charcoal. Given that these two activities are concentrated within a 40 km radius of the city, this partly explains the low AGB conservation rate near cities. In more remote areas, the roads are often poor-quality, restricting access to forests and trees. This is consistent with the results found by Li et al. [73] and Mena et al. [74], who showed that the quality of roads indirectly affects forest resources. Thus, the difficulty of evacuating products prevents artisanal logging and charcoal production in these areas, thus sparing trees outside forests on agricultural land. Regarding these results, future research must investigate the specific impacts of different types of infrastructure (roads, rivers, etc.) on AGB conservation and analyse the interaction between road quality, transportation costs, and resource exploitation. Other studies should analyse the specific commodity chains that drive resource exploitation near cities (e.g., timber, charcoal, agricultural products). Future research must also analyse how urban demand for resources (timber, charcoal, food) influences land-use practices and AGB conservation in surrounding rural areas. This research enhances the importance of conducting economic analyses to understand the incentives and disincentives for farmers to conserve trees on their land.

5. Conclusions

This study analysed the contribution of trees outside forests on agricultural lands (TOF-AL) to conserving the initial forest AGB following slash-and-burn agriculture in Mongala province of the Democratic Republic of Congo. Observations were carried out in 45 villages spread across three territories in Mongala province. Most trees preserved in farmers’ fields have a DBH ≥ 60 cm. On agricultural lands, the average AGB is 66.8 ± 52.9 Mg ha−1, representing 17.9% of the pre-existing forests’ AGB. The AGB conservation rate varies from one territory to another. The territory of Lisala has the highest AGB conservation rate (22.1%), followed by Bongandanga (20.5%) and Bumba (11.2%). Artisanal logging and charcoal production negatively impact the biomass conservation rate of TOF-AL. Without charcoal production, the AGB conservation rate is 23.4%. However, if charcoal production is practised, the AGB conservation rate is 8.9%. Similarly, the AGB conservation rate is 22.5% if artisanal logging is practised and 8.8% when artisanal logging is not practised. A positive correlation exists between distance from major cities and the AGB conservation rate, which is higher when farther away from cities. These results demonstrate the importance of encouraging and supporting the conservation of TOF-AL. This conservation may be seen as a viable option for implementing the REDD+ mechanism in this province. However, it is also evident that to guarantee its success, alternative activities to charcoal production and artisanal logging must be developed to provide local populations with other sources of income.

Author Contributions

J.P.A. conceptualised this study, designed the methodology, coordinated all research data collection and analysis interpretation, and drafted the manuscript. P.W.C. and J.N.K. provided crucial academic supervision throughout this study, offering substantial intellectual inputs and critical revisions to the manuscript. J.E. and J.P.P.M. contributed to the design of the data collection protocol. H.D. and A.-O.Z. assisted in data analysis and manuscript drafting. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the PASET Regional Scholarship and Innovation Fund.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We sincerely thank the Regional Scholarship and Innovation Fund (RSIF) of the Partnership for Skills in Applied Sciences, Engineering, and Technology (PASET) for providing the scholarship that made this research possible. We also wish to thank Ridjo Mbula, our local guides who assisted with tree inventories, and the administrative and traditional authorities of Mongala province for their support during our various missions in the area. Additionally, we are grateful to the local communities who welcomed us and facilitated our access to their fields.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
TOF-ALTrees outside forests on agricultural land.
DRCDemocratic Republic of Congo.
AGBAboveground biomass.

