Ecology and Evolution
Volume 14, Issue 6 e11476
RESEARCH ARTICLE
Open Access
Open Data

The effects of environmental variability and forest management on natural forest carbon stock in northwestern Ethiopia

Melkamu Kassaye, 

Corresponding Author

Melkamu Kassaye

Department of Forestry and Climate Science, Injibara University, Injibara, Ethiopia

Correspondence

Melkamu Kassaye, Department of Forestry and Climate Science, Injibara University, Injibara, Ethiopia.

Email: [email protected]

Contribution: Conceptualization (equal), Formal analysis (equal), ​Investigation (equal), Methodology (equal), Software (equal), Writing - original draft (equal)

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Yonas Derebe, 

Yonas Derebe

Department of Forestry and Climate Science, Injibara University, Injibara, Ethiopia

Contribution: Methodology (equal), Supervision (equal), Validation (equal), Visualization (equal), Writing - review & editing (equal)

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Wondwossen Kibrie, 

Wondwossen Kibrie

Department of Forestry and Climate Science, Injibara University, Injibara, Ethiopia

Contribution: Methodology (equal), Validation (equal), Writing - review & editing (equal)

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Fikadu Debebe, 

Fikadu Debebe

Department of Natural Resources Management, Injibara University, Injibara, Ethiopia

Contribution: Conceptualization (equal), Data curation (equal), Formal analysis (equal), Writing - review & editing (equal)

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Etsegenet Emiru, 

Etsegenet Emiru

Department of Forestry and Climate Science, Injibara University, Injibara, Ethiopia

Contribution: Conceptualization (equal), Methodology (equal), Writing - review & editing (equal)

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Bahiru Gedamu, 

Bahiru Gedamu

Department of Forestry and Climate Science, Injibara University, Injibara, Ethiopia

Contribution: Data curation (equal), Validation (equal), Writing - review & editing (equal)

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Mulugeta Tamir, 

Mulugeta Tamir

Department of Forestry and Climate Science, Injibara University, Injibara, Ethiopia

Contribution: Conceptualization (equal), Data curation (equal), Supervision (equal), Writing - review & editing (equal)

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First published: 06 June 2024
https://doi.org/10.1002/ece3.11476
Citations: 4

Abstract

Natural forests are crucial for climate change mitigation and adaptation, but deforestation and degradation challenges highly reduce their value. This study evaluates the potential of natural forest carbon stock and the influence of management interventions on enhancing forest carbon storage capacity. Based on forest area cover, a study was conducted in nine purposely selected forest patches across various forest ecosystems. Data on diameter, height, and environmental variables from various forest management approaches were collected and analyzed with R Ver. 4.1. The findings revealed a substantial difference (p .029) in carbon stock between environmental variables and management interventions. The findings revealed a strong connection between environmental variables and the overall pool of carbon stock within forest patches (p .029). Carbon stocks were highest in the Moist-montane forest ecosystem (778.25 ton/ha), moderate slope (1019.5 ton/ha), lower elevation (614.50 ton/ha), southwest-facing (800.1 ton/ha) and area exclosures (993.2 ton/ha). Accordingly, natural forests, particularly unmanaged parts, are sensitive to anthropogenic stresses, decreasing their ability to efficiently store carbon. As a result, the study highlighted the importance of sustainable forest management, particularly area exclosures and participatory forest management, in increasing forest carbon storage potential.

1 INTRODUCTION

Natural disasters and climate change are two major global environmental challenges caused mostly by fossil fuels and pollution. This is caused to the overuse and depletion of natural resources, particularly vegetation. Climate change had several consequences, including global warming, glacier melting (frequent floods), more fires, and recurring droughts. This is a universal problem that has received significant attention in research and project intervention (Ostwald & Ravindranath, 2008). Forests have an important role in reducing greenhouse gas emissions through carbon sequestration, as well as maintaining natural balance (Malhi, 2010). There has been limited scientific research on the CO2 sequestration capacity of these forest resources in connection to environmental factors and forest management strategies. The majority of carbon pool studies emphasized the importance and role of forest resources in lowering and adjusting to greenhouse gas emissions (Bayley et al., 2021; Havu et al., 2022; Joshi et al., 2021; Piffer et al., 2022). However, understanding the potential amount of carbon stored in the forest with specific environmental variables, as well as demonstrating the role of forest management in enhancing forest carbon store potential, are the first steps toward providing credit and financing for carbon under the international clean development mechanism policy.

The studies by Saatchi et al. (2011), Chazdon et al. (2016), and Mo et al. (2023) highlights the significant carbon storage potential of natural forests, particularly in the Amazon, Asia, and tropical forest ecosystems, for mitigating climate change effects. However, forest management practices like enclosures can enhance carbon storage, while anthropogenic pressure can decline the value of forest resources. Natural regeneration of second-growth forests offers high carbon sequestration potential, but the study's scope is limited and may require future refinement due to differences in forest area assessments. Because African tropical forests, particularly montane forests, store a significant amount of carbon, it is critical that they be protected and maintained responsibly. The average aboveground living tree biomass carbon (AGC) stock in these forests is 149.4 megagrams per hectare, comparable to lowland African forests. The research emphasizes for the forest's carbon pool to increase, sustainable management is required. Nevertheless, as the study relied on remote-sensing techniques, it is difficult to precisely and thoroughly estimate the carbon stocks in these forests (Cuni-Sanchez et al., 2021; Lewis et al., 2009).

There is a large amount of aboveground (biomass) and belowground (SOC) in various regions of Ethiopia's forest ecosystems, including natural forests in several churches, southern, northwestern, and Afromontane forest ecosystems. The carbon stock of Ethiopian forests is significantly influenced by altitude, slope, and topographic aspects. Forests on north-facing slopes have higher carbon stocks compared to those on south-facing slopes, attributed to variations in microclimate, soil properties, and vegetation composition. However, the studies recommend the sustainable forest management to enhance the carbon stock potential. However, the studies by Kendie et al. (2021), Chimdessa (2023), and Ahmed and Lemessa (2024) acknowledges that information on the carbon stock efficiency of different forest types in Ethiopia is scarce, and the study does not provide details on the methods used to collect primary and secondary data, which may limit the generalizability of the findings. Therefore, understanding these species-specific dynamics is crucial for effective forest management and climate change mitigation in Ethiopian forests (Asbeck et al., 2021).

