Forest cover changes in southwest Ethiopia have significant social and environmental implications. This study explored the primary drivers and effects of forest cover changes from 1986 to 2019 in the region. A mixed-methods approach was employed, combining socioeconomic data, geospatial data, and both global and locally adapted ecosystem services value (ESV) coefficients. The findings indicated a significant decline in forest cover attributed to its conversion to human-modified land types, with approximately 87.3 percent of respondents acknowledging this reduction in the study area. The intricate factors driving forest cover changes include agricultural expansion, population growth, land grabbing, fuelwood collection, and illegal logging, highlighting the necessity for a multifaceted approach to tackle these challenges. Logistic regression analysis demonstrated that various sociodemographic factors significantly influenced (p < 0.05) respondents’ perceptions regarding the drivers of forest cover change. The ESV over three timeframes indicated a decrease from around US$226.75 million to US$172.81 million, equating to a total loss of approximately 23.79%. The ESV of shrublands fell by 46.35%, wetlands by 32.63%, dense forests by 23.77%, and open forests by 17.29% during the evaluation period. In contrast, cropland rose by 38.06% and agroforestry by 20.03%. Significant ecosystem services, like water supply and disturbance regulation, faced reductions of over 30%. Correlation analysis reveals that forest conservation strategies are linked to the drivers of deforestation, suggesting that integrated approaches can mitigate deforestation and promote sustainable resource management. Positive attitudes toward afforestation, enhanced enforcement, community forest management, and improved agricultural practices highlight opportunities for collaborative conservation efforts. Achieving sustainable conservation requires increased community engagement and governance, along with comprehensive policies to address changes in forest cover and their impacts on ecosystem services.

1. Introduction

Forests are indispensable for the survival of above 1.6 billion individuals and support 80% of terrestrial biodiversity [1, 2]. They provide clean water and air, habitats for wildlife, recreational areas, and more than 5000 daily-use products [3]. Forests also play a vital role in securing the livelihoods of small-scale farmers [4] and contribute significantly to sustainable development goals [5]. Additionally, they help regulate local climates, sequester carbon dioxide [6, 7], maintain healthy watersheds [8–10], and provide significant environmental values, estimated at around US$4.7 trillion annually [11].

Over the past 60 years, global forest cover has reduced by about 82 million hectares, resulting in a loss of over 60% of global forest cover per capita between 1960 and 2019 [12]. Forest cover change continues at an alarming speed, with an average annual loss of 4.7 million ha [2]. Most forest losses occur in low-income countries in the tropics, particularly in Africa, where unsustainable land use practices contribute to widespread deforestation [12]. Forest cover changes are derived by social, economic, and environmental factors, including anthropogenic activities such as agriculture and infrastructure development [13]. In the tropics, deforestation resulted from direct human activities such as agriculture and timber extraction, influenced by underlying factors like demographic pressure and poverty [13, 14]. Agriculture is the leading cause of deforestation, responsible for about 80% of global [15] and 62% of African deforestation [16]. Notably, smallholder agriculture expansion accounted for 92% of Africa’s deforestation from 2001 to 2015 [17].

The loss of forest cover has significantly harmed tropical landscapes [13]. The reduction of forest cover due to deforestation and degradation has severe negative impacts on the environment, biodiversity, and human well-being [5, 18]. Deforestation can lead to reduced income and employment opportunities in rural areas [19], as well as increased food insecurity in African countries [20, 21]. Forest loss also increases vulnerability and reduces resilience to environmental shocks and disasters, negatively affecting rural livelihoods [22].

Ethiopia has experienced significant deforestation over the past three millennia, resulting in isolated patches of forest in remote regions [23]. About 40% of its original forest cover around 20th century [23, 24] has been reduced, leaving only 11.4% remaining [25]. Ethiopia is considered one of the countries that have lost more than 90% of its original forest cover [26]. Deforestation in Ethiopia results from a complex interplay of various factors that shape human–environment interactions [27]. It occurred largely due to the spread of agriculture and population growth [23, 28–30] and policy-related factors [14, 27].

The southwestern region of Ethiopia is one of the areas where the majority of Ethiopia’s high forests are now confined [23, 27]. The forests of southwestern Ethiopia represent a distinctive ecosystem that is rich in biodiversity and globally significant as the genetic origin of Coffea arabica L. [27, 31]. They are recognized as one of the last two substantial blocks of high forest in the country [32] and support the major vegetation type in Ethiopia [33]. This area is also essential for providing various forest-related goods and services, including spices, forest honey, and nontimber forest products that are vital for local communities [27, 31]. However, it has experienced significant deforestation due to humanoid activity, with approximately 80% of its forest declining between 1957 and 2007 [34]. Additionally, it has been significantly fragmented over the past 3 decades [29, 35].

The consequences of deforestation and the resulting environmental degradation have significantly affected the socioeconomic landscape of Ethiopia, leading to challenges such as food insecurity, poverty, migration, and political instability [23]. Local communities face severe repercussions from deforestation, including diminished soil fertility, loss of biodiversity, and disrupted water cycles [18, 36]. Deforestation has also impacted the hydrological response of watersheds, resulting in changes to sediment yield [36, 37]. The effects of deforestation also extend well beyond immediate consequences, carrying long-term socioeconomic and environmental implications, including climate change, desertification, economic losses, and social conflicts [38]. Therefore, tackling forest degradation, loss, and fragmentation has become a primary focus of recent initiatives [39].

Forest cover change and its drivers have garnered attention due to their significant ecological and socioeconomic impacts, shifting focus from individual trees to broader dynamics like afforestation and biodiversity loss [40]. Remote sensing (RS) tools are now used to monitor these changes and support sustainable forest management [40–42], while combining local knowledge with RS and geographic information system (GIS) data enhances the understanding of forest dynamics [43, 44]. This integration improves data accuracy and addresses gaps, making RS, GIS, and socioeconomic data vital for analyzing forest cover changes. A comprehensive approach that considers environmental, socioeconomic, and ecological factors is necessary for effective resource management. Furthermore, linking forest cover change with empirical assessments of forest ecosystem services (FESs) aids in understanding local impacts [45–49] and aligns management efforts with community priorities, promoting targeted strategies for sustainable forest management.

Reversing deforestation and degradation demands innovative strategies that balance socioeconomic and ecological factors [39, 50]. In Ethiopia, efforts to combat deforestation since the 1970s have seen limited success due to a campaign-based approach [23]. Additionally, government initiatives to restore degraded lands and manage forests have struggled due to insufficient local adaptations and a lack of long-term resilience strategies [51].

Recent studies highlight the significance of understanding local perspectives, identifying drivers of change, and creating adaptable forest conservation strategies that integrate ecological and socioeconomic goals. Solomon et al. [28] demonstrated that effective forest management necessitates comprehending local communities’ views and specific drivers of change. Another study also revealed that for conservation initiatives to be successful, they must consider site-specific factors to improve both conservation outcomes and livelihoods [51]. Collaborative forest management plans that align ecological and socioeconomic objectives in Ethiopia are essential, as emphasized by [52]. Studies recommend against a one-size-fits-all conservation strategy [39, 53], emphasizing the need for diversified systems that align with specific social-ecological contexts and adaptable approaches.

The southwestern region of the country is characterized by diverse biophysical and sociocultural factors, resulting in varied society–nature interactions that affect natural resources, including forests, in different ways. Most studies concerning forests in the southwest either focus on LU/LC change dynamics [35, 49, 54] or are conducted in geographically distinct areas with varying sociocultural characteristics [29, 30, 55–57]. Consequently, they fail to effectively inform land use policies in our study area due to the complex interplay of social, environmental, and diverse biophysical and socioeconomic factors [58].

To sustainably conserve the forest blocks of southwest Ethiopia and protect their ecosystem services, it is essential to tackle site-specific drivers, understand community perceptions of current conservation strategies, and assess the effects of forest cover changes. This study seeks to advance our understanding of forest cover change dynamics in the moist evergreen Afromontane forests of the Bench-Sheko Zone by examining the factors that drive these changes and their effects. The research site, located in the upper catchments of the Baro-Akobo basin, plays a vital role in watershed protection and biodiversity conservation in the region.

This study seeks to address this gap by examining forest cover data from 1986 to 2019, combined with relevant socioeconomic data and both global and locally adapted ecosystem services value (ESV) coefficients. It identifies the key drivers and impacts of changes in forest cover while proposing strategies to mitigate deforestation. The findings will aid in formulating effective conservation strategies for the region and potentially beyond.

2. Materials and Methods

2.1. Study Area

The research site covered Shey Bench and Semen Bench districts of Bench-Sheko Zone within Southwest Ethiopia Peoples’ Regional (SWEPR) State, Ethiopia. It lies between 6°44′24″ to 7°12′18″ N latitude and 35°32′1″ to 35°53′2″ E longitude (Figure 1).

Physically, it was characterized by a hilly landscape, with the largest forest coverage lying on the slope of the terrain. The study landscape is characterized by a moist and evergreen Afromontane forest ecosystem [59]. The altitudes vary between 1144 and 2696 m above sea level. The mean annual precipitation is 1780 mm per year. The average air temperature is between 13°C and 27°C [36]. According to the Harmonized African Soil Map, leptosols predominate on mountain ridges, nitisols on hill slopes, and fluvisols on valley floors [60]. The research area was inhabited by a total of 288,391 individuals, comprising 48.1% males and 51.9% females [61]. The local community relies heavily on mixed agriculture, which combines rain-fed farming and animal husbandry for sustenance. The major income sources of the community include coffee, korarima, cereal crops, fruits, other cash crops, and livestock sales.

Details are in the caption following the image — **Figure 1**
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Location map of the study area.

2.2. Methods

2.2.1. Forest Cover Data

LU/LC data were sourced from Ref. [35] (Table 1). The LU/LC data were analyzed using satellite images collected United States Geological Survey (USGS) during the dry season (December and January) for the years 1986, 2002, and 2019 and were classified using the Maximum Likelihood Classification (MLC) method. The classification process adhered to the guidelines established by Refs. [62, 63]. Seven LU/LC types were identified in the study area, as detailed in Table 1 [35].

Table 1. LU/LC class and their respective distribution during study periods (1986–2019).

LU/LC class	Distribution of LU/LC (ha (%))
LU/LC class	1986	2002	2019
Dense forest	19,171 (20%)	15,153 (15.8%)	14,618 (15.3%)
Open forest	20,058 (21%)	17,193 (18%)	16,591 (17.4%)
Shrub land	6920 (7.24%)	4217 (4.4%)	3719 (4%)
Settlement	1073 (1.12%)	1711 (1.8%)	2830 (3%)
Wetland	6039 (6.3%)	4689 (5%)	4069 (4.3%)
Cropland	15,958 (16.7%)	32,552 (34%)	22,032 (23%)
Agroforestry	26,429 (27.6%)	20,135 (21%)	31,791 (33.2%)

Note: Source [35].

The overall accuracy of the classified LU/LC maps ranges from 87.7% to 92% across the study periods with corresponding kappa statistics ranges from 0.86 to 0.91 (Table 2), which indicated reliable LU/LC classification. To assess changes in LU/LC classes over the study period from 1986 to 2019, a thematic change detection method was utilized. Additional details on the LU/LC data and methodology can be found in Ref. [35]. For the analysis of forest cover change, the classified LU/LC was reclassified using ArcGIS 10.4.1.

Table 2. Accuracy assessment results of the classified image of the study area.

LU/LC class	1986		2002		2019
LU/LC class	Users’	Producer’s	Users’	Producer’s	Users’	Producer’s
Shrubland	90	84.9	92.5	89.1	95	93.4
Wetland	92.2	90.4	98.2	90	91.7	91.7
Settlement	93	94.6	92.3	88.9	93.3	95
Open forest	82.8	82.8	79.6	82.7	86.7	89.7
Agroforestry	88.3	77.9	87	85.5	93.3	95
Dense forest	85	98.1	86.5	91.8	93.3	93.3
Cropland	83.9	88	81.4	92.3	91.7	87.3
Overall accuracy	87.7		88.1		92.1
Kappa statistics	0.86		0.86		0.91

Note: Source [35].