References

  1. Lal, R. Forest Soils and Carbon Sequestration. For. Ecol. Manag. 2005, 220, 242–258. [Google Scholar] [CrossRef]
  2. Bonan, G.B. Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests. Science 2008, 320, 1444–1449. [Google Scholar] [CrossRef]
  3. Bradford, M.; Murphy, H.T. The Importance of Large-Diameter Trees in the Wet Tropical Rainforests of Australia. PLoS ONE 2018, 14, e0208377. [Google Scholar] [CrossRef] [PubMed]
  4. Banoho, L.-P.K.; Zapfack, L.; Weladji, R.B.; Djomo, C.C.; Nyako, M.C.; Nasang, J.M.; Tagnang, N.M.; Mbobda, R.B.T. Biodiversity and Carbon Sequestration Potential in Two Types of Tropical Rainforest, Cameroon. Acta Oecologica 2020, 105, 103562. [Google Scholar] [CrossRef]
  5. Mauya, E.W.; Madundo, S. Aboveground Biomass and Carbon Stock of Usambara Tropical Rainforests in Tanzania. Tanzan. J. For. Nat. Conserv. 2021, 90, 63–82. [Google Scholar]
  6. Suyanto, S.; Nugroho, Y.; Harahap, M.M.; Kusumaningrum, L.; Wirabuana, P.Y.A.P. Spatial Distribution of Vegetation Diversity, Timber Production, and Carbon Storage in Secondary Tropical Rainforest at South Kalimantan, Indonesia. Biodivers. J. Biol. Divers. 2022, 23, 6147–6154. [Google Scholar] [CrossRef]
  7. Barlow, J.; Lennox, G.D.; Ferreira, J.; Berenguer, E.; Lees, A.C.; Nally, R.M.; Thomson, J.R.; Ferraz, S.F.D.B.; Louzada, J.; Oliveira, V.H.F.; et al. Anthropogenic Disturbance in Tropical Forests Can Double Biodiversity Loss from Deforestation. Nature 2016, 535, 144–147. [Google Scholar] [CrossRef]
  8. Mori, A.S.; Lertzman, K.P.; Gustafsson, L. Biodiversity and Ecosystem Services in Forest Ecosystems: A Research Agenda for Applied Forest Ecology. J. Appl. Ecol. 2017, 54, 12–27. [Google Scholar] [CrossRef]
  9. Brummitt, N.; Araújo, A.C.; Harris, T. Areas of Plant Diversity—What Do We Know? Plants People Planet 2021, 3, 33–44. [Google Scholar] [CrossRef]
  10. Soe, K.T.; Yeo-Chang, Y. Livelihood Dependency on Non-Timber Forest Products: Implications for REDD+. Forests 2019, 10, 427. [Google Scholar] [CrossRef]
  11. Walters, G.; Sayer, J.; Boedhihartono, A.K.; Endamana, D.; Angu, K.A. Integrating Landscape Ecology into Landscape Practice in Central African Rainforests. Landsc. Ecol. 2021, 36, 2427–2441. [Google Scholar] [CrossRef]
  12. Jabeen, S.; Arshad, F.; Harun, N.; Waheed, M.; Alamri, S.; Haq, S.M.; Vitasović-Kosić, I.; Fatima, K.; Chaudhry, A.S.; Bussmann, R.W. Folk Knowledge and Perceptions about the Use of Wild Fruits and Vegetables–Cross-Cultural Knowledge in the Pipli Pahar Reserved Forest of Okara, Pakistan. Plants 2024, 13, 832. [Google Scholar] [CrossRef] [PubMed]
  13. Mahabale, D.; Bodmer, R.; Pizuri, O.; Uraco, P.; Chota, K.; Antunez, M.; Groombridge, J. Sustainability of Hunting in Community-Based Wildlife Management in the Peruvian Amazon. Sustainability 2025, 17, 914. [Google Scholar] [CrossRef]
  14. Kipalu, P.; Koné, L.; Bouchra, S.; Vig, S.; Loyombo, W. Securing Forest Peoples’ Rights and Tackling Deforestation in the Democratic Republic of Congo: Deforestation Drivers, Local Impacts and Rights-Based Solutions. 2016. Available online: https://www.cabidigitallibrary.org/doi/full/10.5555/20173065227 (accessed on 26 February 2025).
  15. Austin, K.G.; Schwantes, A.; Gu, Y.; Kasibhatla, P.S. What Causes Deforestation in Indonesia? Environ. Res. Lett. 2019, 14, 024007. [Google Scholar] [CrossRef]
  16. Yameogo, C.E.W. Globalization, Urbanization, and Deforestation Linkage in Burkina Faso. Environ. Sci. Pollut. Res. 2021, 28, 22011–22021. [Google Scholar] [CrossRef]
  17. Chervier, C.; Ximenes, A.C.; Mihigo, B.-P.N.; Doumenge, C. Impact of Industrial Logging Concession on Deforestation and Forest Degradation in the DRC. World Dev. 2024, 173, 1–44. [Google Scholar] [CrossRef]
  18. Richards, P. What Drives Indirect Land Use Change? How Brazil’s Agriculture Sector Influences Frontier Deforestation. Ann. Assoc. Am. Geogr. 2015, 105, 1026–1040. [Google Scholar] [CrossRef]