Recent studies conducted in the Awi zone (northwestern Ethiopia) by Gebeyehu et al. (2019) and Sewagegn et al. (2022) proved that there is a huge amount of carbon stock in some church forests, and the natural forests are inaccessible for grazing and human encroachment. However, the forests have experienced significant degradation and deforestation due to agricultural land expansion. Disturbance, elevation, soil pH, and stand structure all have a significant impact on the forest carbon store potential. These studies are limited to church forests and isolated forest patches, and thus lack a broad perspective for policymakers based on a forest ecosystem-based approach that is sensitive to environmental variability. Therefore, studying representative forest patches and specific environmental factors is crucial for policy formulation and sustainable restoration to enhance forest carbon stock potential for climate change mitigation and adaptation.

As a result, this study focuses on (a) quantifying the variation in biomass and carbon stocks among forest ecosystems and forest patches; (b) assessing the impacts of slope, aspect, and elevation on biomass and carbon stocks; (c) exploring the role of selected forest management interventions for biomass and carbon stock improvement; and (d) determining the capacity of a natural forest ecosystem to store soil organic carbon. This study is an essential and fundamental step toward sustainable forest conservation and restoration for reducing climate change and preparing for it (Kassaye et al., 2022). The study makes a substantial contribution to the creation and implementation of policies and strategies for the conservation of forest ecosystem and their use, primarily in measures for coping with and adapting to climate change. Furthermore, understanding the potential amount of carbon stored in forests is the first step toward providing credit and financing for carbon under the international clean development framework policy. International commitments and national initiatives, such as the clean development mechanism, REDD+, and UNFCC, have an intervention action that includes incentives based on the amount of carbon stored in the forest and its geographic distribution, as well as national initiatives by nations to fulfill the commitments (IPCC, 2006).

2 MATERIALS AND METHODS

2.1 Study area description

The investigation took place in northwestern Ethiopia at latitudes 11°10′85″ N and longitudes 36°39′60″ and 36′57″ E. Its elevation ranges from 600 to 3500 m.a.sl., with an average annual rainfall of 1750 mm and temperatures ranging from 17 to 27°C. The research area is topographically quite flat and productive (Kassaye et al., 2023) (Figure 1).

Details are in the caption following the image
FIGURE 1
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Map of the study area.

Awi zone have Woina-Dega (equivalent to mid-altitude) (72%), followed by Dega (equivalent to highland) (17%), and Kolla (equivalent to lowland agroclimatic zone) (11%). The study area ranges from 700 to 3200 a. sl. in altitude, and it is the area with the best annual rainfall distribution (800 to 2700 mm/year) in the area. The temperature in the area ranges between 15 and 24°C. Temperatures in the area range from 15 to 24°C (Mekasha et al., 2022).

From the total area of the zone (8,935,520 ha) of land, 297,133 ha (33.25%) are used for farm practices. However, most of the area in the Awi zone (34.02%) (of 76,554 ha of plantation and 277,842 ha of natural forest) is covered with forest. Rangeland and grazing land cover 24.3% (217,138 ha) of the total area, and other land uses like infrastructure and settlement cover 8.38% (74,853 ha) of the of the area (Table 1).

TABLE 1. The forest cover of study areas (Awi ZOne Agricultural office, 2021).
Agroclimatic zones Total area (ha) Area of natural forest (ha) Cover (%) Area of plantation (ha) Cover (%) Total cover (%)
Highland 47,915.8 2679 5.6 18,752.7 39.13 44.7
Mid-altitude 107,195 21,956 20.5 8976.4 8.4 28.8
Lowland 332,671 162,775 48.9 2663.5 0.8 49.7

2.2 Study forest description and sampling techniques

First, three agroclimatic zones—Highland (2300–3200), Mid-altitude (1500–2200), and Lowland (500–1500) m.a.sl.—were identified as the research area (Azene, 2007; Gorfu & Ahmed, 2011). In each agroclimatic zone parallel forest ecosystem was selected. The studies by Kelbessa and Girma (2011) and Asefa et al. (2020) classified Ethiopian forests into seven ecosystems (Afroalpine, Sub-Afroalpine, moist montane, dry montane, montane grassland, Combretum-Terminalia = broad-leaved deciduous woodland, and Acacia-Commiphora woodland) based on altitude, climatic conditions, and species composition and characteristics. The study forests in northwestern Ethiopia (Awi zone) have been segmented into three forest ecosystems according to the above classification. As a result, three forest ecosystems were selected for study: moist-montane (highland), dry-evergreen-montane (mid-altitude), and broad-lived deciduous (lowland). Three forest patches for each forest ecosystem were selected (one open/not managed, one for PFM, and one for area exclosures). Secondary data for forest patches under management were obtained from the Awi zone Forest and Environmental Protection Department. These data were validated by field observations (observing the forest prior to the study, checking the forest guards, and interviewing local residents). The report provided information on the years since management intervention as well as the area of each forest patch (Table 2). Thus, a total of nine forest patches were selected for this study. These forest patches, under different management interventions and forest ecosystems, were selected purposefully based on their area coverage. That means the forest patch with the highest forest cover (ha) was selected for each forest ecosystem.

TABLE 2. Selected forest patches for this study (Awi ZOne Agricultural office, 2021).
Agroclimatic zone Forest ecosystems Forest patches (local name) Area (ha) Time since intervention Current management interventions No. of quadrates taken
Highland (2300–3200 m. a.sl.). Moist-montane forest ecosystem Gubel 140.9 1999 Area exclosures 10
Darkan 160.7 Open forest 8
Saharakani 379 2000 PFM 5
Mid-altitude (1500–2300 m. a. sl.) Dry-evergreen montane forest Den Maryam 574 Open forest 10
Elala 574 2001 PFM 13
Dikuma 502 1998 Area exclosures 13
Lowland (500–1500 m. a. sl.) Broad-leaved deciduous forest Asech 8.4 Open forest 5
Ambaser 11.93 2005 Area exclosures 3
Abuhay Dengara 14.1 2006 PFM 5
Total plots 72
  • Note: Working definition for this study (from Table 2).
  • a Agroclimatic zone is a land unit accurately represented in terms of major climate conditions and growing periods, making it conducive for specific vegetation and agricultural crops.
  • b Forest ecosystem is complex and interconnected community of living organisms (both plants and animals) that inhabit a variety of forest patches.
  • c Forest patch is the fragmented forests after long and intensive encroachment within forest ecosystem.
  • d Area exclosures is the forest restoration strategy by excluding human and grazing interferences.
  • e PFM is the participatory forest management at which local community engage in the forest management with the support of NGOs and the government.
  • f Open Forest is the forest communally accessed by all communities without any restriction.