The results showed that agroforestry covered 27.6% of the study area in 1986, followed by open forest (21%), dense forest (20%), and cultivated land (16.7%). The coverage of settlements and wetlands is 1.12% and 6.3%, respectively. By 2002, cropland became the dominant cover type at 34.4%, while agroforestry, open forest, and dense forest occupied 21%, 18%, and 15.8% of the surveyed landscape, respectively. In 2019, agroforestry covered the majority of the surveyed area at 33.2%, while cultivated land was at 23%, while settlements, shrublands, and wetlands remained minimal [35].

2.2.2. ESV Estimation

The LU/LC data from [35] were used to evaluate the impact of changes in forest cover on the value of forest ecosystem services (FESVs) in the region, applying the benefit transfer method (BTM). We obtained the coefficients for ESVs related to the study’s LU/LC categories from empirical studies conducted by Refs. [64, 65] (Table 3). Given the variations in local and global conditions [47], a combination of global annual value coefficients [65] and locally adjusted coefficients [65, 66] for the ecosystem service function (ESVf) associated with each land cover type was utilized (Table 4). Since there is a scarcity of ESV studies focused on traditional agroforestry, we adopted the value coefficients from Ref. [65], based on the assumption that the multistrata agroforestry characteristics at the study site are comparable to those of the policy area.

Table 3. Summary of LU/LC types and their equivalent biomes with the corresponding value coefficients (US$ ha⁻¹·yr⁻¹) based on [64, 65]^a.

LU/LC types	Equivalent biome	Ecosystem service coefficient (US$ ha⁻¹·yr⁻¹)
Agroforestry	Tropical forest	^a603.30
Cropland	Cropland	225.56
Dense forest	Tropical forest	2095.25
Open forest	Tropical forest	2095.25
Shrubland	Forests	969
Wetland	Swamps/marsh	19,581
Settlement	Urban	0

^aRepresents ESV coefficients (US$ ha⁻¹ yr⁻¹) adopted from [65].

Table 4. Annual value coefficients for ecosystem service functions of each land cover type [64].

Ecosystem services	ESV (US$ ha⁻¹·yr⁻¹) of LC type
Ecosystem services	Dense forest	Open forest	Agroforestry	Shrubland	Wetland	Cropland	Settlement
Provisioning services	396	396	171.1	200	7696	187.56	0
Water supply	8	8	16.10^b	3	7600
Food production	32	32	102.80^b	43	47	187.56^a
Raw material	315	315	19.30^b	138	49
Genetic resources	41	41	32.90^b	16

Regulating services	628.68	628.68	344	330	9194	24	0
Water regulation	6	6	2.40^b	2	30
Wastewater treatment	136^a	136^a	19.60^b	87	1659
Erosion control	245	245	183.40^b	96
Climate regulation	223	223	132.30^b	141
Biological control			6.30^b	2		24
Gas regulation	13.68^a	13.68^a	14.0^b	2	265
Disturbance regulation	5	5			7240

Supporting services	956.57	956.57	67.4	371	439	14	0
Nutrient cycling	922	922	32.3^b	361
Pollination	7.27	7.27	18.7^b
Soil formation	10	10	6.5^b
Habitat/refugia	17.3^a	17.3^a	9.9^b		439		0

Cultural services	114	114	6.8^b	68	2252
Recreation	112	112	6.1^b	66	491
Cultural	2	2	0.7^b	2	1761

Total	2095.25	2095.25	603.30	969	19,581	225.56	0

^abESV coefficients (US$ ha⁻¹·yr⁻¹) adopted from [65, 66], respectively.

The ESV for each land use type, each service function, and the overall ESV were estimated by the methods used by Refs. [67, 68] as

()

where ESVk is the ESV of LU/LC type k, ESV is the total ESV, ESV_f is the value of the ESVf f, A_k is the area (ha) for LU/LC type k; VC_k is the value coefficient (US$ per hectare per year) for LU/LC category k; and VCk_f is the value coefficient (US$ per hectare per year) for land use category k with ESVf type f. The percentage changes in ESV were estimated by using the following formula [68]:

()

where ESV_i and ESV_j represent the ESV_s in the initial and final years, respectively.

Due to uncertainties in value coefficients and mismatched biomes for LU/LC types, sensitivity analyses were conducted to evaluate the changes in ESVs based on value coefficient variations [66]. A coefficient of sensitivity (CS) was calculated to validate the robustness of coefficients value adapted in ESV assessment [47] as

()

where CS is the sensitivity coefficient, ESV_i and ESV_j are the initial and adjusted total value of the estimated ecosystem service, respectively, and VC_ik and VC_jk are the initial and adjusted value coefficients (US $ ha⁻¹ yr ⁻¹), respectively, for LU/LC type “k.” In this study, CS was analyzed by adjusting the ESV value coefficients by ±50% for selected LU/LC classes. The CS > 1 indicates the estimated ESV for VC is elastic while it considered inelastic if CS < 1. CS = 1 indicates full elasticity, while CS = 0 reflects complete inelasticity [47, 69].

2.2.3. Socioeconomic Data Collection and Analysis

The socioeconomic data were integrated with geospatial information to address complex questions that geospatial data alone cannot fully resolve, such as the underlying reasons and mechanisms driving these changes [43, 70], and to conduct a comprehensive analysis of forest cover dynamics that informs conservation decisions. A mixed-methods approach, including focus group discussions, key informant interviews, and household surveys, was employed to gather community perspectives on forest cover change, its drivers, consequences, and attitudes toward conservation strategies. Furthermore, we consulted experts and reviewed the existing literature to deepen our understanding of the issue [28, 71].

A multistage sampling strategy was used to recruit study participants. Four kebeles—Woshiken, Dakin, Kusha, and Shafa Guid—were purposively selected based on forest presence, cover change, and representativeness, in consultation with district forestry experts. Villages near the forest within each kebele were then randomly selected, followed by a lottery-based selection of respondents from each village. Ultimately, 126 households were chosen from a total of 1458 household heads: Woshiken (356), Dakin (155), Kusha (455), and Shafa Guid (492) using the following formula [72]:

()

where n is the size of the sample, z is the selected critical value (1.96) of the desired confidence level (95%), p is the percentage of the population to be included in the sample (10%), q (0.9) = 1 - p, and e (5%) is the preferred precision level, and N is the sampling frame (1,458). The target was to include 10% of the sampling frame, surpassing the 5% threshold recommended for achieving a representative sample [73, 74].

After refining the questionnaire, household surveys were conducted in each kebele, following a pilot test on ten households to identify and correct any errors or ambiguities, thereby improving validity [75]. Both closed and open-ended questionnaires were designed to capture various socioeconomic variables, including age, family size, income, education, duration of residence, and farm size. The perceptions of respondents on LU/LC changes were assessed as 1 = Increased, 2 = Decreased, 3 = No Change, 4 = Don’t Know. Community attitudes toward forest conservation approaches were measured using a five-point Likert scale. Additionally, key informant interviews and focus group discussions were conducted using guiding checklists to gather historical data on forest cover changes and their driving factors and consequences. Furthermore, the population figures for 1986 and 2002 were estimated using the 1994, 2007, and 2019 data from the Central Statistical Agency (CSA) [61, 76, 77] and the exponential growth rate formula employed in prior research [78–80] as

()

where P₁ and P₂ are the population totals for two different time periods, “t” is the number of years between the two enumerations, and “r” is the average annual rate of growth.

Socioeconomic data were compiled and managed using SPSS Version 23, where the data were summarized using frequency, mean, and standard deviation statistics. To calculate the ranking of the causes of forest cover change as perceived by respondents, a weighted average approach was employed [28, 79] as

()

where R_n is the value for the lowest-ranked level (e.g., if the lowest rank is 8th, then R_n is 8, R_n−1 is 7, R₁ is 1) and C_n is the counts of lowest-ranked levels (e.g., in the above, the count of eighth ranks is C_n and first rank is C₁).

A binary logistic regression analysis was conducted to identify socioeconomic factors influencing respondents’ perceptions of LU/LC changes. The dependent variable, farmers’ perception of a specific driver, was coded as “perceived” or “not perceived.” Independent variables included age, sex, education, residence duration, landholding size, income, and family size. The logistic model is indicated as

()

where Y is the dependent variable indicating the probability that Y = 1(perceived), β₀ is the intercept, and β₁…β_n are coefficients of the independent variables X₁, X₂, …X_n. Prior to analysis, we checked for multicollinearity among the independent variables in SPSS using a collinearity diagnostics index to ensure the validity of our results [75, 78].

Farmers’ attitudes were assessed using a ranking method adapted from Ref. [81], where ratings on a Likert scale were aggregated into mean scores for each respondent. These scores were then categorized into four groups: “very high,” “high,” “medium,” and “low.” This classification helps forest conservation decision-makers prioritize effective strategies based on public perspectives.

Very High = Weighted Mean + SD ≤ A ≤ Max.; High = Weighted Mean ≤ B < Mean + SD; Moderate = Weighted Mean − SD ≤ C < weighted mean, and Low = Min. ≤ D < weighted mean − SD,

where A, B, C, and D are grades (means of the respondents) assigned, and Max. and Min. represents the maximum (5) and minimum (1) Likert scale value, respectively. Spearman’s correlation analysis was conducted to assess the relationship between participants’ perceptions of change drivers and their attitudes toward conservation measures.

3. Result and Discussion

3.1. Socioeconomic Characteristics of Respondents

The survey result indicated that the majority of participants (80.2%) were male. The average age of the household was 48.2 years. All participants were married and had an average family size of around six people (Table 5). Approximately 51% of the participants were illiterate, while 49.2% attended primary school or higher. The average landholding size of the participants was 2.76 ha. The average annual income of the participants was 18,492 ETB. Participants had been residing in the area for an average of 43.25 years.

Table 5. Socioeconomic characteristics and descriptive results of sample household.

Household attributes	Value
Gender (male, %)	80.2
Average household age (year)	48.2 (±10.95)
Marital status (married, %)	100
Average family size (number)	6.44 (±2.27)
Education background (illiterate, %)	50.8
Average land holding size (ha)	2.76 (±2.1)
Had enough for agriculture (yes, %)	42.9
Mean household income (birr ^∗/year)	18,492 (±13,470)
Average length of duration of local residence (year)	43.25 (±13.21)
Visit forest to get benefits from forest (yes, %)	88.9

^∗Ethiopian currency: at the time of the study, 1 USD = 32.65ETB.

3.2. Perception of Community Toward Forest Cover Change

The survey results (Figure 2) showed that a significant majority of respondents observed decreases in forest (87.3%) and wetland (94.4%) areas in the study region. In contrast, increases were reported in settlement areas, cropland, and agroforestry land use, with 100%, 81.8%, and 79.4% of participants acknowledging these changes, respectively. These findings are consistent with the LU/LC changes reported by Ref. [35] (Table 1), showing a decline of 23.75% in dense forests, 17.29% in open forest, 46.27% in shrubs, and 32.63% in wetlands, alongside increases of 163.82% in settlements, 38.06% in cropland, and 20.29% in agroforestry over the past 3 decades. This shift is attributed to the growing demand for agricultural production, settlement expansion, and forest products, leading to loss and fragmentation forest blocks in the study area (Figure 3).

Focus group discussions and key informants confirmed a significant decrease in forest cover in the research area, primarily due to land clearing for agriculture, settlements, eucalyptus plantations, and cash crops like coffee and khat (Figure 4). LU/LC analysis revealed substantial conversion of forested areas (Figure 5), with 88% of shrublands, 61% of open forests, and 42% of dense forests transformed into other land types, leading to an annual loss of 340 ha of forest cover [35].