  19. Schroeder, J.; Peplau, T.; Pennekamp, F.; Gregorich, E.; Tebbe, C.C.; Poeplau, C. Deforestation for Agriculture Increases Microbial Carbon Use Efficiency in Subarctic Soils. Biol. Fertil. Soils 2022, 60, 17–34. [Google Scholar] [CrossRef]
  20. Kiruki, H.M.; van der Zanden, E.H.; Malek, Ž.; Verburg, P.H. Land Cover Change and Woodland Degradation in a Charcoal Producing Semi-Arid Area in Kenya. Land Degrad. Dev. 2017, 28, 472–481. [Google Scholar] [CrossRef]
  21. Chiteculo, V.; Lojka, B.; Surový, P.; Verner, V.; Panagiotidis, D.; Woitsch, J. Value Chain of Charcoal Production and Implications for Forest Degradation: Case Study of Bié Province, Angola. Environments 2018, 5, 113. [Google Scholar] [CrossRef]
  22. Villazón Montalván, R.A.; de Medeiros Machado, M.; Pacheco, R.M.; Nogueira, T.M.P.; de Carvalho Pinto, C.R.S.; Fantini, A.C. Environmental concerns on traditional charcoal production: A global environmental impact value (GEIV) approach in the southern Brazilian context. Environ. Dev. Sustain. 2019, 21, 3093–3119. [Google Scholar] [CrossRef]
  23. Bamwesigye, D.; Kupec, P.; Chekuimo, G.; Pavlis, J.; Asamoah, O.; Darkwah, S.A.; Hlaváčková, P. Charcoal and Wood Biomass Utilization in Uganda: The Socioeconomic and Environmental Dynamics and Implications. Sustainability 2020, 12, 8337. [Google Scholar] [CrossRef]
  24. Adeniji, O.A.; Ibrahim, A.O.; Joshua, D.A.; Fingesi, U.I.; Osaguona, P.O.; Ajibade, A.J.; Akinbowale, A.S.; Olaifa, O.P. Assessment of Charcoal Production and Its Impact on Deforestation and Environment in Borgu Local Government Area of Niger State, Nigeria. J. Appl. Sci. Environ. Manag. 2022, 26, 711–717. [Google Scholar] [CrossRef]
  25. Turubanova, S.; Potapov, P.V.; Tyukavina, A.; Hansen, M.C. Ongoing Primary Forest Loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 2018, 13, 074028. [Google Scholar] [CrossRef]
  26. Shapiro, A.; D’Annunzio, R.; Jungers, Q.; Desclée, B.; Kondjo, H.; Iyanga, J.M.; Gangyo, F.; Rambaud, P.; Sonwa, D.; Mertens, B.; et al. Are Deforestation and Degradation in the Congo Basin on the Rise? An Analysis of Recent Trends and Associated Direct Drivers. Preprints 2022, 37. Available online: https://www.researchsquare.com/article/rs-2018689/v1 (accessed on 26 February 2025).
  27. Brooks, T.M.; Mittermeier, R.A.; Mittermeier, C.G.; Da Fonseca, G.A.B.; Rylands, A.B.; Konstant, W.R.; Flick, P.; Pilgrim, J.; Oldfield, S.; Magin, G.; et al. Habitat Loss and Extinction in the Hotspots of Biodiversity. Conserv. Biol. 2002, 16, 909–923. [Google Scholar] [CrossRef]
  28. Hughes, A.C. Understanding the Drivers of Southeast Asian Biodiversity Loss. Ecosphere 2017, 8, e01624. [Google Scholar] [CrossRef]
  29. Lindenmayer, D.B. Forest Biodiversity Declines and Extinctions Linked with Forest Degradation: A Case Study from Australian Tall, Wet Forests. Land 2023, 12, 528. [Google Scholar] [CrossRef]
  30. Wilson, S.A.; Scholes, R.J. The Climate Impact of Land Use Change in the Miombo Region of South Central Africa. J. Integr. Environ. Sci. 2020, 17, 187–203. [Google Scholar] [CrossRef]
  31. Hu, X.; Næss, J.S.; Iordan, C.M.; Huang, B.; Zhao, W.; Cherubini, F. Recent Global Land Cover Dynamics and Implications for Soil Erosion and Carbon Losses from Deforestation. Anthropocene 2021, 34, 100291. [Google Scholar] [CrossRef]
  32. Berhanu, Y.; Dalle, G.; Sintayehu, D.W.; Kelboro, G.; Nigussie, A. Land Use/Land Cover Dynamics Driven Changes in Woody Species Diversity and Ecosystem Services Value in Tropical Rainforest Frontier: A 20-Year History. Heliyon 2023, 9, e13711. [Google Scholar] [CrossRef] [PubMed]
  33. Iwuji, M.C.; Okpara, J.C.; E Ukaegbu, K.O.; Iwuji, K.M.; Uyo, C.N.; Onuegbu, S.V.; Acholonu, C. Impact of Deforestation on Rural Livelihood in Mbieri, Imo State Nigeria. Int. J. Geogr. Reg. Plan. Res. 2022, 7, 1–13. [Google Scholar] [CrossRef]
