Miombo is one of the most important forest ecosystems in southern Africa, covering large areas of Angola, Mozambique, Tanzania, Zimbabwe, Zambia, and the Democratic Republic of Congo, covering between 2.5 to 3.6 million km² [1]. This ecosystem includes mosaics of dry forests and wooded savannahs, with a high diversity of flora and fauna [2], with medium productivity and high social value in terms of woody fuel, building materials, pasture, food, and medicinal plants [3]. Miombo usually has a multistrata structure: an upper stratum with adult trees between 15 and 18 m tall and a density around 65 trees ha⁻¹; an intermediate stratum with trees between 8 and 12 m tall and a density of 80 trees ha⁻¹; and the lower stratum with saplings, shrubs, and grassland with a height lower than 8 m and a density range between 375 and 500 trees ha⁻¹ [4,5]. The volume varies between 14 and 59 m³ ha⁻¹ in dry Miombo and between 41 and 100 m³ ha⁻¹ in wet Miombo [4], with higher productivity in wet Miombo forests [6].

More than 55% of Angola’s land area is forested, amounting to about 69 Mha, of which 58.6 Mha is Miombo lands, 3.6 Mha correspond to other native forest and plantation forest, and 2.1 Mha of other lands with scattered trees [7,8]. Angolan Miombo is characterized by species of the genus Brachystegia, Julbernardia, and Isoberlinia, [9]. This ecosystem provides many ecosystem services to the rural population, including timber and nontimber products biomass for household energy production (firewood and charcoal), fodder for livestock, medicinal plants, and wild fruits. Water cycle regulation, the reduction of erosion, and the diversity of habitats favoring a high biodiversity are also important ecosystem services of Miombo lands [9]. Estimated data confirm that Miombo forest in Angola has an annual harvest capacity of 326,000 m³ and an average annual commercial timber productivity of 0.3 m³ ha⁻¹ year⁻¹ [7]. In addition, Miombo forests play an important role in atmospheric CO₂ sequestration with fixation rates of up to 39.6 MgC ha⁻¹ in above-ground biomass [10]. Thus, under sustainable management criteria, the Miombo could be an important source of economic income and employment, especially in rural areas. However, at present, this ecosystem is under great pressure from the local population, which puts its sustainability at risk, mainly due to some specific deforestation drivers.

In general, there is a differentiation between proximate causes (or drivers) and driving forces (or indirect drivers). Proximate causes are human activities that have a direct influence on the deforestation while driving forces are complex interactions of key social, economic, political, cultural, and technological processes [11]. Several works have assessed deforestation drivers as a tool for decision-making to reduce emissions from deforestation [12]. The main drivers identified in tropical forests are the agriculture expansion [13], urban expansion, infrastructural development, mining activity, and commercial timber exploitation or as fuel (firewood and charcoal), grazing, and forest fires [14].

Studies carried out in sub-Saharan Africa have concluded that the subsistence agriculture is the main driver in the poorest rural populations, often associated with deforestation for charcoal production and/or forest fires [15,16]. Thus, some authors link deforestation to food security, with the most frequent causes being agriculture [17] and exploitation of biomass as fuel [18]. In Angola, the main proximate drivers are the overextraction of timber resources, clearing for agricultural purposes, unregulated burning, and overgrazing [19]. This has led to an increase in deforestation rate from 0.2% in 2000 to 0.8% in the period 2000–2019 [7], compromising the current and future ecosystem services of the Miombo.