2.3 Data collection

Following forest patch selection, the first quadrate, measuring 20 m by 20 m square, was randomly placed 100 m from the forest edge. The second quadrate was then established sequentially at a distance of 400 m from the transect lines and 250 m between them as well. The quadrates and transect lines were placed using GPS throughout the elevation gradient. This idea is based Kent's (2012) vegetation description and analysis approaches for the majority of tropical and subtropical forest ecosystems. The height and diameter of trees with a diameter of 5 cm or more were measured in each quadrate. A total of 72 square quadrates were examined throughout nine forest patches. Height in hypsometer, diameter in diameter tape, and environmental patterns with GPS were collected in these forest patches for evaluation of forest biomass and carbon stock potential. Site parameters such as slope class (gentle slope = 1%–10%, mid-slope = 10.1%–20%, and upper slope = 20.1%–30%), aspect angle data were collected at each quadrate and then classified as Northeast (NE), Northwest (NW), Southeast (SE), and Southwest (SW), and elevation were classified for individual forest patches and collected with GPS from each quadrate (Vásquez-Grandón et al., 2018).

2.4 Data analysis

First, data were collected at each quadrate (0.04 ha) and prepared on a on a sheet before being encoded in the Excel spreadsheet. The factors (independent variables)—forest ecosystems, forest patches, slope, elevation, aspect, and forest management intervention—were then arranged, along with the dependent variables (height and diameter). The aboveground, belowground, total biomass, carbon stock, SOC, and CO2 sequestration potential were then computed using Microsoft Excel. The data was then compiled and prepared for inferential and descriptive analysis of the dependent variable in respect to the factors. The aboveground biomass of woody species with DBH ≥5 cm was calculated using allometric equation of Chave et al. (2014).
AGB = 0.0673 WD * H * DBH 2 0.976 $$ \mathrm{AGB}=0.0673\ {\left({\mathrm{WD}}^{\ast }{H}^{\ast }{\mathrm{DBH}}^2\right)}^{0.976} $$ ()
where; AGB, aboveground biomass in KG; WD, wood density in kg/m3; H, height in meter; DBH, diameter at breast height in cm.

This allometric equation was applied for this study because it is widely used and most appropriate in tropical African natural forest trees (Henry, 2010; IPCC, 2006; Nizami, 2010; Toru & Kibret, 2019). Many studies with biomass and carbon estimation of forest and other agricultural land use in tropical Africa, specifically Ethiopia, use this biomass allometric question (Bazezew et al., 2015a; Dibaba et al., 2019; Gedefaw et al., 2013; Kendie et al., 2021; Meragiaw et al., 2021; Siraj, 2019; Solomon et al., 2017).

The wood-specific density was taken from (Ethiopia's Forest Reference Level Submission to the UNFCCC, 2016) guideline. To simplify the process for estimating belowground biomass, it is recommended that the root-to-shoot ratio value of 1:5 is used; that is, to estimate belowground biomass as 20% of aboveground tree biomass (Bhishma et al., 2010) and (Bazezew et al., 2015a).
BGB = 0.2 * AGB $$ \mathrm{BGB}={0.2}^{\ast}\mathrm{AGB} $$ ()
where: BGB: belowground biomass in kg;
TB = AGB + BGB $$ \mathrm{TB}=\mathrm{AGB}+\mathrm{BGB} $$ ()
CS = TB * 0.47 $$ \mathrm{CS}={\mathrm{TB}}^{\ast }0.47 $$ ()
CO 2 Seq = CS * 3.67 $$ {CO}_2\mathrm{Seq}={\mathrm{CS}}^{\ast }3.67 $$ ()
where; TB, total biomass in Kg; CS, carbon stock (IPCC, 2006).

2.5 Soil sample collection and analysis

The five (1 × 1 m) sup-quadrates at the four corners and in the center were established. The soil samples were then collected at a depth of 20 cm using an augur and composited in the primary quadrate level, followed by three elevations at each forest patch; a total of 27 soil samples were taken to Injibara University soil laboratory for organic carbon analysis. The organic carbon which makes up 58% of soil organic matter, is determined using the modified Walkely–Black method and colorimetric method. These methods involve wet oxidation of organic carbon in an acid dichromate solution, followed by back titration with ferrous ammonium sulphate or photometric determination of Cr3+ (Sortsu & Bekele, 2000). Soil bulk density were collected and analyzed using core Method which is utilized when coarse fragments (particles larger than 2 mm in diameter) account for less than 25% of the total volume. A double-cylinder, drop-hammer sampler with a core is intended to extract a cylindrical core of soil. The sampling head has an inner cylinder that is pressed into the soil using a drop hammer. The inner cylinder containing an undisturbed soil core is then removed and trimmed to the end using a knife, resulting in a core whose volume can be computed using its length and diameter. The weight of this soil core is then determined after it has been dried in an oven at 105°C for approximately 18–24 h (Sortsu & Bekele, 2000). Then, organic carbon was converted to Soil organic carbon with:
SOC = BD * D * % OC * 100 $$ \mathrm{SOC}={\mathrm{BD}}^{\ast }{\mathrm{D}}^{\ast}\%{\mathrm{OC}}^{\ast }100 $$ ()
where SOC, Soil Organic Carbon (ton/ha), BD, Bulk Density (g/cm3), D, soil depth (cm), OC (%), Carbon concentration in the soil under the forest, 1000 is the conversion factor from g/cm2 to ton/ha (Hagos et al., 2021).

Then inferential and descriptive statistics were applied with R Ver.4.1, via carbon as an overall pool and environmental factors, forest ecosystems, forest patches, and forest management interventions.

3 RESULTS

3.1 Carbon stock across the forest ecosystems and forest patches

Aboveground biomass, belowground biomass, carbon stock, and CO2 sequestration potential differed significantly (p .001) across forest ecosystems and forest patches (Table A4). Moist-montane forest ecosystems had the highest carbon stock (778.25 ton/ha and 2856.18 ton/ha CO2 sequestration potential), followed by dry-evergreen-montane forest ecosystems (471.74 ton/ha carbon stock and 1731.33 ton/ha CO2 sequestration potential). The Saharakani forest patch had the highest carbon stock and CO2 sequestration potential (1054.59 ton/ha carbon stock and 3870.34 ton/ha CO2 sequestration potential), followed by the “Gubel” forest patch (829.88 ton/ha carbon stock and 3045.68 ton/ha CO2 equivalent) and the Elala forest patch (648.27 ton/ha carbon stock and 2379 ton/ha CO2 equivalent) (Tables 3 and A2).