  34. Okorie, N.; Aba, S.; Amu, C.; Baiyeri, K. The Role of Trees and Plantation Agriculture in Mitigating Global Climate Change. Afr. J. Food Agric. Nutr. Dev. 2017, 17, 12691–12707. [Google Scholar] [CrossRef]
  35. Fagan, M.E.; Kim, D.-H.; Settle, W.; Ferry, L.; Drew, J.; Carlson, H.; Slaughter, J.; Schaferbien, J.; Tyukavina, A.; Harris, N.L.; et al. The Expansion of Tree Plantations across Tropical Biomes. Nat. Sustain. 2022, 5, 681–688. [Google Scholar] [CrossRef]
  36. Pati, P.K.; Kaushik, P.; Khan, M.L.; Khare, P.K. Biodiversity and Ecosystem Services of Trees Outside Forests: A Case Study from Dr. Harisingh Gour Vishwavidyalaya, Sagar, Central India. Indian J. Ecol. 2022, 49, 608–615. [Google Scholar] [CrossRef]
  37. Jeevan, K.; Shilpa, G.; Manjusha, K.; Muthukumar, A.; Muthuchamy, M. Comparison of Carbon Stock Potential of Different ‘Trees Outside Forest’ Systems of Palakkad District, Kerala: A Step Towards Climate Change Mitigation. Land Degrad. Dev. 2025, 36, 885–899. [Google Scholar] [CrossRef]
  38. Quand, A.K. Building Livelihood Resilience in Semi-Arid Kenya: What Role Does Agroforestry Play? University of Colorado: Fort Collins, CO, USA, 2017; 347p. [Google Scholar]
  39. Tiwari, P. Agroforestry for Sustainable Rural Livelihood. A Review. Int. J. Pure Appl. Biosci. 2017, 5, 299–309. [Google Scholar] [CrossRef]
  40. Fida, T.G. The Role of Homegarden Agroforestry in Househld Livelihoods. Int. J. For. Plant. 2019, 2, 60–73. [Google Scholar]
  41. Bansal, A.K. Enhancing the Contribution of Agroforestry and Other Tree Outside Forest Resources of India in National Development; Network for Certification and Conservation of Forests (NCCF), Policy Paper 1/2024; NCCF: New Delhi, India, 2024. [Google Scholar]
  42. Guo, Z.D.; Hu, H.F.; Pan, Y.D.; Birdsey, R.A.; Fang, J.Y. Increasing Biomass Carbon Stocks in Trees Outside Forests in China over the Last Three Decades. Biogeosciences 2014, 11, 4115–4122. [Google Scholar] [CrossRef]
  43. Bayala, J.; Sanou, J.; Bazié, H.R.; Coe, R.; Kalinganire, A.; Sinclair, F.L. Regenerated Trees in Farmers’ Fields Increase Soil Carbon across the Sahel. Agrofor. Syst. 2020, 94, 401–415. [Google Scholar] [CrossRef]
  44. Zellweger, F.; Flack-Prain, S.; Footring, J.; Wilebore, B.; Willis, K.J. Carbon Storage and Sequestration Rates of Trees inside and Outside Forests in Great Britain. Environ. Res. Lett. 2022, 17, 074004. [Google Scholar] [CrossRef]
  45. Bremer, L.L.; Farley, K.A. Does Plantation Forestry Restore Biodiversity or Create Green Deserts? A Synthesis of the Effects of Land-Use Transitions on Plant Species Richness. Biodivers. Conserv. 2010, 19, 3893–3915. [Google Scholar] [CrossRef]
  46. Kranz, O.; Schoepfer, E.; Tegtmeyer, R.; Lang, S. Earth Observation Based Multi-Scale Assessment of Logging Activities in the Democratic Republic of the Congo. ISPRS J. Photogramm. Remote Sens. 2018, 144, 254–267. [Google Scholar] [CrossRef]
  47. OSFAC. Observatoire Satellitales des Forêts d’Afrique Centrale: Rapport d’Activites 2019–2020; OSFAC: Yaoundé, Cameroon, 2020. [Google Scholar]
  48. Enabel. Enabel En RD Congo Programme Transitoire de Coopération Gouvernementale Belgique–RD Congo (2020–2022). 2022; p. 4. [Google Scholar]
  49. Azenge, C.; Meniko, J.P.P. Espèces et Usages d’arbres Hors Forêt Sur Les Terres Agricoles Dans La Région de Kisangani En République Démocratique Du Congo. Rev. Marocaine Sci. Agron. Vétérinaires 2020, 8, 163–169. [Google Scholar]
  50. Peros, C.S.; Dasgupta, R.; Estoque, R.C.; Basu, M. Ecosystem Services of ‘Trees Outside Forests (TOF)’ and Their Contribution to the Contemporary Sustainability Agenda: A Systematic Review. Environ. Res. Commun. 2022, 4, 112002. [Google Scholar] [CrossRef]
  51. Shrestha, H.L.; Rai, A.; Dhakal, P. Assessment of above Ground Biomass of Trees Outside Forest (TOF) in the Context of Climate Change. J. Ecol. Nat. Resour. 2020, 4, 000186. [Google Scholar] [CrossRef]