Charcoal production is a very important activity in Miombo and is increasingly becoming a lucrative business. Charcoal production in rural areas causes large environmental impact, especially in comparison with that generated by fuelwood, mainly in periurban areas [7]. This activity, which is continuously growing in Africa, is one of the main causes of deforestation and forest degradation both in Africa and Angola [18,20]. One of the main problems related to charcoal production in the Angolan Miombo is the lack of data about the potential productive capacity of this ecosystem. This leads to a lack of tools for assessing admissible annual cut values and, thus, for sustainable management. Some of the tools often used for assessing productive capacity, such as dendrochronology, have not often been used in such studies in tropical forests due to the great diversity of species and the difficulty in assessing annual growth [21]. Previous works have assessed annual growth in Miombo species to determine the annual allowable cutting volume [22]. Therefore, study of the productivity of the Miombo ecosystem is an important tool to increase knowledge about the charcoal production capacity and the annual allowable cutting volume, including the variables in forest management plans to increase the sustainability of this activity.

The aim of this study was to assess the productivity of Miombo ecosystem in Huambo (Angola) to estimate C sequestration and charcoal production capacity. The specific objectives were (i) to assess deforestation in miombo forest in Huambo province (Angola) during the last 20 years, (ii) to evaluate carbon storage capacity of miombo, and (iii) to calculate the charcoal productive capacity of those forests and compare them with the carbon storage capacity. The results obtained will provide information about the charcoal production for the management of miombo woodland resources in Huambo province, ensuring the sustainability of the activity and ecosystem services and improving forest management in a context of vulnerability of the local population. This information could also open up new funding opportunities, facilitating the engagement of rural communities with initiatives for reducing emissions from deforestation, forest degradation, and forest management (REDD+) or Clean Development Mechanisms (CDM) [1,23].

2. Material and Methods 

Figure 1. Distribution of miombo plots in the Huambo province (Angola). Forest area corresponds to the year 2000.

3. Deforestation

The forest area in Huambo province in 2000 was 2,770,205 ha, of which 359,130 ha (12.96%) were deforested during the period 2000–2019 (Figure 2). The highest levels of deforestation occurred in the municipalities of Caála (20.67%), Bailundo (15.17%), and Longonjo (13.16%); the lowest levels occurred in the municipalities of Huambo and Ecunha, with 8.02% and 8.68% of deforested area, respectively (Table 1).

Figure 2. Deforestation in Huambo province. Forest area in 2000 (treecover2000); deforested area and non-deforested area from 2000 to 2019.

Table 1. Deforestation in Huambo province. Forest area in 2000 (ha); deforested area from 2000 to 2019 expressed in ha and as a percentage.

Municipality	Forest Area 2000 (ha)	Deforestation (ha)	Deforestation (%)
Bailundo	662,026	100,449	15.17
Caála	270,146	55,826	20.67
Cathiungo	225,175	26,568	11.80
Ecunha	113,311	9837	8.68
Huambo	205,780	16,511	8.02
Londuimbali	241,025	30,931	12.83
Longonjo	177,232	23,325	13.16
Mungo	280,719	25,533	9.10
Tchikala	358,482	44,211	12.33
Tchinjenje	82,765	10,788	13.03
Ukuma	153,543	15,151	9.87
Total	2,770,205	359,130	12.96

4. Composition and Structure

Forty tree species were found in the study area, of which 36 were identified by their scientific name and 33 by their local name. Two species were not identified by either their local name or scientific name, although they were perfectly discriminated from the rest, so they were identified as independent species. The species with the highest frequencies were Albizia anthunesiana (12.94%), Brachystegia spiciformis (12.28%), Julbernardia paniculata (8.44%), Monote spp. (8.42%), Brachystegia boemii (7.85%), Isoberlinea angolensis (5.88%), Anisophyllea boehmii (5.86%), Syzygium guineense (4.84%), and Erythrophleum africanum (4.50%). The average density of adult trees was 3616.38 trees ha⁻¹. Caála and Tchicala have the highest densities (4747.35 trees ha⁻¹ and 4313.09 trees ha⁻¹, respectively) and Ukuma and Bailundo have the lowest densities (3151.26 trees ha⁻¹ and 3226.16 trees ha⁻¹, respectively).