TABLE 3. Woody species biomass and carbon stock (ton ha−1) (** shows significant difference at .01 level of significance; the values with a similar letter are insignificant, according to mean separation).
Forest ecosystems Forest patches AGB BGB TB Carbon stock CO2 seq
Moist-montane Forest Darkan 798.37 159.67 958.05d 450.28d 1652.53d
Gubel 1471.4 294.29 1765.71b 829.88b 3045.68b
Saharakani 1869.8 373.97 2243.81a 1054.59b 3870.34b
Dry-evergreen-montane Forest Den Maryam 508.53 101.71 610.23e 286.81e 1052.59e
Dikuma 851.35 170.27 1021.63d 480.16d 1762.20d
Elala 1149.4 229.88 1379.30c 648.27c 2379.16c
Broad-leaved-deciduous Forest Asech 690.66 138.13 828.79d 389.53d 1429.58d
Ambaser 136.32 27.26 163.59f 76.89f 282.18f
Abuhay Dengara 698.29 139.66 837.95d 393.84d 1445.39d
Mean 908.25 181.65 1089.90** 512.25** 1879.96**

3.2 Effects of environmental patterns on carbon stock

There was a significant difference (p .003) between aboveground biomass, belowground biomass, carbon stock, and CO2 sequestration potential across the slope gradients. The gentle slope gradient had the highest carbon stock (1019.5 ton/ha) and CO2 equivalent (3741.6-ton/ha), followed by the steep slope (619.7 ton/ha carbon stock and 2274.4 ton/ha CO2 equivalent) and medium slope (472.2 ton/ha carbon stock and 1732.8 ton/ha CO2 sequestration potential) (Tables 4 and A3).

TABLE 4. Carbon stock (ton ha−1) across various forest patches and slope patterns (** shows significant difference at .01 level of significance).
Slope Forest patches Mid-altitude Highland Lowland Mean Significance (.05)
Den Maryam Elala Dikuma Gubel Darkan Saharakani Asech Ambaser Abuhay Dengara
Gentle TB 709.1 1601 1419.4 3548.6 1169.5 8785.5 915.6 273.3 1100.4 2169.1 p < .003**
C stock 333.3 752.5 667.1 1667.8 549.7 4129.2 430.3 128.4 517.2 1019.5
CO2 seq 1223.1 2761 2448.4 6120.9 2017.3 15,154.0 1579.3 471.4 1898.0 3741.6
Medium TB 1271.5 689.6 2117.5 2153.5 258.2 787.6 446.6 149.6 1167.5 1004.6
C stock 597.6 324.1 995.2 1012.1 121.3 370.2 209.9 70.3 548.7 472.2
Co2 seq 2193.2 1189 3652.5 3714.5 445.3 1358.5 770.3 258.0 2013.8 1732.8
Steep TB 561.9 578.1 2346.9 3698.9 2149.8 2006.7 255.4 157.4 112.2 1318.6
C stock 264.1 271.7 1103.1 1738.5 1010.4 943.1 120.0 74.0 52.7 619.7
CO2 seq 969.2 997.1 4048.2 6380.2 3708.2 3461.3 440.6 271.6 193.6 2274.4
Mean 902.5 1018 2088.7 3337.2 1270.0 4110.7 574.2 206.0 844.9 1594.7
Significance (.05) p < .001**

Carbon pools differed significantly (p 0.04) across aspect gradients. As overall pools, southwest-facing had the highest carbon stock (800.1 ton/ha) followed by west-facing (774.2 ton/ha) and southeast-facing (748.3 ton/ha) (Table 5).

TABLE 5. Carbon stock in aspect gradient (different superscript letters show the differnces between means and similar superscript lettes shows no different between means based on mean separation; *shows significant difference at .05 level of significance).
Aspects Total biomass (ton/ha) Carbon stock (ton/ha) CO2 sequestration (ton/ha)
North 1153.4cd 542.1cd 1989.4cd
South 1647.3a 774.2a 2841.4a
East 1439.2b 674.4b 2482.3b
West 1361.4bc 639.9bc 2348.3bc
North East 1286.2c 604.5c 2218.5c
North West 1020.5d 479.7d 1760.3d
South East 1592.2b 748.3b 2746.4b
South West 1702.3a 800.1a 2936.3a
Mean 1400.3125* 657.9* 2415.363*

There was a significant difference (p .038) in CO2 equivalent among altitudes but not in carbon stock (p .57). Lower altitude has the greatest potential for CO2 equivalent (2253.5 ton/ha) followed by medium altitude (1923.1 ton/ha) and higher altitude (890.1 ton/ha) (Figure 2, Table A1).

Details are in the caption following the image
FIGURE 2
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Carbon stock dynamics in elevation pattern (the values with different letters have significant differences according to mean separation).

3.3 Effects of forest management intervention on carbon stock

Forest carbon pools differed significantly (p .034) across forest management interventions. The mean aboveground biomass (1509.26 ton/ha), belowground biomass (301.85 ton/ha), carbon stock (851.22 ton/ha), and CO2 sequestration potential (3123.98 ton/ha) were all highest in the forest patches under area enclosure (Gubel, Dikuma, and Ambaser) (Table 6).

TABLE 6. Effects of forest management interventions on carbon stock (the values with different letters have significant differences according to mean separation).
Forest management intervention Forest patches AGB (ton/ha) BGB (ton/ha) TB (ton/ha) Carbon (ton/ha) CO2 (ton/ha)
Participatory forest management Saharakani 1869.82 373.96 2243.78 1054.58 3870.30
Den Maryam 1508.53 301.71 1810.24 850.81 3122.48
Elala 1149.42 229.88 1379.31 648.27 2379.17
Mean 1509.26b 301.85b 1811.11b 851.22b 3123.98b
Area exclosures Ambaser 1161.40 232.28 1393.68 655.03 2403.96
Gubel 2171.44 434.29 2605.73 1224.69 4494.62
Dikuma 1951.35 390.27 2341.62 1100.56 4039.07
Mean 1761.40a 352.28a 2113.68a 993.43a 3645.88a
Open forest Darkan 798.36 159.67 958.04 450.28 1652.51
Asech 690.66 138.13 828.79 389.53 1429.58
Abuhay Dengara 698.30 139.66 837.96 393.84 1445.40
Mean 729.11c 145.82c 874.91c 411.21c 1509.17c
p-value .034

3.4 Soil organic carbon across the forest patches

Soil organic carbon (SOC) differed significantly (p .029) between forest patches and elevation gradient within forest patches. The highest SOC was found at lower elevation (mean: 61.7 ton/ha) and Asech forest patches (105 ton/ha), followed by the Darkan and Den Maryam forest patches (70 ton/ha) (Figure 3, Figure A1).