Additionally, studies indicate a significant decline in forest cover in southwestern Ethiopia. One study found a 19.6% reduction in natural forest cover, while coffee plantations increased by 10.2% [57]. Another study noted an 88% rise in agricultural land and a 157% increase in settlements, resulting in a 30.5% decrease in forest cover within the Kaffa biosphere reserve [29]. Furthermore, Ref. [82] identified a 25.76% drop in natural forest cover, alongside an 18.35% increase in nonforest areas in the region.

The decline in forest cover is largely due to human activities, particularly policy changes in agriculture and land use, as well as population growth. Post-1990 agricultural development policies have intensified deforestation, especially in the southwest [83]. Focus group discussions and key informant interviews indicated that a government-sponsored resettlement program from 1985 to 1988 was also a significant factor. They also indicated that changes in land policies during the Derg regime (1974–1991) weakened customary laws and traditional practices, further driving forest degradation. The banning of cultural practices and the persecution of community leaders damaged the connection between local communities and the forests, eroding traditional ownership norms.

Studies in Ethiopia support this research, showing that the abolition of land rights in the 1970s led to legal pluralism in nationalized forest areas, significantly contributing to the destruction of moist evergreen Montane forests during the Derg regime [84]. Furthermore, it was found that state forest policies’ failure to recognize traditional institutions has resulted in illegal exploitation and deforestation [85]. Additionally, the Derg regime’s land policies undermined conservation efforts and disrupted traditional land management practices, worsening the issue [86].

Focus group participants linked the resettlement program to widespread deforestation, highlighting the study area’s role as a major hub for Derg’s initiatives. Another study confirmed that resettlement programs adversely impacted forest cover in Ethiopia [23, 87]. Key informants noted accelerated deforestation during the Ethiopian People’s Revolutionary Democratic Front (EPRDF) regime (1991–2018), attributing this to inadequate government control over forest management and policies promoting intensive agriculture and coffee production. Reports associated with this indicate more than 10% increase in plantations of coffee and/or tea [56, 82] over the past 3 decades in southwest Ethiopia, significantly contributing to the decline in forest cover in addition to other factors.

3.3. Perception of Community on Drivers of Forest Cover Change

The research identified 15 key factors driving deforestation in the region (Table 6), with agricultural expansion (100%), population growth (100%), land grabbing (99.2%), fuelwood collection (95.2%), and illegal logging (99.2%) ranked as the most significant. Other contributors include the lack of community involvement, weak law enforcement, corruption, and human settlement. Key informants noted that local corruption hindered forest regulation enforcement and community engagement in conservation. Significant differences in perceptions of drivers of forest cover changes were found among residents, particularly regarding settlement, law enforcement, government policies, and loss of traditional conservation practices.

Table 6. Drivers of forest cover changes ranking.

Perceived causes of forest cover loss	Response by kebele (yes, %)				Overall	χ²	Rank
Perceived causes of forest cover loss	Shafa Guid	Woshiken	Dakin	Kusha	Overall	χ²	Rank
Fuel wood collection	100	80.6	100	100	95.2		4
Agriculture expansion	100	100	100	100	100		1
Population growth	100	100	100	100	100		2
Forest land grabbing	97.67	100	100	100	99.2		3
Settlement	74.4	83.9	84.6	56.4	72.2	^∗	10
Low forest law enforcement	76.7	87.1	100	66.7	78.6	^∗	8
Illegal logging	90.7	100	100	92.3	94.4		5
Charcoal making	93	80.6	92.3	89.7	88.9		6
Government policy	58.1	83.9	92.3	46.2	64.3	^∗∗∗	11
Corruption	76.7	61.3	84.6	31.5	73.02		9
Free grazing	41.9	41.9	46.2	17.9	34.9		15
Farmland degradation	81.4	67.7	92.3	71.8	76.2		12
Increasing wood processing industry	55.8	71	92.3	59	64.3		13
Low community participation	93	90.3	100	94.9	93.7		7
Loss of traditional forest conservation	58.1	90.3	100	20.5	58.7	^∗∗∗	14

^∗∗∗and ^∗indicate significance at p < 0.001 and p < 0.05, respectively, depicting that the location of respondents had a significant effect on the perception of farmers toward the drivers, N = 126.

Expert feedback and observations indicate a trend of converting forests into agroforestry and settlements, driven by declining arable land productivity and soil issues like acidity and reduced fertility. Deforestation, especially on steep slopes, has increased due to land degradation and rising costs of fertilizers, resulting in nutrient depletion in cropland [35].

Analysis of population trends in the study area reveals an increase from 95,129 in 1986 to 288,391 in 2019, a growth of 203.16% over 33 years (Figure 6). This population surge is expected to elevate demand for agricultural land, settlement area, biomass energy, and construction materials, impacting forest ecosystems.

Research indicates that illegal logging, corruption, and land grabbing significantly contribute to deforestation in Gambella, Ethiopia [88]. Corruption undermines law enforcement and forest management efforts [89, 90]. Various studies link agricultural expansion driven by population growth to changes in forest cover [85, 90–94].

Experts note that the illicit timber trade and government backing of small-scale wood processing to combat urban unemployment have exacerbated deforestation, threatening species like Cordia africana and Pouteria altissima. Reports from Bench-Sheko Zonal Enterprise and Industry Development Office (2020) show licensed timber processing enterprises grew from 2 in 2008 to 129 in 2019, a staggering 6350% increase, highlighting the adverse effects of employment-driven policies on forest resources.

3.4. Socioeconomic Factors Influencing of Forest Cover Change Drivers

The study analyzed the influence of socioeconomic variables on local communities’ perceptions of the drivers of forest cover change. A logistic regression analysis revealed that six variables—gender, age, education level, income, landholding size, and length of residency—significantly impacted (p < 0.05) respondents’ perceptions (Table 7).

Table 7. Logistic regression results with significant socioeconomic factors affecting respondents’ perception of driving forest cover change.

Drivers	Independent variables	β	S.E.	Wald	Df`	Sig.	Exp (β)
Charcoal making	Landholding size	1.34 ^∗∗∗	0.35	14.88	1	0.00	3.82
	Income	−0.002 ^∗	0.001	3.5	1	0.04	0.998
	Constant	−5.61 ^∗	2.58	4.74	1	0.03	0.004
Farmland degradation	Sex (1)	1.59 ^∗	0.87	3.34	1	0.04	4.89
Farmland degradation	Landholding size	0.78 ^∗∗∗	0.2	15.58	1	0.00	2.18

Firewood collection	Landholding size	0.95 ^∗	0.42	5.05	1	0.02	2.58

Settlement	Age	−0.05 ^∗	0.04	1.28	1	0.04	0.96
	Lived in the village	0.07 ^∗	0.03	3.83	1	0.04	1.07
	Constant	−2.76 ^∗	1.24	4.96	1	0.02	0.06

Change in government policy	Sex (1)	1.32 ^∗	0.6	4.93	1	0.02	3.77
Change in government policy	Constant	−2.21 ^∗	1.17	3.61	1	0.03	0.11

Corruption	Sex(1)	−0.18 ^∗	0.07	6.69	1	0.01	0.83
	Education			5.53	2	0.06
	Education (1)	1.05 ^∗	0.47	5.06	1	0.02	2.85

Wood processing industry	Landholding size	0.33 ^∗	0.14	5.55	1	0.01	1.39

Loss of indigenous forest management	Age	0.22 ^∗	0.11	4.21	1	0.04	1.244
	Sex (1)	−1.13 ^∗	0.52	4.65	1	0.03	0.324
	Constant	−2.83 ^∗	1.24	5.22	1	0.02	0.059

Note: Number of observation = 126; R² is Negelkerke’s R².
^∗p < 0.05.
^∗∗∗p < 0.001.

The gender of respondents significantly affected their perceptions of farmland degradation, government policy change, corruption, and the loss of indigenous forest management systems as drivers of forest cover change. For instance, male respondents were more likely than female respondents to perceive farmland degradation and government policy changes as contributing to forest cover changes.

Age was found to positively and significantly influence perceptions of the loss of indigenous forest management systems and settlement as drivers of forest cover changes, indicating that older respondents were more likely to recognize these factors. The size of landholdings significantly impacted perceptions of charcoal making, farmland degradation, and firewood collection as drivers of forest cover changes, with larger landholders more likely to view these activities as contributing to deforestation.

Additionally, income and length of residency in the study village influenced perceptions of charcoal making and settlement. Education also played a role in shaping perceptions of corruption and the loss of traditional forest management practices as drivers of forest cover change.

The study’s findings align with previous research highlighting the importance of socioeconomic factors in shaping local communities’ perceptions of forest cover changes [75, 78, 95, 96]. It emphasizes the need for tailored conservation strategies that address the unique needs of local communities. The results suggest that policies and initiatives should prioritize socioeconomic factors that influence how communities perceive and respond to changes in forest cover. This study underscores the significance of considering socioeconomic factors in community-based conservation efforts.

3.5. Change in Forest ESVs

The study estimated the ESV for 1986, 2002, and 2019, showing totals of approximately US$226.75 million, US$183.16 million, and US$172.81 million, respectively (Table 8). In 1986, wetlands (52.15%) were the largest contributors followed by open forests (18.54%), while croplands accounted for only 1.59%. By 2002, wetlands still led at 50.13%, followed by open forests (19.67%) and dense forests (17.33%). In 2019, wetlands (46.1%), open forests (20.11%), and agroforestry (11.1%) remained significant contributors.

Table 8. Estimated ESV and change for each LU/LC type during the study periods in million US$ per year of the study area.

LU/LC type	ESV(US$ in 10⁶)						ESV changes (%)
	1986		2002		2019		2002–1986	2019–2002	2019–1986
	US$	%	US$	%	US$	%	2002–1986	2019–2002	2019–1986
Dense forest	40.18	17.72	31.75	17.33	30.63	17.72	−20.98	−3.53	−23.77
Open forest	42.03	18.54	36.02	19.67	34.76	20.11	−14.29	−3.5	−17.29
Shrubland	6.71	2.96	4.09	2.23	3.6	2.08	−39.05	−11.98	−46.35
Agroforestry	15.98	7.05	12.15	6.63	19.18	11.10	−23.97	+57.86	+20.03
Wetland	118.25	52.15	91.81	50.13	79.67	46.10	−22.36	−13.22	−32.63
Cropland	3.6	1.59	7.34	4.01	4.97	2.88	+103.89	−32.29	+38.06
Settlement	0	0	0	0	0	0	0	0	0
Total	226.75	100	183.16	100	172.81	100	−19.22	−5.65	−23.79

The main ESVf in the study area were water supply, disturbance regulation, and nutrient cycling, contributing 20.58%, 19.37%, and 17.43% of total ESVf in 1986, respectively. In 2002, these figures were 19.78%, 18.63%, and 17.47%. By 2019, water supply accounted for 18.34% of the total ESV, followed closely by nutrient cycling at 18.02% and disturbance regulation at 17.14% (Table 9). The CS remains consistently below 1, indicating that the estimate of total ESV is relatively inelastic to the coefficient values, thus validating the robustness of our assessment (Table 10).

Table 9. Estimated ecosystem services of individual ecosystem services function and its change over the study periods (1986–2019).