5. Accumulated Carbon and CO₂ Equivalent

Total biomass was 195.05 Mg ha⁻¹ (55.02 Mg ha⁻¹ below-ground biomass and 140.04 Mg ha⁻¹ above-ground biomass (Table 2)). The highest biomass values were found in Ukuma and Bailundo (289.85 Mg ha⁻¹ and 218.18 Mg ha⁻¹, respectively), and the lowest in Tchicala and Longonjo (113.03 Mg ha⁻¹ and 157.69 Mg ha⁻¹, respectively) (Table 3).

Table 2. Biomass, accumulated Carbon, and CO₂ equivalent in the Miombo of Huambo province expressed in Mg ha⁻¹.

Variable	Above-Ground	Below-Ground	Total
Biomass 1	140.04 ± 108.59 (24.68–716.41)	55.02 ± 31.69 (14.68–209.55)	195.05 ± 140.14 (39.36–925.96)
Accumulated Carbon 2	65.82 (11.60–336.71)	25.86 ± 14.89 (6.90–98.48)	91.67 (18.50–435.20)
CO2 equivalent 3	241.32 (42.53–1234.61)	94.81 (25.30–361.12)	336.13 (67.84–1595.74)

¹ Total biomass, above-ground biomass, and below-ground biomass expressed in Mg ha⁻¹. ² Total accumulated Carbon, above-ground Carbon, and Below-ground Carbon expressed in Mg ha⁻¹. ³ Total CO₂ eq., above-ground CO₂ eq., and below-ground CO₂ eq. expressed in Mg ha⁻¹. For each variable, the mean value and standard deviation of all plots of Huambo province are included; further, maximum and minimum values found in plots are included.

Table 3. Biomass, volume, accumulated Carbon, and CO₂ equivalent by municipality. Mean values expressed in Mg ha⁻¹ and standard deviation are presented in the table.

Municipality	Total Biomass 1	Volume 2	Accumulated C 3	CO2eq 4
Bailundo	218.18 ± 214.35	79.09 ± 36.01	102.55 ± 100.75	376.00 ± 369.40
Caála	177.10 ± 75.42	92.39 ± 46.73	83.24 ± 35.45	305.21 ± 129.98
Ekunha	212.01 ± 49.72	72.86 ± 27.16	99.65 ± 23.37	365.37 ± 85.68
Katchiungo	178.48 ± 46.66	83.87 ± 5.49	83.88 ± 21.93	307.58 ± 80.42
Londuimbale	181.23 ± 42.11	70.88 ± 12.14	85.18 ± 19.79	312.32 ± 72.57
Longonjo	157.69 ± 55.75	77.24 ± 17.73	74.11 ± 26.20	271.75 ± 96.08
Tchicala	113.03 ± 58.74	56.44 ± 23.15	53.12 ± 27.61	194.79 ± 101.23
Tchingenje	184.29 ± 112.72	70.62 ± 26.28	86.62 ± 52.98	317.59 ± 194.26
Ukuma	289.85 ± 6.74	96.71 ± 10.48	136.23 ± 3.17	499.51 ± 11.62

¹ Total biomass, expressed in Mg ha⁻¹. ² Wood volume, expressed in m³ ha⁻¹. ³ Total accumulated Carbon, expressed in Mg ha⁻¹. ⁴ Total CO₂ eq., expressed in Mg ha⁻¹. For each variable, the mean value and standard deviation by municipality are included.

The mean wood volume in the Miombo forest was 78.57 m³ ha⁻¹, with the highest values in Ukuma and Caála (96.71 m³ ha⁻¹ and 92.39 m³ ha⁻¹, respectively) and the lowest in Tchingenje and Loundimbale (70.62 m³ ha⁻¹ and 70.88 m³ ha⁻¹, respectively) (Table 3).