  52. Ewango, C.; Maindo, A.; Shaumba, J.-P.; Kyanga, M.; Macqueen, D. Options pour l’Incubation Durable d’Entreprises Forestières Communautaires en République Démocratique du Congo (RDC) Informations sur les Auteurs ce Rapport a Été Écrit par; IIED: London, UK, 2019. [Google Scholar]
  53. Balandi, J.; Mikwa Ngamba, J.-F.; Kumba Lubemba, S.; Meniko Tohulu, J.-P.; Maindo, A.; Ngonga, M.; Ndola, N. Anthropisation et Dynamique Spatio-Temporelle de l’occupation Du Sol Dans La Région de Lisala Entre 1987 et 2015. Rev. Mar. Sci. Agron. Vét. 2020, 8, 445–451. [Google Scholar]
  54. Enabel. PIREDD Mongala République Démocratique du Congo; Belgian Development Agency: Kinshasa, Democratic Republic of the Congo, 2020. [Google Scholar]
  55. Bosakabo, G.B.B. Intégration Sociale et Développement Des Peuples Autochtones Pygmées dans le Territoire de Bongandanga, RDC: Nécessité du Rôle des Anthropologues du Développement. アカデミア. 社会科学編 2024, 26, 61–92. [Google Scholar]
  56. de Foresta, H.; Somarriba, E.; Temu, A.; Boulanger, D.; Feuilly, H.; Gauthier, M. Towards the Assessment of Trees Outside Forests. Resources Assessment Working Paper 183; Food and Agriculture Organization of the United Nations: Rome, Italy, 2013. [Google Scholar]
  57. de Foresta, H. Where Are the Trees Outside Forest in Brazil? Pesqui. Florest. Bras. 2017, 37, 393–401. [Google Scholar] [CrossRef]
  58. Fayolle, A.; Doucet, J.L.; Gillet, J.F.; Bourland, N.; Lejeune, P. Tree Allometry in Central Africa: Testing the Validity of Pantropical Multi-Species Allometric Equations for Estimating Biomass and Carbon Stocks. For. Ecol. Manag. 2013, 305, 29–37. [Google Scholar] [CrossRef]
  59. Zanne, A.E.; Lopez-Gonzalez, G.; Coomes, D.A.; Ilic-Highett, J.; Jansen, S.; Lewis, S.L.; Miller, R.B.; Swenson, N.G.; Wiemann, M.C.; Chave, J. Global Wood Density Database. Dryad. 2009. Available online: http://hdl.handle.net/10255/dryad.235 (accessed on 26 February 2025).
  60. Slik, J.W.F.; Paoli, G.; Mcguire, K.; Amaral, I.; Barroso, J.; Bastian, M.; Blanc, L.; Bongers, F.; Boundja, P.; Clark, C.; et al. Large Trees Drive Forest Aboveground Biomass Variation in Moist Lowland Forests across the Tropics. Glob. Ecol. Biogeogr. 2013, 22, 1261–1271. [Google Scholar] [CrossRef]
  61. Sist, P.; Mazzei, L.; Blanc, L.; Rutishauser, E. Large Trees as Key Elements of Carbon Storage and Dynamics after Selective Logging in the Eastern Amazon. For. Ecol. Manag. 2014, 318, 103–109. [Google Scholar] [CrossRef]
  62. Lutz, J.A.; Furniss, T.J.; Johnson, D.J.; Davies, S.J.; Allen, D.; Alonso, A.; Anderson-Teixeira, K.J.; Andrade, A.; Baltzer, J.; Becker, K.M.L.; et al. Global Importance of Large-Diameter Trees. Glob. Ecol. Biogeogr. 2018, 27, 849–864. [Google Scholar] [CrossRef]
  63. Patil, I. Visualizations with Statistical Details: The “ggstatsplot” Approach. J. Open Source Softw. 2021, 6, 3167. [Google Scholar] [CrossRef]
  64. Zeileis, A.; Hothorn, T. Diagnostic Checking in Regression Relationships. R News 2002, 2, 7–10. [Google Scholar]
  65. Gómez-Rubio, V. Ggplot2—Elegant Graphics for Data Analysis (2nd Edition). J. Stat. Softw. 2016, 77, 3–5. [Google Scholar] [CrossRef]
  66. Shapiro, A.C.; Bernhard, K.P.; Zenobi, S.; Müller, D.; Aguilar-Amuchastegui, N.; d’Annunzio, R. Proximate Causes of Forest Degradation in the Democratic Republic of the Congo Vary in Space and Time. Front. Conserv. Sci. 2021, 2, 1–19. [Google Scholar] [CrossRef]
  67. Lescuyer, G.; Cerutti, P.O.; Tshimpanga Ongona, C.P.; Biloko, F.; Adebu-abdala, B.; Tsanga, R.; Yembe, R.I.Y.; Essiane-Mendoula, E. Le Marché Domestique du Sciage Artisanal en République Démocratique du Congo: Etats des Lieux, Opportunités, Défis. Document Occasionnel 10; CIFOR: Bogor, Indonesia, 2014. [Google Scholar]
  68. Gillet, P.; Vermeulen, C.; Feintrenie, L.; Dessard, H.; Garcia, C. Quelles Sont les Causes de la Déforestation Dans le Bassin du Congo? Synthèse Bibliographique et Études de Cas. Base 2016, 20, 183–194. [Google Scholar] [CrossRef]