ESV_f	ESV_f (US$ in 10⁶)			Change in ESV_f (%)
ESV_f	1986	2002	2019	2002–1986	2019–2002	2019–1986
Provisioning services	70.92 (31.28%)	59.29 (32.37%)	53.98 (31.24%)	−15.43	−8.96	−23.20
Water supply	46.66 (20.58%)	36.23 (19.78%)	31.69 (18.34%)	−22.35	−12.52	−32.07
Food production	7.55 (3.33%)	9.61 (5.25%)	8.75 (5.06%)	+27.26	−8.97	+15.85
Raw material	14.12 (6.23%)	11.39 (6.22%)	11.16 (6.46%)	−19.34	−2.04	−20.98
Genetic resources	2.59 (1.14%)	2.06 (1.12%)	2.38 (1.38%)	−20.64	+16	−7.94
Regulating services	92.33 (40.72%)	72.84 (39.76%)	70.16 (40.6%)	−21.11	−3.68	−24.01
Water regulation	0.49 (0.22)	0.39 (0.21%)	0.39 (0.23%)	−20.75	0.4	−20.43
Wastewater treatment	16.48 (7.27%)	12.94 (7.06%)	11.94 (6.9%)	−21.46	−7.71	−27.52
Erosion control	15.13 (6.67%)	12.02 (6.56%)	13.83 (8%)	−20.56	+15.07	−8.59
Climate regulation	13.23 (5.84%)	10.47 (5.72%)	11.69 (6.77%)	−20.84	+11.63	−11.63
Biological control	0.56 (0.25%)	0.92 (0.5%)	0.74 (0.43%)	+62.58	−19.64	+30.64
Gas regulation	2.52 (1.11%)	1.98 (1.08%)	1.96 (1.13%)	−21.67	−0.89	−22.37
Disturbance regulation	43.92 (19.37%)	34.12 (18.63%)	29.61 (17.14%)	−22.34	−13.18	−32.57
Supporting services	44.74 (19.73%)	36.39 (19.86%)	35.47 (20.53%)	−18.66	−2.53	−20.72
Nutrient cycling	39.52 (17.43%)	32 (17.47%)	31.14 (18.02%)	−19.05	−2.66	−21.2
Pollination	1 (0.44%)	1.07 (0.58%)	1.13 (0.65%)	6.31	+5.85	+12.53
Soil formation	0.63 (0.28%)	0.5 (0.27%)	0.56 (0.32%)	−21.65	+11.97	−12.27
Habitat/refugia	3.59 (1.58%)	2.82 (1.52%)	2.64 (1.56%)	−21.57	−6.26	−26.48
Cultural services	18.73 (8.26%)	14.67 (8.01%)	13.19 (7.63%)	−21.68	−10.84	−29.58
Recreation	7.98 (3.52%)	6.33 (3.46%)	5.93 (3.43%)	−20.7	−6.22	−25.63
Cultural	10.75 (4.74%)	8.34 (4.55%)	7.26 (4.2%)	−22.35	−13.03	−32.47

Note: (+) and (−) indicate gain and loss, respectively.

Table 10. Percentage change in the estimated total ESV and coefficient of sensitivity resulting from a 50% adjustment in the ecosystem valuation coefficients (VCs).

Change in valuation coefficient (VC) (%)	Effect of changing VC from the original value
	1986		2002		2019
	%	CS	%	CS	%	CS
Dense forest VC ± 50	±8.86	±0.18	±8.67	±0.17	±8.86	±0.18
Open forest VC ± 50	±9.27	±0.19	±9.83	±0.20	±10.6	±0.20
Shrubland VC ± 50	±3.41	±0.07	±1.12	±0.02	±1.04	±0.02
Agroforestry VC ± 50	±3.52	±0.07	±3.32	±0.07	±5.56	±0.11
Wetland VC ± 50	±26.07	±0.52	±25.06	±0.5	±23.05	±0.46
Cropland VC ± 50	±0.79	±0.02	±2.0	±0.04	±1.14	±0.03

Approximately 88.9% of respondents reported visiting forests for various provisioning ecosystem services at least once per month, indicating that the majority of the community relies on forest resources for social and economic purposes (Table 3). Key informants and focus group discussions confirmed the diverse value of forests, including their historical significance as hunting grounds and sites for rituals believed to bring blessings to community well-being. Previous research highlights the diverse benefits that forests provide to community livelihoods, including essential direct products and environmental services that support well-being and income [95, 97–100].

However, the study revealed a significant decline in the ESV over the analyzed periods, with an overall loss of about 23.79% from 1986 to 2019. This decrease in the ESV is primarily linked to the degradation of wetlands and forests, largely driven by their conversion into cropland, agroforestry, and settlement areas. Different LU/LC experienced varied changes in the ESV: Shrublands showed a decline of 46.35%, wetlands decreased by 32.63%, dense forests dropped by 23.77%, and open forests declined by 17.29%. In contrast, cropland and agroforestry experienced increases of 38.06% and 20.03%, respectively. Despite the growing contributions from agroforestry and cropland, their overall effect on ESV remains minor compared to the substantial losses in natural ecosystems. These findings indicate that the continued degradation of wetlands and forests could adversely impact environmental health and local livelihoods. The decline in the ESV as a result of forest cover decline has also reported in other studies [66, 67, 101–103].

Similarly, the forest cover change over the last 33 years has resulted in a reduction of most ESVfs, except for biological control, food production, and pollination, which rose by 30.64%, 15.85%, and 12.53%, respectively. These increases are mainly associated with increasing agroforestry land and cropland uses. The most impacted services were disturbance regulation (32.57%), cultural service (32.47%), water supply (32.07%), wastewater treatment (27.5%), habitat/refugia (26.48%), recreation (25.63%), gas regulation (21%), and raw materials (21%). A study reported by [67] also demonstrated that significant losses have occurred in essential services such as erosion control and wastewater treatment in the Munessa-Shashemene landscape. Overall, cultural, regulatory, provisioning, and supporting services declined by 29.58%, 24.01%, 23.2%, and 20.72%, respectively. This decline may threaten water supply, shelter, and economic stability for communities dependent on agriculture and forestry, while a loss in regulatory services can lead to economic losses, food insecurity, health risks, and increased vulnerability to extreme weather.

This study is supported by earlier research indicating that deforestation negatively affects ecosystem services [92, 100, 104]. The loss of forest cover exacerbates flooding and disrupts the water cycle [89, 105–107], potentially leading to reduced rainfall and droughts [108], increasing sediment runoff [36], impacting hydroelectric operations [109], and degrading water quality. The shift from forest land to simplified land uses such as agroforestry and cropland can adversely affect native woody species and diminish ESVs [46]. These changes in forest cover can generally threaten local climates, resource availability, and agricultural productivity, resulting in economic losses and poorer living conditions.

3.6. Community Opinions on Forest Conservation

Incorporating community insights is vital for effective forest conservation. Over the past 33 years, the study area has seen various forest management systems. The Derg regime (1974–1991) used a top-down strategy focused on afforestation and reforestation, which reduced deforestation but lacked community engagement, leading to later deforestation issues. Research confirms this trend, showing improved forest cover in northern Ethiopia during that time [110].

Since the EPRDF assumed power in 1991, efforts have aimed to transition from state-controlled to community forest management (CFM) to enhance conservation and engage local communities and nongovernmental organizations (NGOs). CFM has been implemented by various NGOs in the study region [111], and reports from Zonal Agriculture offices suggest that the majority of natural forests are now being incorporated into PFM, with the support of the REDD + project. Besides, forest conservation initiatives included demarcating forest boundaries and relocating illegal settlers from steep forested areas to fertile lowland. However, these attempts have largely been ineffective due to insufficient community involvement and weak law enforcement.

The analysis of attitude data (Table 11) reveals ranges of opinions on forest conservation options, with a mean score of 4.08 indicating overall positive attitudes. Households showed strong support for various initiatives, scoring highest for afforestation (4.52), strengthening enforcement (4.45), enhancing environmental education (4.37), CFM (4.30), improving agricultural productivity (4.26), and agroforestry practices (4.25). In contrast, there was less support for relocating illegal settlers (3.78), redrawing forest boundaries (3.38), and regulating illegal commercialization (3.67). Communities may resist relocating settlers due to concerns about livelihood loss and potential conflicts, as these individuals have established ties in the area [112]. Similarly, fears of losing access to essential resources and a sense of identity may lead to opposition against boundary changes, perceived as an external imposition [113].

Table 11. Respondents’ attitudes toward possible solutions for forest conservation as perceived by respondents.

Attitude statements	Mean ± std. error
1. Afforestation and reforestation efforts can significantly increase the diversity and coverage of forest resources	4.52 ± 0.046
2. Strengthening forest law enforcement can help reduce deforestation and forest land grabs	4.45 ± 0.059
3. Agroforestry practices can help reduce deforestation and forest degradation	4.25 ± 0.074
4. Community involvement in forest management can improve the sense of ownership and promote sustainable conservation practices	4.01 ± 0.061
5. Paying for environmental services can be an effective way to encourage the preservation of forests	3.98 ± 0.073
6. Reducing corruption in forest management can help promote sustainable forest conservation practices	4.17 ± 0.054
7. Improving environmental education can help promote sustainable forest management practices	4.37 ± 0.46
8. Improving agriculture land productivity can help reduce forest loss by promoting more efficient use of land	4.26 ± 0.078
9. Removing unlawful settlers from forest areas can help restore forest resources	3.78 ± 0.092
10. CFM can ensure sustainable forest use and thereby prevent deforestation	4.3 ± 0.057
11. Regulating the illegal commercialization of forest products can help curb deforestation	3.67 ± 0.095
12. Upholding and redrawing forest boundaries can help restore degraded forest diversity and forest coverage	3.38 ± 0.096
13. Implementing zero grazing practices can help reduce deforestation and promote sustainable land use	3.96 ± 0.059

Addressing deforestation requires a comprehensive approach focusing on sustainable land use [114, 115]. Improving crop yields through modern techniques—such as better seeds, efficient irrigation, soil amendments, and agroforestry—can reduce the pressure to expand farmland into forested areas. Research shows that intensive agriculture and precision farming can reduce deforestation rates [116]. Studies indicate that higher agricultural productivity reduces deforestation in Africa [117]. Improved cropland management and sustainable practices, such as crop rotation, further enhance productivity and mitigate deforestation [118, 119]. Additionally, landscape restoration boosts smallholder productivity and provides alternative livelihoods to combat deforestation [51, 120]. However, the adoption of sustainable land management in Ethiopia faces challenges related to policy, governance, financial constraints, and socioeconomic factors [121, 122].

The attitude toward the adoption of agroforestry as strategy was supported by LU/LC data, which indicated an increase in agroforestry LU/LC types during the study period. Land users in the area converted unproductive land, affected by soil acidity and fertility loss, into woodlots like eucalyptus for economic reasons [35]. Adopting agroforestry and tree planting alleviates pressure on forests by providing alternatives for timber, fodder, and fuelwood [115, 123, 124]. Studies also support the crucial role of environmental education in tackling forest degradation and promoting sustainability by empowering individuals, thereby fostering awareness and engagement in local ecological issues [125, 126]. Effective law enforcement is also essential to combat deforestation by addressing illegal activities. Research indicates that strong forest law enforcement policies significantly diminish deforestation [127].

The study revealed community support for CFM initiatives, which have the potential to reduce forest depletion [51, 115, 116. However, in Ethiopia, CFM is primarily driven by NGOs and not well integrated into government forest management frameworks [53]. Key challenges include ineffective benefit-sharing, local authority interference, unclear ownership, low participation, and weak institutions [53, 115]. Informants noted that NGO-led CFM initiatives often lack sustainability postproject due to weak community institutional foundations and a growing preference for individual ownership among PFM members.

Correlation analysis shows that various forest conservation strategies are linked to forest cover change (Table 12). Strengthening forest law enforcement correlates negatively with land grabbing, illegal logging, settlements, and corruption, highlighting its role in promoting compliance with environmental laws. Increasing agricultural productivity also correlates negatively with illegal activities and underscores the importance of resource efficiency in mitigating deforestation. Community participation significantly reduces agricultural expansion, fuelwood collection, and corruption, indicating that empowering locals helps combat deforestation. Additionally, reducing corruption is negatively related to land grabbing and settlements, while positively linked to government policy changes. Strategies that enhance environmental education show negative correlations with agricultural degradation, charcoal production, illegal settlements, and weak enforcement, suggesting that increased awareness fosters sustainable practices. These findings highlight the interconnectedness of conservation strategies and deforestation drivers, supporting the effectiveness of integrated approaches to sustainable forest management.