Details are in the caption following the image
FIGURE 3
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Soil organic carbon across forest patches and elevation gradients.

4 DISCUSSION

4.1 Carbon stock across environmental patterns

Because of participatory forest management interventions, Saharakani and Elala forest patches are relatively intact forests with large trees in both height and diameter. This may be the reason why these forest patches have a high carbon stock. The studied forest patches have the highest carbon stock as an overall pool (254–1177 ton/ha) than forest areas' carbon pools in other parts of Ethiopia (84.5–639.87 ton/ha) (Bazezew et al., 2015b; Belay et al., 2018; Feyissa et al., 2013; Gedefaw et al., 2013; Kendie et al., 2021; Solomon et al., 2017).

Furthermore, the studied forest ecosystems had the highest carbon stock potential among tropical, subtropical, and temperate natural forest ecosystems (Alvarez et al., 2012; Jindal et al., 2008; Macdicken et al., 2015; Malhi, 2010; Nizami, 2010; Payton & Weeks, 2012). Regardless of the circumstances, the study forest patches appear to be intact from the outside, yet they have degraded, resulting in a decrease in carbon stock.

The gentle slope has the greatest potential for biomass, carbon stock, and CO2 sequestration, all of which are directly related to vegetation growth performance. This is due to the gentle slope's ideal climatic and edaphic conditions for most woody species. The idea was supported by Yilma and Derero (2020) who found that environmental variables such as altitude, aspect, and slope have a greater influence on forest carbon dynamics. The mild slope provides the highest potential for biomass and carbon storage. The gentle slope is practically flat ground with stable soil and other biophysical factors that promote higher vegetation growth performance; this is why flat areas of forest patches have significant carbon reserves. The question is, why is the carbon stock on the medium slope smaller than on the high slope. Higher human and livestock pressures on the medium slope may lower the forest's value, whereas the steep slope is unsuitable for illegal harvesting and grazing. However, Dibaba et al. (2019) found that the carbon stock of woody species is negatively connected with slope patterns, meaning that carbon stock rises when the slope gradient lowers. This demonstrated that a moderate slope contains more carbon than a medium or steep slope. This concept is agreed upon and contradicts the study's findings. This gap could be explained by the strength of human and grazing stresses.

Total biomass, carbon stock, and potential CO2 sequestration have all reduced as altitude increased. This could be related to stem density, species diversity, and woody species' growth rates. The lower altitude, located at the forest's foot, has a high species diversity, suitable soil conditions, and a high stem density. The individual trees in flat slope are relatively tall with a great diameter. The tendency of all of the above trees' growth and edaphic conditions decline as altitude rise. This determines the carbon stock potential for the altitudinal patterns. This concept is consistent with Dibaba et al. (2019), who stated that carbon stock in woody species has a strong negative correlation with altitudinal gradients, indicating that carbon stock decreases as an altitude of a given forest increases. However, according to Feyissa et al. (2013), carbon stock overall has an increasing trend with an increasing altitudinal gradient. Scientifically, this study result is fairly acceptable because carbon stock is closely related to growth performance, vegetation features, and environmental factors. However, natural and anthropogenic disturbances may have an impact on whether the relationship between altitude and carbon stock is negatively or positively correlated.

Southwest-facing has the highest carbon stock as a total carbon pool. Aspect parameters like as sunlight intensity and direction have a significant impact on vegetation growth performance and woody species diversity, both of which are linked to woody biomass and carbon stocks. In most studies by Alvarez et al. (2012), Feyissa et al. (2013), Assefa et al. (2013), Gedefaw et al. (2013), Liu et al. (2014), and Belay et al. (2018), north- and east-facing countries with strong light intensity and perpendicular light direction, specifically in tropical countries, have higher vegetation performance and carbon stock. However, the studies suggest that this scenario is dependent on anthropogenic disturbances and forest management intensity. Most forest patches in this study are under grazing and human pressure (open forest patches without any management; Darkan, Den Maryam, and Asech), and some forests have hills in the south aspect that makes inaccessible for grazing and illegal harvesting (Dikuma and Saharakani forest patches). This could change the conventional wisdom that south-facing is better for carbon storage. Woody species in the study forest, on the other hand, prefer south-facing sites due to the need for a slight light concentration as a result of climate change and timberline shifts. Climate change, according to Ju and Turton (2014) and Payn et al. (2015) shifts the timberline in terms of altitude and aspect, causing most woody species in the tropics to prefer south and southwest facings.

4.2 Effects of forest management intervention on carbon stock

Some forest patches in the study area are managed through participatory forest management (Saharakani, Elala, and Abuhay Dengara), while others are subject to area exclosures. Forests under area exclosures management had enhanced carbon stock, which could be attributed to better woody species diversity, growth performance, and wood density of valuable important tree species. Forest management, as a general intervention, has the potential to raise forest value and consequently carbon stock capacity. In the study area, carbon stock increased by 41.4% from open, unmanaged forest to forest under area exclosures. There are numerous arguments that forest management interventions improve the carbon stock potential of forest ecosystems (Bazezew et al., 2015a, 2015b; Daba et al., 2022; Jati, 2012; Noormets et al., 2014). Furthermore, forest under area exclosures improves carbon stock potentials when compared to open forest patches, and it has intermediate biomass and carbon stock potential when compared to open forests and reference church forests (Kassaye et al., 2022). However, socioeconomic factors should be considered for more successful enhancement of natural forest biomass and carbon stock potential under area exclosures for long-term climate change mitigation and adaptation (Kassaye et al., 2022).

4.3 Soil organic carbon across the forest patches

The study forest patches are located in various agroclimatic zones and alongside forest ecosystems, resulting in varying microclimatic conditions. The concentration of organic carbon is the most important determinant factor for soil organic carbon. Broad-leaved deciduous forest ecosystems have higher soil organic carbon levels due to the high litterfall concentration and the rapid decomposition of it, whereas moist-montane forest ecosystems have higher vegetation growth rates. The concept is similar to that of Dalle (2014), who stated that the broad-leaved deciduous forest ecosystem found in lowland agroclimatic zones has the highest SOC due to its high decomposition rate, whereas the moist-montane forest ecosystem has higher litter accumulation but a slow decomposition rate, thus having a low SOC.