  69. Win, Z.C.; Mizoue, N.; Ota, T.; Kajisa, T.; Yoshida, S.; Oo, T.N.; Ma, H. Differences in Consumption Rates and Patterns between Firewood and Charcoal: A Case Study in a Rural Area of Yedashe Township, Myanmar. Biomass Bioenergy 2018, 109, 39–46. [Google Scholar] [CrossRef]
  70. Kasekete, D.K.; Bourland, N.; Gerkens, M.; Louppe, D.; Schure, J.; Mate, J.-P. Bois-Énergie et Plantations à Vocation Énergétique En République Démocratique Du Congo: Cas de La Province Du Nord-Kivu—Synthèse Bibliographique. Bois For. Trop. 2023, 357, 5–28. [Google Scholar] [CrossRef]
  71. Xiong, B.; Chen, R.; Xia, Z.; Ye, C.; Anker, Y. Large-Scale Deforestation of Mountainous Areas during the 21st Century in Zhejiang Province. Land Degrad. Dev. 2020, 31, 1761–1774. [Google Scholar] [CrossRef]
  72. Damania, R.; Wheeler, D. Road Improvements and Deforestation in the Congo Basin Countries. World Bank Policy Research Working Paper. 2015. Available online: https://xueshu.baidu.com/usercenter/paper/show?paperid=ff4a350e2976a6755575af159f5f6a66&site=xueshu_se (accessed on 26 February 2025).
  73. Li, M.; De Pinto, A.; Ulimwengu, J.M.; You, L.; Robertson, R.D. Impacts of Road Expansion on Deforestation and Biological Carbon Loss in the Democratic Republic of Congo. Environ. Resour. Econ. 2015, 60, 433–469. [Google Scholar] [CrossRef]
  74. Mena, C.F.; Lasso, F.; Martinez, P.; Sampedro, C. Modeling Road Building, Deforestation and Carbon Emissions Due Deforestation in the Ecuadorian Amazon: The Potential Impact of Oil Frontier Growth. J. Land Use Sci. 2017, 12, 477–492. [Google Scholar] [CrossRef]
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Figure 1. Map of study area in Mongala province, DR Congo.
Figure 1. Map of study area in Mongala province, DR Congo.
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Figure 2. Variation in the conservation rates of aboveground biomass (AGB) across different territories of Mongala province.
Figure 2. Variation in the conservation rates of aboveground biomass (AGB) across different territories of Mongala province.
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Figure 3. Influence of artisanal logging on aboveground biomass (AGB) conservation rate.
Figure 3. Influence of artisanal logging on aboveground biomass (AGB) conservation rate.
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Figure 4. Influence of charcoal production activity on AGB conservation rate in Mongala province.
Figure 4. Influence of charcoal production activity on AGB conservation rate in Mongala province.
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Figure 5. Relationship between distance to major cities and initial aboveground biomass (AGB) conservation rates in Mongala.
Figure 5. Relationship between distance to major cities and initial aboveground biomass (AGB) conservation rates in Mongala.
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Table 1. Trees’ density and aboveground biomass stocks (Mg ha−1) for trees outside forests (TOFs) and forest trees in the province of Mongala.
Table 1. Trees’ density and aboveground biomass stocks (Mg ha−1) for trees outside forests (TOFs) and forest trees in the province of Mongala.
TOFForest
DBH Class (cm)Stem Density% of DensityAGB
(Mg ha−1)		% of AGBStem Density% of DensityAGB
(Mg ha−1)		% of AGB
DBH 10–400.5 ± 16.30.4 ± 0.80.6316.0 ± 45.484.988.5 ± 10.923.7
DBH 40–601.2 ± 115.42.6 ± 3.63.933.3 ± 6.49.076.3 ± 11.220.4
DBH ≥ 606.1 ± 478.363.8 ± 51.095.522.8 ± 4.36.1208.7 ± 38.655.9
All7.7 ± 5100.066.8 ± 52.9100.0372.2 ± 46.3100373.5 ± 41.9100
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Azenge, J.P.; Zon, A.-O.; Diesse, H.; Meniko, J.P.P.; Ebuy, J.; Kassi, J.N.; Chirwa, P.W. Conserving Carbon Stocks Under Climate Change: Importance of Trees Outside Forests in Agricultural Landscapes of Mongala Province, Democratic Republic of Congo. Earth 2025, 6, 19. https://doi.org/10.3390/earth6020019