The above-ground accumulated C was 65.82 Mg ha⁻¹ and the below-ground accumulated C was 25.86 Mg ha⁻¹; total accumulated C was 91.67 Mg ha⁻¹ (Table 2). The highest values were found in Ukuma and Bailundo (136.23 Mg ha⁻¹ and 102.55 Mg ha⁻¹, respectively) and the lowest values in Tchicala and Longonjo (53.12 Mg ha⁻¹ and 74.11 Mg ha⁻¹, respectively) (Table 3). These values correspond to a CO₂ equivalent of 336.13 Mg ha⁻¹ (94.81 Mg ha⁻¹ of above-ground CO₂eq and 241.32 Mg ha⁻¹ of below-ground CO₂eq) (Table 2). The highest values were found in Ukuma and Bailundo (499.51 Mg ha⁻¹ and 376.00 Mg ha⁻¹, respectively) and the lowest values in Tchicala and Longonjo (194.79 Mg ha⁻¹ and 271.75 Mg ha⁻¹, respectively) (Table 3).

6. Productive capacity

The Potential Charcoal Productivity (PCP) was 15,359.9 kg ha⁻¹, although the variability between municipalities was very high, ranging from 11,034.87 Kg ha⁻¹ for (Tchicala) to 18,907.60 Kg ha⁻¹ for (Ukuma).

The Allowable Cutting Value (ACV) was 24.78 trees ha⁻¹, with the lowest values in Londuimbale (22 trees ha⁻¹) and the highest values in Caála (31.57 trees ha⁻¹).

Table 4. Potential Charcoal Productivity (PCP) and Allowable Cutting Value (ACV) by municipality. Mean values and standard deviation of PCP expressed in Kg ha⁻¹ are included. Mean values and standard deviations of ACV expressed in number of trees ha⁻¹ are included.

Municipality	1 PCP (Kg ha−1)	2 ACV (N. Trees ha−1)
Bailundo	15,461.62 ± 7040.92	23.06 ± 5.41
Caála	18,061.42 ± 9136.63	31.57 ± 21.69
Ekunha	14,245.06 ± 5310.49	25.50 ± 3.54
Katchiungo	16,395.84 ± 1073.17	24.75 ± 4.79
Londuimbale	13,857.53 ± 2373.48	22.00 ± 1.00
Longonjo	15,100.04 ± 3465.85	25.00 ± 4.24
Tchicala	11,034.87 ± 4525.17	27.50 ± 11.12
Tchingenje	13.806.39 ± 5136.87	22.43 ± 5.91
Ukuma	18,907.60 ± 2048.33	22.67 ± 4.51

¹ Mean values and standard deviations of PCP expressed in Kg ha⁻¹. ² Mean values and standard deviation of ACV expressed in number of trees ha⁻¹. 

When comparing the biomass carbon storage with the potential charcoal productivity, there is a correlation at the extreme values (Tchicala and Ukuma) despite the correlation coefficient not being very high (0.5).

For a rotation period of 55 years, the Annual Allowable Cutting (ACV) was 14.31 ± 5,61 trees ha⁻¹ year⁻¹, increasing for shorter periods (40 years, 19.68 ± 7.72 trees ha⁻¹ year⁻¹; 25 years, 31.5 ± 12.35 trees ha⁻¹ year⁻¹; 15 years, 52.5 ± 20.58 trees ha⁻¹ year⁻¹).

Values of AAC corresponding to values of Annual Allowable Cutting Volume (AACV) of 10.77 ± 6.05 m³ ha⁻¹ year⁻¹, 14.13 ± 7.96 m³ ha⁻¹ year⁻¹, 21.17 ± 11.93 m³ ha⁻¹ year⁻¹, and 32.85 ± 18.51 m³ ha⁻¹ year⁻¹ (55, 40, 25, and 15 years, respectively).

The Potential Annual Allowable Charcoal Productivity (PAACP) were 2107.08 ± 1183.53 kg ha⁻¹ year⁻¹, 2762.96 ± 1557.41 kg ha⁻¹ year⁻¹, 4139.21 ± 2333.17 kg ha⁻¹ year⁻¹, and 6422.56 ± 3620.24 kg ha⁻¹ year⁻¹ for the rotation lengths considered (55, 40, 25, and 15, respectively).