Table 12. Spearman’s correlation analysis of drivers of deforestation and forest conservation strategies.

Conservation option	Drivers of deforestation
Conservation option	Agri exp	Land grab	Fuel coll	Illeg log	Land degrd.	Char mak	Pop groz	Settle	Free graz	Inc wood	Low com	Corrup	Gov pol	Loss tra	Low for
Strengthen forest law enforcement	−0.173	−0.391 ^∗∗	−0.173	−0.218 ^∗	0.08	−0.056	0.08	−0.218 ^∗	0.044	0.013	0.142	−0.434 ^∗∗	−0.033	0.145	−0.038
Remove illegal settlers	0.095	0.076	0.095	−0.134	−0.05	−0.133	0.095	0.083	−0.084	0.144	0.172	−0.156	0.246 ^∗∗	0.025	0.06
Increase agriculture land productivity	−0.058	−0.143	−0.058	−0.188 ^∗	−0.09	−0.047	0.09	−0.234 ^∗∗	0.053	−0.123	−0.035	−0.179 ^∗	0.014	0.056	−0.180 ^∗
Community participation	−0.192 ^∗	0.138	−0.192 ^∗	0.022	0.008	0.012	0.184 ^∗	−0.082	−0.052	−0.012	−0.143	−0.232 ^∗∗	−0.108	0.181 ^∗	−0.114
Corruption reduction	−0.163	−0.293 ^∗∗	−0.163	0.041	−0.032	−0.139	−0.032	−0.212 ^∗	0.064	0.025	0.039	0.021	0.273 ^∗∗	0.207 ^∗	0.054
Control grazing	0.00	0.06	0.00	0.00	0.00	−0.107	0.00	0.124	0.056	−0.004	−0.060	−0.068	0.032	−0.001	0.009
Payment for ecosystem service	0.015	0.015	0.015	0.159	0.145	−0.009	−0.157	0.137	0.027	0.009	0.139	0.320 ^∗∗	−0.014	−0.018	−0.105
Agroforestry expansion	−0.115	0.038	−0.115	−0.051	0.006	0.106	−0.165	0.033	0.043	−0.012	0.030	0.174	0.153	0.041	0.056
Community-based forest management	−0.056	−0.12	−0.056	0.104	−0.056	0.038	0.109	0.004	−0.033	−0.206 ^∗	0.077	0.06	−0.049	−0.108	0.015
Redrawing forest border	0.054	0.324 ^∗∗	0.054	−0.093	−0.058	−0.082	−0.058	0.114	0.295 ^∗∗	0.266 ^∗∗	−0.098	0.038	0.063	0.165	0.099
Environmental education	−0.068	0.092	−0.068	0.024	−0.178 ^∗	−0.175	−0.068	−0.126	−0.011	−0.002	−0.002	0.089	0.029	0.054	−0.125
Control illegal forest product marketing	0.107	−0.297 ^∗∗	0.107	−0.266 ^∗∗	−0.091	−0.13	−0.004	−0.127	−0.073	−0.230 ^∗∗	0.110	−0.127	0.08	−0.141	−0.045
Afforestation and reforestation	−0.191 ^∗	0.196 ^∗	0.113	−0.127	0.332 ^∗∗	−0.679 ^∗∗	0.061	0.055	0.093	0.038	−0.033	0.066	−0.087	0.023	−0.068

Note: Fuel coll, fuel wood collection; land degrd, farmland degradation; land grab, forest land grabbing; low for, low forest law enforcement; inc wood, increasing wood processing industry; loss tra, loss of traditional forest conservation; low com, low community participation.
Abbreviations: Agri Exp, agriculture expansion; Char mak, charcoal making; Corrup, corruption; Free graz, free grazing; Gov pol, government policy; Illeg log, illegal logging; Pop groz, population growth; Settle, settlement.
^∗p < 0.05.
^∗∗p < 0.001.

The attitude survey reveals that 59.5% of respondents strongly support forest conservation, while 40.5% show moderate to low support (Figure 7). This hesitance may arise from concerns about potential livelihood loss due to conservation measures such as relocation, boundary adjustments, and regulations on illegal commercialization, which could affect communities dependent on forest resources for their income and livelihoods. To build stronger support for conservation initiatives, it is crucial to address these concerns through inclusive dialog, involve affected communities in decision-making, and offer alternative livelihoods or compensation for those facing relocation.

The study highlights the importance of community involvement in forest conservation. While top-down approaches have been ineffective, CFM initiatives have shown promise but are often driven by NGOs and lack sustainability. Addressing deforestation requires a comprehensive approach focusing on sustainable land use, including improving agricultural productivity, promoting agroforestry, and strengthening law enforcement.

4. Conclusion

Analyzing changes in forest ecosystems—focusing on patterns, causes, consequences, and conservation options—is crucial for understanding their dynamics and formulating effective conservation strategies. The study shows a significant decline in moist evergreen Afromontane forests, largely due to conversion to other land types, driven by 15 key factors. The top five drivers include agricultural expansion, population growth, land grabbing, fuelwood collection, and illegal logging. Perceived drivers of LU/LC changes varied among respondents from different kebeles, with notable influences from settlement, weak law enforcement, government policies, and the loss of traditional conservation methods. Socioeconomic factors such as sex, age, education, income, landholding size, and residency duration also shaped perceptions of forest cover changes. The ESV decreased from approximately US$226.75 million in 1986 to US$172.81 million in 2019, reflecting a 23.79% loss, primarily due to wetland and deforestation and forest degradation. Addressing forest cover changes and ecosystem service loss requires community insights and engagement. Historical management analyses demonstrate that top-down approaches often fail without local involvement, resulting in ongoing deforestation. While CFM shows promise but are often driven by NGOs and lack sustainability. The correlation analysis reveals that various forest conservation strategies are negatively correlated with deforestation drivers, indicating that integrated approaches can effectively mitigate deforestation and promote sustainable resource management. The study revealed that conservation strategies must address both ecological goals and the immediate socioeconomic needs of forest-dependent communities to gain their full support and involvement. To achieve sustainable conservation, enhancing community engagement and governance, along with a comprehensive policy and management plan targeting these areas, is essential for combating forest cover changes and their impacts on ecosystem services in the region.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

No funding was received for this research.

Open Research

Data Availability Statement

The data are included within the manuscript.