AMA Style

Azenge JP, Zon A-O, Diesse H, Meniko JPP, Ebuy J, Kassi JN, Chirwa PW. Conserving Carbon Stocks Under Climate Change: Importance of Trees Outside Forests in Agricultural Landscapes of Mongala Province, Democratic Republic of Congo. Earth. 2025; 6(2):19. https://doi.org/10.3390/earth6020019

Chicago/Turabian Style

Azenge, Jean Pierre, Aboubacar-Oumar Zon, Hermane Diesse, Jean Pierre Pitchou Meniko, Jérôme Ebuy, Justin N’Dja Kassi, and Paxie W. Chirwa. 2025. "Conserving Carbon Stocks Under Climate Change: Importance of Trees Outside Forests in Agricultural Landscapes of Mongala Province, Democratic Republic of Congo" Earth 6, no. 2: 19. https://doi.org/10.3390/earth6020019

APA Style

Azenge, J. P., Zon, A.-O., Diesse, H., Meniko, J. P. P., Ebuy, J., Kassi, J. N., & Chirwa, P. W. (2025). Conserving Carbon Stocks Under Climate Change: Importance of Trees Outside Forests in Agricultural Landscapes of Mongala Province, Democratic Republic of Congo. Earth, 6(2), 19. https://doi.org/10.3390/earth6020019

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