Furthermore, the study by Lal (2008), Assefa et al. (2013), Alemu (2014), and Djibril et al. (2016) from around the world found that soil organic carbon is highly dynamic spatially and temporally, even on a small scale. It is a relatively long-term carbon reservoir that plays an important role in climate change mitigation and adaption.

5 CONCLUSION

The forest patches located in moist-montane forest ecosystems have the greatest potential for carbon storage, followed by the forest patches found in dry-evergreen-montane forest ecosystems and the forest patches found in broad-leaved deciduous forest ecosystems. This is due to the high height and diameter growth of trees, varied environmental factors, and different forest ecosystems. Slope, aspect, and elevation gradients have all had a significant impact on woody biomass, carbon stock, and CO2 sequestration potential in the study area's natural forest patches. Overall, carbon stock and slope gradient are inversely related, meaning that carbon stock is higher on slight slopes. Carbon stock decreases proportionally to the elevation gradient. The southwest-facing has the highest carbon stock. The aspect factor, which is influenced by sunlight intensity and direction, has a major impact on vegetation growth performance and woody species variety, both of which are linked to woody biomass and carbon stocks. The forest patches found in the Broad-leaved forest ecosystem have greater soil organic carbon levels due to the quick decomposition of tree litter. The study area's natural forest patches have a large amount of carbon stock, which helps mitigate the crisis of climate change regardless of forest degradation or deforestation. The study concluded that forest management interventions, specifically forest management with area exclosures, can improve the carbon stock potential and values of forest ecosystems.

The study area's highest mean overall carbon pool (woody species and soil organic carbon) indicates that natural forests have a high potential for mitigating climate change by removing a reasonable amount of carbon from the atmosphere. However, appropriate conservation priorities and credit have yet to be assigned, and these resources are rapidly depleting. Even if there are some forest management interventions, it is not sure if area exclosures and PFM are successful or not (required further investigation). As a result, urgent conservation (area exclosures and PFM) for the open forest patches has been required, followed by appropriate and site-specific sustainable forest management to maintain the health of the forest ecosystem (in terms of function, structures, and composition, particularly natural regeneration; number of seedlings > saplings > mature trees; and optimum species diversity, cover abundances, and lifeforms) for climate change mitigation, social stability, and economic benefit from natural forests in the long term. According to international commitments, international agencies such as REDD+ and the United Nations intergovernmental panel on climate change mitigation should be involved.

AUTHOR CONTRIBUTIONS

Melkamu Kassaye: Conceptualization (equal); formal analysis (equal); investigation (equal); methodology (equal); software (equal); writing – original draft (equal). Yonas Derebe: Methodology (equal); supervision (equal); validation (equal); visualization (equal); writing – review and editing (equal). Wondwossen Kibrie: Methodology (equal); validation (equal); writing – review and editing (equal). Fikadu Debebe: Conceptualization (equal); data curation (equal); formal analysis (equal); writing – review and editing (equal). Etsegenet Emiru: Conceptualization (equal); methodology (equal); writing – review and editing (equal). Bahiru Gedamu: Data curation (equal); validation (equal); writing – review and editing (equal). Mulugeta Tamir: Conceptualization (equal); data curation (equal); supervision (equal); writing – review and editing (equal).

ACKNOWLEDGMENTS

First and foremost, we would like to thank Injibara University for its assistance and other facilities. Finally, we'd like to express our gratitude to the university colleges and supporting staff for their efforts in making this project a success.

    CONFLICT OF INTEREST STATEMENT

    There is no conflict of interest or copyright issue.