References

1 Fao and Unep, The State of the World’s Forests 2020, Forests, biodiversity and people. Rome. (2020) .
Google Scholar
2 Singh M., Shahina N., Das S. et al., P. Panwar, G. Shukla, J. A. Bhat, and S. Chakravarty, Forest Resources of the World: Present Status and Future Prospects, Land Degradation Neutrality: Achieving SDG 15 by Forest Management, 2022, Springer Nature, Singapore, 1–24.
10.1007/978-981-19-5478-8_1
Google Scholar
3 Amenu B. T., Review: Forest Management and Conservation Practices in Ethiopia: Opportunities and Constraints, Asian J For. (2017) 1, no. 2, 77–82, https://doi.org/10.13057/asianjfor/r010204.
10.13057/asianjfor/r010204
Google Scholar
4 Nerfa L., Rhemtulla J. M., and Zerriffi H., Forest Dependence Is More Than Forest Income: Development of a New Index of Forest Product Collection and Livelihood Resources, World Development. (2020) 125, https://doi.org/10.1016/j.worlddev.2019.104689.
10.1016/j.worlddev.2019.104689
Web of Science® Google Scholar
5 Raj A., Jhariya M., Khan N., Nema S., Devi A., and Ghanshyam, P. Panwar, G. Shukla, J. Bhat, and S. Chakravarty, Effect of Deforestation and Forest Fragmentation on Ecosystem Services, Land Degradation Neutrality: Achieving SDG 15 by Forest Management, 2022, Springer, Nature Singapore, 25–41.
10.1007/978-981-19-5478-8_2
Google Scholar
6 Hui D., Deng Q., Tian H., and Luo Y., Climate Change and Carbon Sequestration in Forest Ecosystems, Handbook of climate change mitigation and adaptation. (2017) 555.
10.1007/978-3-319-14409-2_13
Google Scholar
7 Nunes L. J., Meireles C. I., Pinto Gomes C. J., and Almeida Ribeiro N. M., Forest Contribution to Climate Change Mitigation: Management Oriented to Carbon Capture and Storage, Climate. (2020) 8, no. 2, https://doi.org/10.3390/cli8020021.
10.3390/cli8020021
PubMed Web of Science® Google Scholar
8 Fao, Iufro, and Usda, A Guide to Forest-Water Management, 2021, FAO Forestry, Rome.
Google Scholar
9 Tsai Y. W., Lin J. Y., and Chen Y. C., Establishment of the Watershed Health Indicators and Health Check of Reservoirs, Ecological Indicators. (2021) 127, https://doi.org/10.1016/j.ecolind.2021.107779.
10.1016/j.ecolind.2021.107779
Web of Science® Google Scholar
10 Sumalatha I. P., Sugunan R., Thethi H. P., Pratap B., Fallah M. H., and Maan P., Integrating Forest Management and Watershed Health for Sustainable Water Sources, E3S Web of Conferences 529, 2024, EDP Sciences.
10.1051/e3sconf/202452903015
Google Scholar
11 Costanza R., Groot R., Sutton P. et al., Changes in the Global Value of Ecosystem Services, Global Environmental Change. (2014) 26, 152–158.
10.1016/j.gloenvcha.2014.04.002
Web of Science® Google Scholar
12 Estoque R., Dasgupta R., Winkler K. et al., Spatiotemporal Pattern of Global Forest Change over the Past 60 Years and the Forest Transition Theory, Environmental Research Letters. (2022) 17, no. 8, https://doi.org/10.1088/1748-9326/ac7df5.
10.1088/1748-9326/ac7df5
Web of Science® Google Scholar
13 Jayathilake H., Prescott G., Carrasco L., Rao M., and Symes W., Drivers of Deforestation and Degradation for 28 Tropical Conservation Landscapes, Ambio. (2021) 50, no. 1, 215–228, https://doi.org/10.1007/s13280-020-01325-9.
10.1007/s13280-020-01325-9
PubMed Web of Science® Google Scholar
14 Ango T., Hylander K., and Börjeson L., Processes of Forest Cover Change since 1958 in the Coffee-Producing Areas of Southwest Ethiopia, Land. (2020) 9, no. 8, https://doi.org/10.3390/land9080278.
10.3390/land9080278
Web of Science® Google Scholar
15 Kissinger G., Herold M., and De Sy V., Drivers of Deforestation and Forest Degradation: A Synthesis Report for REDD+ Policymakers, 2012, Lexeme Consulting, Vancouver, Canada.
Google Scholar
16 Carter S., Herold M., Avitabile V. et al., Agriculture-driven Deforestation in the Tropics from 1990–2015: Emissions, Trends and Uncertainties, Environmental Research Letters. (2018) 13, no. 1, https://doi.org/10.1088/1748-9326/aa9ea4, 2-s2.0-85041125308.
10.1088/1748-9326/aa9ea4
Web of Science® Google Scholar
17 Curtis P. G., Slay C. M., Harris N. L., Tyukavina A., and Hansen M. C., Classifying Drivers of Global Forest Loss, Science. (2018) 361, no. 6407, 1108–1111, https://doi.org/10.1126/science.aau3445, 2-s2.0-85053347244.
10.1126/science.aau3445
CAS PubMed Web of Science® Google Scholar
18 Wassie S., Natural Resource Degradation Tendencies in Ethiopia: a Review, Environ Syst Res. (2020) 9, no. 1, 33–29, https://doi.org/10.1186/s40068-020-00194-1.
10.1186/s40068-020-00194-1
Google Scholar
19 Angelsen A., Jagger P., Babigumira R. et al., Environmental Income and Rural Livelihoods: A Global- Comparative Analysis, World Development. (2014) 64, 12–S28, https://doi.org/10.1016/j.worlddev.2014.03.006, 2-s2.0-84911399519.
10.1016/j.worlddev.2014.03.006
PubMed Web of Science® Google Scholar
20 Ajiola F. and Ilesanmi T., Deforestation, Food Security and Environmental Sustainability in South-West, Nigeria, 1960-2015, Unilag J Humanit. (2017) 5, no. 1.
Google Scholar
21 Christopher Ewuzie O., Uchenna Paulinus O., Louiemarie Adaeze I., Onyinye Gift U., and Chukwuemeka Chinedu E., Beyond Climate Change: The Effects of Deforestation on Food Security in the Lake Chad Region, IKENGA International Journal of Institute of African Studies. (2023) 24, no. 2, 1–34, https://doi.org/10.53836/ijia/2023/24/2/002.
10.53836/ijia/2023/24/2/002
Google Scholar
22 Chaudhary S., Tshering D., Phuntsho T., Uddin K., Shakya B., and Chettri N., Impact of Land Cover Change on a Mountain Ecosystem and its Services: Case Study from the Phobjikha Valley, Bhutan, Ecosystem Health and Sustainability. (2017) 3, no. 9, https://doi.org/10.1080/20964129.2017.1393314, 2-s2.0-85043711434.
10.1080/20964129.2017.1393314
Web of Science® Google Scholar
23 Lemenih M., Review of the Historic Trajectory of Deforestation, its Drivers and Implications in Ethiopia, Preprints. (2024) .
Google Scholar
24 Kasu H. B., Impact of Deforestation in Ethiopia, J. Selva Andina biosph. (2022) 10, no. 2, 86–95, https://doi.org/10.36610/j.jsab.2022.100200086x.
10.36610/j.jsab.2022.100200086x
Google Scholar
25 Bekele M., Tesfaye Y., Mohammed Z. et al., The Context of REDD+ in Ethiopia: Drivers, Agents and Institutions, 2015, Bogor, Indonesia: CIFOR.
Google Scholar
26 Millennium Ecosystem Assessment, Ecosystems and Human Wellbeing: Synthesis, 2005, Island Press, Washington, DC.
Google Scholar
27 Tadesse G., Zavaleta E., Shennan C., and FitzSimmons M., Policy and Demographic Factors Shape Deforestation Patterns and Socio-Ecological Processes in Southwest Ethiopian Coffee Agroecosystems, Applied Geography. (2014) 54, 149–159, https://doi.org/10.1016/j.apgeog.2014.08.001, 2-s2.0-84906486033.
10.1016/j.apgeog.2014.08.001
Web of Science® Google Scholar
28 Solomon N., Hishe H., Annang T., Pabi O., Asante I., and Birhane E., Forest Cover Change, Key Drivers and Community Perception in Wujig Mahgo Waren Forest of Northern Ethiopia, Land. (2018) 7, no. 1, https://doi.org/10.3390/land7010032, 2-s2.0-85053047562.
10.3390/land7010032
Web of Science® Google Scholar
29 Mengist W., Soromessa T., and Feyisa G. L., Monitoring Afromontane Forest Cover Loss and the Associated Socio-Ecological Drivers in Kaffa Biosphere Reserve, Ethiopia, Trees, Forests and People. (2021) 6, https://doi.org/10.1016/j.tfp.2021.100161.
10.1016/j.tfp.2021.100161
Google Scholar
30 Masha M., Bojago E., Belayneh M., Tadila G., and Abera A., Quantifying Forest Degradation Rates and Their Drivers in Alle District, Southwestern Ethiopia: Implications for Sustainable Forest Management Practices, Geomatica. (2024) 76, no. 2, https://doi.org/10.1016/j.geomat.2024.100009.
10.1016/j.geomat.2024.100009
Google Scholar
31 Senbeta F. and Denich M., Effects of Wild Coffee Management on Species Diversity in the Afromontane Rainforests of Ethiopia, Forest Ecology and Management. (2006) 232, no. 1-3, 68–74, https://doi.org/10.1016/j.foreco.2006.05.064, 2-s2.0-33746313142.
10.1016/j.foreco.2006.05.064
Web of Science® Google Scholar
32 Wood A., Tolera M., Snell M., O’Hara P., and Hailu A., Community Forest Management (CFM) in South-West Ethiopia: Maintaining Forests, Biodiversity and Carbon Stocks to Support Wild Coffee Conservation, Global Environmental Change. (2019) 59, https://doi.org/10.1016/j.gloenvcha.2019.101980, 2-s2.0-85072570269.
10.1016/j.gloenvcha.2019.101980
PubMed Web of Science® Google Scholar
33 Friis I., Forests and Forest Trees of Northeast Tropical Africa: Their Natural Habitats and Distribution Patterns in Ethiopia, Djibouti and Somalia, 1992, Royal Botanical Gardens, London.
Google Scholar
34 Getahun K., Poesen J., and Van Rompaey A., Impacts of Resettlement Programs on Deforestation of Moist Evergreen Afromontane Forests in Southwest Ethiopia, Mountain Research and Development. (2017) 37, no. 4, 474–486, https://doi.org/10.1659/mrd-journal-d-15-00034.1, 2-s2.0-85039068244.
10.1659/MRD-JOURNAL-D-15-00034.1
Web of Science® Google Scholar
35 Mulatu K., Hundera K., and Senbeta F., Analysis of Land Use/land Cover Changes and Landscape Fragmentation in the Baro-Akobo Basin, Southwestern Ethiopia, Heliyon. (2024) 10, no. 7, https://doi.org/10.1016/j.heliyon.2024.e28378.
10.1016/j.heliyon.2024.e28378
Web of Science® Google Scholar
36 Debebe B., Senbeta F., Teferi E., Diriba D., and Teketay D., Analysis of Forest Cover Change and its Drivers in Biodiversity Hotspot Areas of the Semien Mountains National Park, Northwest Ethiopia, Sustainability. (2023) 15, no. 4, https://doi.org/10.3390/su15043001.
10.3390/su15043001
Web of Science® Google Scholar
37 Kassa H., Frankl A., Dondeyne S., Poesen J., and Nyssen J., Sediment Yield at Southwest Ethiopia’s Forest Frontier, Land Degradation & Development. (2019) 30, no. 6, 695–705, https://doi.org/10.1002/ldr.3260, 2-s2.0-85060828142.
10.1002/ldr.3260
Web of Science® Google Scholar
38 Kalsido T. and Berhanu B., Impact of Land-Use Changes on Sediment Load and Capacity Reduction of Lake Ziway, Ethiopia, Natural Resources. (2020) 11, no. 11, 530–542, https://doi.org/10.4236/nr.2020.1111031.
10.4236/nr.2020.1111031
Google Scholar
39 Mantilla Contreras J., Schüßler D., and Zerbe S., Editorial: Current Challenges in Forest Restoration and Sustainable Forest Management, Front For Glob Change. (2023) 6, https://doi.org/10.3389/ffgc.2023.1172760.
10.3389/ffgc.2023.1172760
PubMed Google Scholar
40 Hussein A., Impacts of Land Use and Land Cover Change on Vegetation Diversity of Tropical Highland in Ethiopia, Applied and Environmental Soil Science. (2023) 2023, no. 1, 2531241–2531311, https://doi.org/10.1155/2023/2531241.
10.1155/2023/2531241
Web of Science® Google Scholar
41 Lu D. and Weng Q., A Survey of Image Classification Methods and Techniques for Improving Classification Performance, International Journal of Remote Sensing. (2007) 28, no. 5, 823–870, https://doi.org/10.1080/01431160600746456, 2-s2.0-33947591833.
10.1080/01431160600746456
Web of Science® Google Scholar
42 Song X., Hansen M., Stehman S. et al., Global Land Change from 1982 to 2016, Nature. (2018) 560, no. 7720, 639–643, https://doi.org/10.1038/s41586-018-0411-9, 2-s2.0-85054075812.
10.1038/s41586-018-0411-9
CAS PubMed Web of Science® Google Scholar
43 Abdurahman A., Yirsaw E., Nigussie W., and Hundera K., Past and Future Land-Use/land-Cover Change Trends and its Potential Drivers in Koore’s Agricultural Landscape, Southern Ethiopia, Geocarto International. (2023) 38, no. 1, https://doi.org/10.1080/10106049.2023.2229952.
10.1080/10106049.2023.2229952
Web of Science® Google Scholar
44 Ahammad R., Stacey N., Eddy I., Tomscha S., and Sunderland T., Recent Trends of Forest Cover Change and Ecosystem Services in Eastern Upland Region of Bangladesh, The Science of the Total Environment. (2019) 647, 379–389, https://doi.org/10.1016/j.scitotenv.2018.07.406, 2-s2.0-85050912989.
10.1016/j.scitotenv.2018.07.406
CAS PubMed Web of Science® Google Scholar
45 Rotich B., Kindu M., Kipkulei H., Kibet S., and Ojwang D., Impact of Land Use/land Cover Changes on Ecosystem Service Values in the Cherangany Hills Water Tower, Kenya, Environmental Challenges. (2022) 8, https://doi.org/10.1016/j.envc.2022.100576.
10.1016/j.envc.2022.100576
Google Scholar
46 Berhanu Y., Dalle G., Sintayehu D. W., Kelboro G., and 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, no. 2, https://doi.org/10.1016/j.heliyon.2023.e13711.
10.1016/j.heliyon.2023.e13711
Web of Science® Google Scholar
47 Tesfay F., Tadesse S. A., Getahun Y. S., Lemma E., and Gebremedhn A. Y., Evaluating the Impact of Land Use Land Cover Changes on the Values of Ecosystem Services in the Chacha Watershed, Ethiopia’s Central Highland, Environmental and Sustainability Indicators. (2023) 18, https://doi.org/10.1016/j.indic.2023.100256.
10.1016/j.indic.2023.100256
Web of Science® Google Scholar
48 Tadesse T., Berhanu Y., Gitima G., Kassie M., and Jakubus M., Impacts of Land Use and Cover Changes on Ecosystem Service Values from 1992 to 2052 in Gena District, Southwest Ethiopia, Scientific African. (2024) 24, https://doi.org/10.1016/j.sciaf.2024.e02244.
10.1016/j.sciaf.2024.e02244
Web of Science® Google Scholar
49 Yilma Z. A. and Yitay B. A., Assessing Vegetation Cover and Valuing Ecosystem Services in Southwestern Ethiopia: Implications for Conservation, PLoS One. (2024) 19, no. 6, https://doi.org/10.1371/journal.pone.0304456.
10.1371/journal.pone.0304456
Web of Science® Google Scholar
50 Kettle C. J., Harrison R., and Koh L. P., C. J. Kettle and L. P. Koh, Future Forests: Fantasy or Façade? A Synthesis, Global Forest Fragmentation, 2014, CABI, Wallingford UK, 158–169.
10.1079/9781780642031.0158
Google Scholar
51 Kassa H., Abiyu A., Hagazi N., Mokria M., Kassawmar T., and Gitz V., Forest Landscape Restoration in Ethiopia: Progress and Challenges, Front For Glob Change. (2022) 5, https://doi.org/10.3389/ffgc.2022.796106.
10.3389/ffgc.2022.796106
PubMed Web of Science® Google Scholar
52 Yami M. and Mekuria W., Challenges in the Governance of Community-Managed Forests in Ethiopia: Review, Sustainability. (2022) 14, no. 3, https://doi.org/10.3390/su14031478.
10.3390/su14031478
Web of Science® Google Scholar
53 Angelstam P., Bush T., and Manton M., Challenges and Solutions for Forest Biodiversity Conservation in Sweden: Assessment of Policy, Implementation Outputs, and Consequences, Land. (2023) 12, no. 5, https://doi.org/10.3390/land12051098.
10.3390/land12051098
PubMed Web of Science® Google Scholar
54 Gubila M., Regassa A., Legess G., and Mulatu K., Land Use Land Cover Change and its Effect on Selected Soil Physico-Chemical Properties in Southwest Ethiopia, American Journal of Environmental Science and Engineering. (2024) 8, no. 3, 65–78, https://doi.org/10.11648/j.ajese.20240803.12.
10.11648/j.ajese.20240803.12
Google Scholar
55 Mengistu A. and Woldetsedik M., Proximate Causes and Underlying Driving Forces of Land Cover Change in Southwest Ethiopia, J sustain dev Afri. (2018) 1520–5509.
Google Scholar
56 Yadeta T., Deribew K. T., Getahun K., Debesa G., Abreha G., and Hailu S., Recent Resettlement Programs, as Drivers for Afromontane Forest Loss in the Hawa-Galan District of Ethiopia, Cogent Social Sciences. (2022) 8, no. 1, https://doi.org/10.1080/23311886.2022.2088462.
10.1080/23311886.2022.2088462
PubMed Web of Science® Google Scholar
57 Ameneshewa W., Kebede Y., Unbushe D., and Legesse A., Trends of Land Use Land Cover Dynamics of Sheka Biosphere Reserve, A Case of Shato Core Area, Southwest Ethiopia, PLoS One. (2023) 18, no. 11, https://doi.org/10.1371/journal.pone.0287830.
10.1371/journal.pone.0287830
Web of Science® Google Scholar
58 Getahun K., Van Rompaey A., Van Turnhout P., and Poesen J., Factors Controlling Patterns of Deforestation in Moist Evergreen Afromontane Forests of Southwest Ethiopia, Forest Ecology and Management. (2013) 304, 171–181, https://doi.org/10.1016/j.foreco.2013.05.001, 2-s2.0-84878437489.
10.1016/j.foreco.2013.05.001
Web of Science® Google Scholar
59 Friis I., Demisse W., and Breuge L., Atlas of the Potential Vegetation of Ethiopia, 2010, The Royal Danish Academy of Sciences and Letters, Copenhagen, Denmark.
Google Scholar
60 Dewitte O., Jones A., Spaargaren O. et al., Harmonisation of the Soil Map of Africa at the Continental Scale, Geoderma. (2013) 211–212, 138–153, https://doi.org/10.1016/j.geoderma.2013.07.007, 2-s2.0-84884332889.
10.1016/j.geoderma.2013.07.007
Web of Science® Google Scholar
61 Ethiopian Central Statistical Agency’s, The Federal Democratic Republic of Ethiopia Central Statistical Agency: Projected Population of 2019, 2019, CSA, Addis Ababa, Ethiopia.
Google Scholar
62 Congalton R. and Green K., Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, 2009, CRC Press.
Google Scholar
63 Lillesand T., Kiefer R., and Chipman J., Remote Sensing and Image Interpretation, 2015, 7th edition, Wiley.
Google Scholar
64 Costanza R., D’Arge R., de Groot R. et al., The Value of the World’s Ecosystem Services and Natural Capital, Ecological Economics. (1998) 25, no. 1, 253–315, https://doi.org/10.1016/s0921-8009(98)00020-2.
10.1016/s0921-8009(98)00020-2
Web of Science® Google Scholar
65 Temesgen H., Wu W., Shi X., Yirsaw E., Bekele B., and Kindu M., Variation in Ecosystem Service Values in an Agroforestry Dominated Landscape in Ethiopia: Implications for Land Use and Conservation Policy, Sustainability. (2018) 10, no. 4, https://doi.org/10.3390/su10041126, 2-s2.0-85045274307.
10.3390/su10041126
Web of Science® Google Scholar
66 Kindu M., Schneider T., Teketay D., and Knoke T., Changes of Ecosystem Service Values in Response to Land Use/land Cover Dynamics in Munessa–Shashemene Landscape of the Ethiopian Highlands, The Science of the Total Environment. (2016) 547, 137–147, https://doi.org/10.1016/j.scitotenv.2015.12.127, 2-s2.0-84953449362.
10.1016/j.scitotenv.2015.12.127
CAS PubMed Web of Science® Google Scholar
67 Gashaw T., Tulu T., Argaw M., Worqlul A., Tolessa T., and Kindu M., Estimating the Impacts of Land Use/land Cover Changes on Ecosystem Service Values: The Case of the Andassa Watershed in the Upper Blue Nile Basin of Ethiopia, Ecosystem Services. (2018) 31, no. 31, 219–228, https://doi.org/10.1016/j.ecoser.2018.05.001, 2-s2.0-85046730384.
10.1016/j.ecoser.2018.05.001
Web of Science® Google Scholar
68 Mengist W., Soromessa T., and Feyisa G., Estimating the Total Ecosystem Services Value of Eastern Afromontane Biodiversity Hotspots in Response to Landscape Dynamics, Environmental and Sustainability Indicators. (2022) 14, https://doi.org/10.1016/j.indic.2022.100178.
10.1016/j.indic.2022.100178
Web of Science® Google Scholar
69 Bera B., Bhattacharjee S., Sengupta N. et al., Significant Reduction of Carbon Stocks and Changes of Ecosystem Service Valuation of Indian Sundarban, Scientific Reports. (2022) 12, no. 1, https://doi.org/10.1038/s41598-022-11716-5.
10.1038/s41598-022-11716-5
Web of Science® Google Scholar
70 Lambin F., Geist J., and Lepers E., Dynamics of Land-Use and Land-Cover Change in Tropical Regions, Annual Review of Environment and Resources. (2003) 28, no. 1, 205–241, https://doi.org/10.1146/annurev.energy.28.050302.105459, 2-s2.0-0038197486.
10.1146/annurev.energy.28.050302.105459
Web of Science® Google Scholar
71 Kindu M., Schneider T., Teketay D., and Knoke T., Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands, Remote Sensing. (2013) 5, 2411–2435, https://doi.org/10.3390/rs5052411, 2-s2.0-84880337702.
10.3390/rs5052411
Web of Science® Google Scholar
72 Kothari C., Research Methodology: Methods and Techniques, 2004, 2nd edition, New Age International (P) Limited, New Delhi.
Google Scholar
73 Boyd H., Westfall R., and Stasch S., Marketing Research Text and Cases, 1985, 6th edition.
Google Scholar
74 Bartlett J., Kotrlik J., and Higgins C., Organizational Research: Determining Appropriate Sample Size in Survey Research Appropriate Sample Size in Survey Research, Int Inf Technol J. (2001) 19, no. 1.
Google Scholar
75 Handavu F., Chirwa P., and Syampungani S., Socio-economic Factors Influencing Land-Use and Land-Cover Changes in the Miombo Woodlands of the Copperbelt Province in Zambia, Forest Policy and Economics. (2019) 100, 75–94, https://doi.org/10.1016/j.forpol.2018.10.010, 2-s2.0-85057323897.
10.1016/j.forpol.2018.10.010
Web of Science® Google Scholar
76 Ethiopian Central Statistical Agency’s, Federal Democratic Republic of Ethiopia Office of Population and Housing Census Commission Central Statistical Authority the 1994 Population and Housing Census of Ethiopia Result for SNNRR, 1996.
Google Scholar
77 Ethiopian Central Statistical Agency’s, Fedral Democratic Republic of Ethiopia Office of Population and Housing Census Commission Central Statistical Authority the 2007 Population and Housing Census of Ethiopia Result for SNNPR Analytical Report, Addis Ababa, Ethiopia. (2007) .
Google Scholar
78 Kindu M., Schneider T., Teketay D., and Knoke T., Drivers of Land Use/land Cover Changes in Munessa-Shashemene Landscape of the South-Central Highlands of Ethiopia, Environmental Monitoring and Assessment. (2015) 187, no. 7, https://doi.org/10.1007/s10661-015-4671-7, 2-s2.0-84934964659.
10.1007/s10661-015-4671-7
PubMed Web of Science® Google Scholar
79 Munthali M., Davis N., Adeola A. et al., Local Perception of Drivers of Land-Use and Land-Cover Change Dynamics across Dedza District, Central Malawi Region, Sustainability. (2019) 11, no. 3, https://doi.org/10.3390/su11030832, 2-s2.0-85061082925.
10.3390/su11030832
Web of Science® Google Scholar
80 Berihun M. L., Tsunekawa A., Haregeweyn N. et al., Exploring Land Use/land Cover Changes, Drivers and Their Implications in Contrasting Agro-Ecological Environments of Ethiopia, Land Use Policy. (2019) 87, https://doi.org/10.1016/j.landusepol.2019.104052, 2-s2.0-85067410550.
10.1016/j.landusepol.2019.104052
Web of Science® Google Scholar
81 Gebrelibanos T. and Assen M., Farmers’ Attitudes towards Land Resource Conservation and its Implication for Sustainable Land Management in the Hirmi Watershed, Northern Highlands of Ethiopia, Journal of Sustainable Development in Africa. (2013) 15, no. 1.
Google Scholar
82 Belay H., Drivers of Land Use/Land Cover Change in the Guraferda District of Bench Maji Zone, Southwestern Ethiopia, Research Review: Journal of Ecology and Environmental Sciences. (2018) 6, no. 1, 52–62.
Google Scholar
83 Tadesse G., Zavaleta E., and Shennan C., Coffee Landscapes as Refugia for Native Woody Biodiversity as Forest Loss Continues in Southwest Ethiopia, Biological Conservation. (2014) 169, 384–391, https://doi.org/10.1016/j.biocon.2013.11.034, 2-s2.0-84892646266.
10.1016/j.biocon.2013.11.034
Web of Science® Google Scholar
84 Stellmacher T., The Historical Development of Local Forest Governance in Ethiopia: from Imperial Times to the Military Regime of the Derg, Africa Spectrum. (2007) 42, no. 3, 519–530.
Web of Science® Google Scholar