    APPENDIX A

    Details are in the caption following the image
    FIGURE A1
    Open in figure viewerPowerPoint
    Soil result from laboratory.
    TABLE A1. Carbon stock across the agroecology.
    Carbon pool/forest community N Mean SD SE 95% confidence interval for mean Minimum Maximum
    Lower bound Upper bound
    CO2 conc (ton/ha) Highland 23 2288.1087 2835.54477 591.25196 1061.9272 3514.2902 14.80 10,603.20
    Mid-altitude 36 1240.7722 1059.08345 176.51391 882.4299 1599.1145 18.30 3866.70
    Lowland 13 1089.0769 853.24657 236.64802 573.4652 1604.6887 193.60 2479.70
    Total 72 1547.9486 1852.35186 218.30176 1112.6673 1983.2300 14.80 10,603.20
    Carbon stock (ton/ha) Highland 23 623.4609 772.61992 161.10239 289.3550 957.5668 4.00 2889.10
    Mid-altitude 36 338.0889 288.57841 48.09640 240.4480 435.7298 5.00 1053.60
    Lowland 13 296.7385 232.49792 64.48332 156.2414 437.2355 52.70 675.70
    Total 72 421.7833 504.72458 59.48236 303.1789 540.3878 4.00 2889.10
    Total Biomass (ton/ha) Highland 23 1326.5174 1643.88349 342.77340 615.6489 2037.3859 8.60 6147.10
    Mid-altitude 36 719.3361 613.99784 102.33297 511.5891 927.0831 10.60 2241.70
    Lowland 13 631.3923 494.68866 137.20195 332.4549 930.3297 112.20 1437.60
    Total 72 897.4181 1073.88801 126.55892 645.0667 1149.7694 8.60 6147.10
    Below-ground Biomass (ton/ha) Highland 23 264.7870 208.81435 43.54080 174.4889 355.0851 61.40 912.90
    Mid-altitude 36 172.7472 214.82110 35.80352 100.0622 245.4322 13.70 989.90
    Lowland 13 113.1308 66.68605 18.49538 72.8328 153.4287 24.90 217.50
    Total 72 191.3847 200.13579 23.58623 144.3551 238.4143 13.70 989.90
    Aboveground Biomass (ton/ha) Highland 23 1323.9304 1044.06360 217.70231 872.4435 1775.4174 307.10 4564.50
    Mid-altitude 36 863.7611 1074.10968 179.01828 500.3347 1227.1875 68.40 4949.70
    Lowland 13 565.6769 333.50012 92.49629 364.1448 767.2090 124.30 1087.50
    Total 72 956.9389 1000.67910 117.93116 721.7908 1192.0869 68.40 4949.70
    TABLE A2. Carbon stock across the forest communities.
    Carbon pool/forest community N Mean SD SE 95% confidence interval for mean Minimum Maximum
    Lower bound Upper bound
    CO2 conc (ton/ha) Darkan 8 288.6125 698.97277 247.12419 −295.7434 872.9684 14.80 2017.30
    Gubel 10 2396.0000 1231.73400 389.50849 1514.8706 3277.1294 721.80 4216.90
    Saharakani 5 5271.5200 4560.93336 2039.71141 −391.6268 10,934.6668 971.20 10,603.20
    Den Maryam 10 930.6000 917.49773 290.13826 274.2617 1586.9383 135.90 2917.10
    Dikuma 13 1912.6923 1140.18377 316.23008 1223.6861 2601.6985 177.30 3866.70
    Elala 13 807.4462 753.28621 208.92400 352.2399 1262.6525 18.30 2287.40
    Asech 5 1027.9400 836.53903 374.11163 −10.7604 2066.6404 440.60 2479.70
    Ambaser 3 333.6667 119.47424 68.97848 36.8762 630.4571 258.00 471.40
    Abuhay Dingara 5 1603.4600 844.84404 377.82574 554.4476 2652.4724 193.60 2249.00
    Total 72 1547.9486 1852.35186 218.30176 1112.6673 1983.2300 14.80 10,603.20
    Carbon stock (ton/ha) Darkan 8 78.6375 190.46744 67.34041 −80.5973 237.8723 4.00 549.70
    Gubel 10 652.8700 335.61813 106.13177 412.7833 892.9567 196.70 1149.00
    Saharakani 5 1436.3600 1242.74686 555.77329 −106.7140 2979.4340 264.60 2889.10
    Den Maryam 10 253.5600 249.98029 79.05071 74.7349 432.3851 37.00 794.80
    Dikuma 13 521.1769 310.67638 86.16612 333.4371 708.9168 48.30 1053.60
    Elala 13 220.0231 205.26856 56.93126 95.9805 344.0656 5.00 623.30
    Asech 5 280.1000 227.95426 101.94424 −2.9426 563.1426 120.00 675.70
    Ambaser 3 90.9000 32.52860 18.78040 10.0945 171.7055 70.30 128.40
    Abuhay Dingara 5 436.8800 230.21930 102.95720 151.0250 722.7350 52.70 612.80
    Total 72 421.7833 504.72458 59.48236 303.1789 540.3878 4.00 2889.10
    Total Biomass (ton/ha) Darkan 8 167.3250 405.21598 143.26548 −171.4440 506.0940 8.60 1169.50
    Gubel 10 1389.0700 714.09566 225.81687 878.2367 1899.9033 418.50 2444.70
    Saharakani 5 3056.1200 2644.16342 1182.50583 −227.0425 6339.2825 563.00 6147.10
    Den Maryam 10 539.5200 531.91568 168.20651 159.0104 920.0296 78.80 1691.20
    Dikuma 13 1108.8769 661.01381 183.33225 709.4303 1508.3236 102.80 2241.70
    Elala 13 468.1154 436.71580 121.12317 204.2107 732.0201 10.60 1326.10
    Asech 5 595.9400 484.98998 216.89411 −6.2546 1198.1346 255.40 1437.60
    Ambaser 3 193.4333 69.27643 39.99676 21.3411 365.5255 149.60 273.30
    Abuhay Dingara 5 929.6200 489.83048 219.05885 321.4151 1537.8249 112.20 1303.90
    Total 72 897.4181 1073.88801 126.55892 645.0667 1149.7694 8.60 6147.10
    Below-ground Biomass (tone/ha) Darkan 8 159.6625 70.75539 25.01581 100.5095 218.8155 61.40 280.80
    Gubel 10 294.3000 200.02215 63.25256 151.2128 437.3872 94.00 666.20
    Saharakani 5 373.9600 319.15447 142.73022 −22.3226 770.2426 127.80 912.90
    Den Maryam 10 101.7100 96.96194 30.66206 32.3476 171.0724 13.70 313.30
    Dikuma 13 170.2615 140.78883 39.04780 85.1837 255.3394 17.60 468.30
    Elala 13 229.8769 316.19332 87.69625 38.8032 420.9506 15.90 989.90
    Asech 5 138.1200 18.92121 8.46182 114.6262 161.6138 105.40 153.00
    Ambaser 3 27.3000 2.08806 1.20554 22.1130 32.4870 24.90 28.70
    Abuhay Dingara 5 139.6400 76.13608 34.04909 45.1046 234.1754 55.60 217.50
    Total 72 191.3847 200.13579 23.58623 144.3551 238.4143 13.70 989.90
    Aboveground Biomass (ton/ha) Darkan 8 798.3625 353.77333 125.07776 502.6006 1094.1244 307.10 1404.10
    Gubel 10 1471.4400 1000.13388 316.27010 755.9873 2186.8927 469.80 3331.20
    Saharakani 5 1869.8200 1595.75010 713.64114 −111.5655 3851.2055 639.20 4564.50
    Den Maryam 10 508.5300 484.79421 153.30539 161.7291 855.3309 68.40 1566.30
    Dikuma 13 851.3538 703.90909 195.22925 425.9858 1276.7219 88.00 2341.40
    Elala 13 1149.4231 1580.98315 438.48583 194.0445 2104.8016 79.60 4949.70
    Asech 5 690.6600 94.53980 42.27948 573.2733 808.0467 527.20 765.00
    Ambaser 3 136.3333 10.46438 6.04161 110.3384 162.3283 124.30 143.30