85 Kassa H., Dondeyne S., Poesen J., Frankl A., and Nyssen J., Transition from Forest-Based to Cereal-Based Agricultural Systems: a Review of the Drivers of Land-Use Change and Degradation in Southwest Ethiopia, Land Degradation & Development. (2016) 28, no. 2, 431–449, https://doi.org/10.1002/ldr.2575, 2-s2.0-84980368167.
10.1002/ldr.2575
Web of Science® Google Scholar
86 Dechassa C., Environmental Management System: During Imperial, Derg and EPRDF Periods in Ethiopia: Review Paper, Journal of Environment and Earth Science. (2015) 5, no. 3.
Google Scholar
87 Abera A., Yirgu T., and Uncha A., Impact of Resettlement Scheme on Vegetation Cover and its Implications on Conservation in Chewaka District of Ethiopia, Environmental Systems Research. (2020) 9, 2–17, https://doi.org/10.1186/s40068-020-00164-7.
10.1186/s40068-020-00164-7
Google Scholar
88 Girma W. and Beyene F., Institutional Challenges in Sustainable Forest Management: Evidence from the Gambella Regional State of Western Ethiopia, Journal of Sustainable Forestry. (2015) 34, no. 3, 233–258, https://doi.org/10.1080/10549811.2014.1003245, 2-s2.0-84926149482.
10.1080/10549811.2014.1003245
Web of Science® Google Scholar
89 Koyuncu C. and Yilmaz R., The Impact of Corruption on Deforestation: A Cross-Country Evidence, The Journal of Developing Areas. (2009) 42, no. 2, 213–222, https://doi.org/10.1353/jda.0.0010.
10.1353/jda.0.0010
Google Scholar
90 Meshesha T., Tripathi S., and Khare D., Analyses of Land Use and Land Cover Change Dynamics Using GIS and Remote Sensing during 1984 and 2015 in the Beressa Watershed Northern Central Highland of Ethiopia, Model Earth Syst Environ. (2016) 2, no. 4, 1–12, https://doi.org/10.1007/s40808-016-0233-4.
10.1007/s40808-016-0233-4
Web of Science® Google Scholar
91 Dibaba W. T., Demissie T. A., and Miegel K., Drivers and Implications of Land Use/Land Cover Dynamics in Finchaa Catchment, Northwestern Ethiopia, Land. (2020) 9, no. 4, https://doi.org/10.3390/land9040113.
10.3390/land9040113
Web of Science® Google Scholar
92 Senbeta F., Community Perception of Land Use/Land Cover Change and its Impacts on Biodiversity and Ecosystem Services in Northwestern Ethiopia, Journal of Sustainable Development in Africa. (2018) 20, no. 1.
Google Scholar
93 WoldeYohannes A., Cotter M., Kelboro G., and Dessalegn W., Land Use and Land Cover Changes and Their Effects on the Landscape of Abaya-Chamo Basin, Southern Ethiopia, Land. (2018) 7, no. 1, https://doi.org/10.3390/land7010002, 2-s2.0-85040777125.
10.3390/land7010002
Web of Science® Google Scholar
94 Mariye M., Jianhua L., and Maryo M., Land Use and Land Cover Change, and Analysis of its Drivers in Ojoje Watershed, Southern Ethiopia, Heliyon. (2022) 8, no. 4, https://doi.org/10.1016/j.heliyon.2022.e09267.
10.1016/j.heliyon.2022.e09267
PubMed Web of Science® Google Scholar
95 Bekele B., Wu W., and Yirsaw E., Drivers of Land Use-Land Cover Changes in the Central Rift Valley of Ethiopia, Sains Malaysiana. (2019) 48, no. 7, 1333–1345, https://doi.org/10.17576/jsm-2019-4807-03, 2-s2.0-85071460136.
10.17576/jsm-2019-4807-03
Web of Science® Google Scholar
96 Bekere J., Senbeta F., and Gelaw A., Socioeconomic Drivers of Land Use and Land Cover Change in Western Ethiopia, International Journal of Financial Research. (2023) 2023, 1–19, https://doi.org/10.1155/2023/8831715.
10.1155/2023/8831715
Google Scholar
97 Kalaba F. K., Quinn C. H., and Dougill A. J., Contribution of Forest Provisioning Ecosystem Services to Rural Livelihoods in the Miombo Woodlands of Zambia, Population and Environment. (2013) 35, no. 2, 159–182, https://doi.org/10.1007/s11111-013-0189-5, 2-s2.0-84886749020.
10.1007/s11111-013-0189-5
Web of Science® Google Scholar
98 Ahammad R., Stacey N., and Sunderland T. C., Use and Perceived Importance of Forest Ecosystem Services in Rural Livelihoods of Chittagong Hill Tracts, Bangladesh, Ecosystem Services. (2019) 35, 87–98, https://doi.org/10.1016/j.ecoser.2018.11.009, 2-s2.0-85057338298.
10.1016/j.ecoser.2018.11.009
Web of Science® Google Scholar
99 Wangchuk J., Choden K., Sears R. R., and Baral H., Ecosystem Services from Forest Management Units in Eastern and Central Bhutan, 2019, CIFOR, Bogor, Indonesia.
Google Scholar
100 Tazebew E. and Woldie Z., The Role of Forest for Rural Livelihood Diversification in Ethiopia, International Journal of Agricultural Science and Research. (2023) 11, no. 1, 1–7.
Google Scholar
101 Tolessa T., Senbeta F., and Kidane M., The Impact of Land Use/land Cover Change on Ecosystem Services in the Central Highlands of Ethiopia, Ecosystem Services. (2017) 23, 47–54, https://doi.org/10.1016/j.ecoser.2016.11.010, 2-s2.0-85002410915.
10.1016/j.ecoser.2016.11.010
Web of Science® Google Scholar
102 Solomon N., Segnon A. C., and Birhane E., Ecosystem Service Values Changes in Response to Land-Use/Land-Cover Dynamics in Dry Afromontane Forest in Northern Ethiopia, International Journal of Environmental Research and Public Health. (2019) 16, no. 23, https://doi.org/10.3390/ijerph16234653.
10.3390/ijerph16234653
Web of Science® Google Scholar
103 Shiferaw M., Kebebew Z., and Gemeda D. O., Effect of Forest Cover Change on Ecosystem Services in Central Highlands of Ethiopia: A Case of Wof-Washa Forest, Heliyon. (2023) 9, no. 7, https://doi.org/10.1016/j.heliyon.2023.e18173.
10.1016/j.heliyon.2023.e18173
Web of Science® Google Scholar
104 Taye A., Anbacha A., and Girma F., Land Use and Land Cover Dynamics: Deriving Forces and Perceptions of Local Community in Derashe, Southern Ethiopia, The Scientific World Journal. (2023) 2023, 1–12, https://doi.org/10.1155/2023/6905404.
10.1155/2023/6905404
Google Scholar
105 Durán Zuazo V. H. and Rodríguez Pleguezuelo C. R., Soil-erosion and Runoff Prevention by Plant Covers. A Review, Agronomy for Sustainable Development. (2008) 28, no. 1, 65–86, https://doi.org/10.1051/agro:2007062, 2-s2.0-40049102138.
10.1051/agro:2007062
Web of Science® Google Scholar
106 Hooper D., ChapinF. S.III, Ewel J. et al., Effects of Biodiversity on Ecosystem Functioning: a Consensus of Current Knowledge, Ecological Monographs. (2005) 75, no. 1, 3–35, https://doi.org/10.1890/04-0922, 2-s2.0-20044377582.
10.1890/04-0922
Web of Science® Google Scholar
107 Balvanera P., Pfisterer A., Buchmann N. et al., Quantifying the Evidence for Biodiversity Effects on Ecosystem Functioning and Services, Ecology Letters. (2006) 9, no. 10, 1146–1156, https://doi.org/10.1111/j.1461-0248.2006.00963.x, 2-s2.0-33748782805.
10.1111/j.1461-0248.2006.00963.x
CAS PubMed Web of Science® Google Scholar
108 Bagley J. E., Desai A. R., Harding K. J., Snyder P. K., and Foley J. A., Drought and Deforestation: Has Land Cover Change Influenced Recent Precipitation Extremes in the Amazon?, Journal of Climate. (2014) 27, no. 1, 345–361, https://doi.org/10.1175/jcli-d-12-00369.1, 2-s2.0-84892473208.
10.1175/JCLI-D-12-00369.1
Web of Science® Google Scholar
109 Ethiopian Electric Power Corporation, Decommission of Dembi Hydro Electric Power Plants. Dembi Power Plant Environmental Impact and Feasibility Study Report, 2000, Final report, Addis Ababa.
Google Scholar
110 Min A., Forest Cover Change in South Gondar, Ethiopia from 1985 to 2015: Landsat Remote Sensing Analysis and Conservation Implications, 2016.
Google Scholar
111 Tolera M., Lemenih M., O’Hara P., and Wood A., In-situ Conservation of Wild Forest Coffee-Exploring the Potential of Participatory Forest Management in South West Ethiopia, 2015.
Google Scholar
112 Gutiérrez-Zamora V. and Estrada M., Responsibilization and State Territorialization: Governing Socio-Territorial Conflicts in Community Forestry in Mexico, For Policy Econ. (2020) 116.
10.1016/j.forpol.2020.102188
Web of Science® Google Scholar
113 Snorek J. and Bolger D., Can the Center Hold? Boundary Actors and Marginality in a Community-Based Natural Resource Management Network, Ecology and Society. (2022) 27, no. 3, https://doi.org/10.5751/es-13512-270341.
10.5751/ES-13512-270341
PubMed Google Scholar
114 Sarmiento F., Larrea C., Oeschger A., and Jose R., Measures to Enhance Forest Conservation and Reduce Deforestation: Viewpoints and Lessons from Producing Countries, SSI Report. (2024) .
Google Scholar
115 Chakravarty S., Ghosh S., Suresh C., Dey A., and Shukla G., Deforestation: Causes, Effects and Control Strategies, Glob Perspect sustain for manag. (2012) no. 1, 1–26.
Google Scholar
116 Angelsen A., A. Angelsen, Policy Options to Reduce Deforestation, Realising REDD+: National Strategy and Policy Options, 2009, CIFOR, Bogor, Indonesia, 125–138.
Google Scholar
117 Bakehe N. P., The Impact of Agricultural Productivity on Deforestation in Central Africa, AERC Research Paper 537. (2023) .
Google Scholar
118 Oljirra A., The Causes, Consequences and Remedies of Deforestation in Ethiopia, Journal of Degraded and Mining Lands Management. (2019) 6, no. 3, 1747–1754, https://doi.org/10.15243/jdmlm.2019.063.1747.
10.15243/jdmlm.2019.063.1747
Google Scholar
119 Abman R., Garg T., Pan Y., and Singhal S., Agriculture and Deforestation, 2020.
Google Scholar
120 Yang K. F., Gergel S. E., and Baudron F., Forest Restoration Scenarios Produce Synergies for Agricultural Production in Southern Ethiopia, Agriculture, Ecosystems & Environment. (2020) 295, https://doi.org/10.1016/j.agee.2020.106888.
10.1016/j.agee.2020.106888
Web of Science® Google Scholar
121 Agidew A. and Singh K., Factors Affecting the Adoption of Sustainable Land Management Practices at Farm Level in the Northeastern Highlands of Ethiopia: The Teleyayen Sub-watershed Case Study, J environ pollut manag. (2019) 2, no. 1, 103–115.
Google Scholar
122 Ewunetu A., Simane B., Teferi E., and F Zaitchik B., Relationships and the Determinants of Sustainable Land Management Technologies in North Gojjam Sub-Basin, Upper Blue Nile, Ethiopia, Sustainability. (2021) 13, no. 11, https://doi.org/10.3390/su13116365.
10.3390/su13116365
Web of Science® Google Scholar
123 Khadka D., Aryal A., Bhatta K. P., Dhakal B. P., and Baral H., Agroforestry Systems and Their Contribution to Supplying Forest Products to Communities in the Chure Range, Central Nepal, Forests. (2021) 12, no. 3, https://doi.org/10.3390/f12030358.
10.3390/f12030358
Web of Science® Google Scholar
124 Tsegaye N. T., The Impact of Agroforestry Practice on Forest Conservation and Community Livelihood Improvement: A Case of Buno Bedele Zone of West Ethiopia’S Chora District, Environmental and Sustainability Indicators. (2023) 17, https://doi.org/10.1016/j.indic.2023.100229.
10.1016/j.indic.2023.100229
Web of Science® Google Scholar
125 Llopiz-Guerra K., Ruiz D., Hernandez R., Mejia V., Nunayalle J., and Sanchez K., Importance of Environmental Education in the Context of Natural Sustainability, Natural and Engineering Sciences. (2024) 9, no. 1, 57–71, https://doi.org/10.28978/nesciences.1473461.
10.28978/nesciences.1473461
Google Scholar
126 Gebrekidan T. K., Environmental Education in Ethiopia: History, Mainstreaming in Curriculum, Governmental Structure, and its Effectiveness: A Systematic Review, Heliyon. (2024) 10, no. 9, https://doi.org/10.1016/j.heliyon.2024.e30573.
10.1016/j.heliyon.2024.e30573
Web of Science® Google Scholar
127 Tacconi L., Rodrigues R. J., and Maryudi A., Law Enforcement and Deforestation: Lessons for Indonesia from Brazil, Forest Policy and Economics. (2019) 108, https://doi.org/10.1016/j.forpol.2019.05.029, 2-s2.0-85066986657.
10.1016/j.forpol.2019.05.029
Web of Science® Google Scholar

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Analysis of Forest Cover Change in the Southwest Ethiopia: Key Drivers, Impacts, and Conservation Implications

Abstract

1. Introduction