    Abuhay Dingara 5 698.3000 380.67106 170.24127 225.6345 1170.9655 278.10 1087.50
    Total 72 956.9389 1000.67910 117.93116 721.7908 1192.0869 68.40 4949.70
    TABLE A3. Carbon stock across the slope pattern.
    Carbon pool/forest community N Mean Std. deviation Std. error 95% confidence interval for mean Minimum Maximum
    Lower bound Upper bound
    CO2 conc (ton/ha) Lower 26 2186.4808 2539.68144 498.07251 1160.6812 3212.2803 18.30 10,603.20
    Medium 21 1490.1476 1127.44379 246.02840 976.9414 2003.3539 14.80 3866.70
    Higher 25 932.4280 1238.29492 247.65898 421.2850 1443.5710 17.70 4216.90
    Total 72 1547.9486 1852.35186 218.30176 1112.6673 1983.2300 14.80 10,603.20
    Carbon stock (ton/ha) Lower 26 595.7731 692.00075 135.71251 316.2679 875.2782 5.00 2889.10
    Medium 21 406.0286 307.20266 67.03712 266.1916 545.8655 4.00 1053.60
    Higher 25 254.0680 337.41837 67.48367 114.7885 393.3475 4.80 1149.00
    Total 72 421.7833 504.72458 59.48236 303.1789 540.3878 4.00 2889.10
    Total Biomass (ton/ha) Lower 26 1267.5962 1472.35959 288.75347 672.8972 1862.2951 10.60 6147.10
    Medium 21 863.9190 653.63158 142.63410 566.3895 1161.4486 8.60 2241.70
    Higher 25 540.5720 717.89894 143.57979 244.2379 836.9061 10.30 2444.70
    Total 72 897.4181 1073.88801 126.55892 645.0667 1149.7694 8.60 6147.10
    Below-ground Biomass (tone/ha) Lower 26 256.3615 274.92939 53.91809 145.3152 367.4079 13.70 989.90
    Medium 21 156.3000 107.17110 23.38665 107.5163 205.0837 17.60 468.30
    Higher 25 153.2800 151.73579 30.34716 90.6465 215.9135 24.00 666.20
    Total 72 191.3847 200.13579 23.58623 144.3551 238.4143 13.70 989.90
    Aboveground Biomass (ton/ha) Lower 26 1281.8115 1374.65406 269.59184 726.5767 1837.0463 68.40 4949.70
    Medium 21 781.5238 535.87001 116.93642 537.5987 1025.4489 88.00 2341.40
    Higher 25 766.4200 758.66584 151.73317 453.2581 1079.5819 120.20 3331.20
    Total 72 956.9389 1000.67910 117.93116 721.7908 1192.0869 68.40 4949.70
    TABLE A4. Carbon stock inferential statistics.
    Tests of between-subjects effects
    Source Dependent variable Type III sum of squares df Mean square F Sig.
    Corrected Model Agroecology 34.611 15 2.307
    Aboveground Biomass (ton/ha) 20,915,299.408 15 1,394,353.294 1.556 .117
    Belowground Biomass (tone/ha) 836,642.994 15 55,776.200 1.556 .117
    Total Biomass (ton/ha) 44,950,874.694 15 2,996,724.980 4.544 .000
    Carbon stock (ton/ha) 9,929,712.050 15 661,980.803 4.544 .000
    CO2 conc (ton/ha) 133,743,156.168 15 8,916,210.411 4.544 .000
    Intercept Agroecology 187.703 1 187.703
    Aboveground Biomass (ton/ha) 39,307,591.357 1 39,307,591.357 43.866 .000
    Belowground Biomass (tone/ha) 1,572,290.270 1 1,572,290.270 43.866 .000
    Total Biomass (ton/ha) 51,008,654.948 1 51,008,654.948 77.351 .000
    Carbon stock (ton/ha) 11,267,560.094 1 11,267,560.094 77.352 .000
    CO2 conc (ton/ha) 151,764,378.978 1 151,764,378.978 77.351 .000
    Forest Agroecology 33.080 8 4.135
    Aboveground Biomass (ton/ha) 12,446,156.105 8 1,555,769.513 1.736 .110
    Belowground Biomass (tone/ha) 497,870.656 8 62,233.832 1.736 .110
    Total Biomass (ton/ha) 32,811,086.109 8 4,101,385.764 6.219 .000
    Carbon stock (ton/ha) 7,247,894.071 8 905,986.759 6.220 .000
    CO2 conc (ton/ha) 97,623,026.707 8 12,202,878.338 6.220 .000
    Alt Agroecology 0.000 2 0.000
    Aboveground Biomass (ton/ha) 119,279.047 2 59,639.523 .067 .936
    Belowground Biomass (tone/ha) 4773.288 2 2386.644 .067 .936
    Total Biomass (ton/ha) 2,489,289.139 2 1,244,644.570 1.887 .161
    Carbon stock (ton/ha) 549,914.452 2 274,957.226 1.888 .161
    CO2 conc (ton/ha) 7,406,590.767 2 3,703,295.383 1.888 .161
    Slope Agroecology 0.000 2 0.000
    Aboveground Biomass (ton/ha) 4,225,450.559 2 2,112,725.280 2.358 .104
    Belowground Biomass (tone/ha) 169,046.861 2 84,523.431 2.358 .104
    Total Biomass (ton/ha) 4,934,542.118 2 2,467,271.059 3.741 .030
    Carbon stock (ton/ha) 1,090,144.887 2 545,072.443 3.742 .030
    CO2 conc (ton/ha) 14,682,311.542 2 7,341,155.771 3.742 .030
    Aspect Agroecology 0.000 3 0.000
    Aboveground Biomass (ton/ha) 4,799,868.922 3 1,599,956.307 1.785 .160
    Belowground Biomass (tone/ha) 192,003.381 3 64,001.127 1.786 .160
    Total Biomass (ton/ha) 4,157,112.256 3 1,385,704.085 2.101 .110
    Carbon stock (ton/ha) 918,358.279 3 306,119.426 2.102 .110
    CO2 conc (ton/ha) 12,368,616.373 3 4,122,872.124 2.101 .110
    Error Agroecology 0.000 56 0.000
    Aboveground Biomass (ton/ha) 50,181,164.924 56 896,092.231
    Belowground Biomass (tone/ha) 2,007,214.659 56 35,843.119
    Total Biomass (ton/ha) 36,928,843.232 56 659,443.629
    Carbon stock (ton/ha) 8,157,317.850 56 145,666.390
    CO2 conc (ton/ha) 109,872,569.192 56 1,962,010.164
    Total Agroecology 284.000 72
    Aboveground Biomass (ton/ha) 137,029,171.000 72
    Belowground Biomass (tone/ha) 5,481,081.710 72
    Total Biomass (ton/ha) 139,865,577.910 72
    Carbon stock (ton/ha) 30,895,914.880 72
    CO2 conc (ton/ha) 416,138,158.350 72
    Corrected Total Agroecology 34.611 71
    Aboveground Biomass (ton/ha) 71,096,464.331 71
    Belowground Biomass (tone/ha) 2,843,857.653 71
    Total Biomass (ton/ha) 81,879,717.927 71
    Carbon stock (ton/ha) 18,087,029.900 71
    CO2 conc (ton/ha) 243,615,725.360 71

    OPEN RESEARCH BADGES

    Open Data

    This article has earned an Open Data badge for making publicly available the digitally-shareable data necessary to reproduce the reported results. The data is available at Mendeley data repository (https://doi.org/10.17632/7wwmwjgrzk.1).

    DATA AVAILABILITY STATEMENT

    The datasets generated during and/or analyzed during the current study are available as supporting material uploaded in this journal submission portal. In addition, the data is available at: Mekonen (2024).

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