Sustainable Forest Management Strategies Research Articles

Disentangling the impact of nested sources of variability on species growth processes: A mixture of multilevel mixed model approach.

SummaryThe understanding of tree growth processes is crucial for promoting sustainable forest management strategies. This is a challenging task in highly biodiverse ecosystems where many tree species are observed on very few individuals and the small sample sizes hinder a good fit of species‐specific models. We propose the use of finite mixture of random coefficient regression models with multilevel nested random effects to infer guild specific fixed and random effects while evaluating the relative importance of the nested sources of variability on goodness‐of‐fit. This approach extends finite mixture of linear mixed model used for longitudinal or single group structured data contexts. A dedicated expectation–maximisation algorithm is introduced for parameter estimation. Simulations are performed for the evaluation of the misspecification of nested‐grouping structures. This work has been motivated by data collected biennially in Central African rainforests from 1986 to 2010. We show the accuracy of the proposed approach in successfully reproducing individual growth processes and classifying tree species into well‐differentiated clusters with clear ecological interpretations. Moreover, results confirm that interindividual variability appears as the most important factor to explain tropical tree species growth process variability from Central Africa forests.

Local Community Attitude towards Forest-Based Ecotourism Development in Arbegona and Nensebo Woredas, Southern Ethiopia

Understanding local community awareness and attitude towards forest-based ecotourism welfare is an important input for policymakers to develop and implement sustainable forest resource management strategies. However, the local community’s awareness and attitude towards forest-based ecotourism development have been poorly assessed and documented in remote areas. To this end, the present study assessed the local community’s awareness and attitude in Arbegona and Nensebo woredas’ forests, southern Ethiopia, towards ecotourism development. Household and key informant interviews and focus group discussions were used to collect data. Results indicated that more than half (57.9%) of the respondents expressed a negative attitude towards forest-based ecotourism development. The educational status of the respondents, their age, presence of conservation measures, source of energy for household consumption, and biophysical and socio-cultural variations in the study sites are significantly associated with local communities’ attitudes that have been exhibited towards the development of ecotourism and conservation of forest resources. Creating awareness through persuasion and communication of new and well-suited information, promoting alternative livelihood options, encouraging local benefits from the conservation of forest resources, and compensating costs induced by wild animals can minimise negative attitudes, thereby contributing to the enhancement of local communities’ positive attitudes towards forest resource conservation.

Reinforcement Learning for Stand Structure Optimization of Pinus yunnanensis Secondary Forests in Southwest China

Aiming to enhance the efficiency and precision of multi-objective optimization in southwestern secondary growth of Pinus yunnanensis forests, this study integrated spatial and non-spatial structural indicators to establish objective functions and constraints for assessing forest structure. Felling decisions were made using the random selection method (RSM), Q-value method (QVM), and V-map method (VMM). Actions taken to optimize the forest stand structure (FSS) through tree selection were approached as decisions by a reinforcement learning (RL) agent. Leveraging RL’s trial-and-error strategy, we continually refined the agent’s decision-making process, applying it to multi-objective optimization. Simulated felling experiments conducted across circular sample plots (P1–P4) compared RL, Monte Carlo (MC), and particle swarm optimization (PSO) in FSS optimization. Notable enhancements in the values of the objective function (VOFs) were observed across all plots. RL-based strategies exhibited improvements, achieving VOF increases of 17.24%, 44.92%, 34.66%, and 17.10% for P1–P4, respectively, outperforming MC-based (10.73%, 41.54%, 30.39%, and 15.07%, respectively) and PSO-based (14.08%, 37.78%, 26.17%, and 16.23%, respectively) approaches. The hybrid M7 scheme, integrating RL with the RSM, consistently outperformed other schemes across all plots, yielding an average 26.81% increase in VOF compared to the average enhancement of all schemes (17.42%). This study significantly advances the efficacy and precision of multi-objective optimization strategies for Pinus yunnanensis secondary forests, emphasizing RL’s superior optimization performance, particularly when combined with the RSM, highlighting its potential for optimizing sustainable forest management strategies.

The relative impacts of vegetation, topography and weather on landscape patterns of burn severity in subtropical forests of southern China

Understanding the landscape patterns of burn severity is vital for managing fire-prone ecosystems. Relatively limited research has been done about fire and burn severity patterns in subtropical forests. Here, we derived the pre-fire forest type data from a global land-cover product at 30 m resolution based on time-series Landsat imageries. Using Landsat 8 OLI remote sensing imagery and field-based composite burn index (CBI), this study spatially mapped the burn severity of 27 forest fires in the subtropical forest ecosystems in southern China from 2017 to 2021. The landscape pattern of patches with different burn severity was quantified using landscape indices. In addition, factors influencing the patterns of burn severity across the landscape were determined using the Geodetector model. Burn severity of patches varied significantly over space. High burn severity was common in forest patches with low fragmentation, low patch density, and regular shape. In contrast, moderate and low burn severity was prevalent in patches with smaller patch size, high patch density, and complex shapes. Extensively burned forest patches were located at higher elevations, while more fragmented patches were located in gently sloping areas. Topographic factors were the most significant factors influencing variances in burn severity across the forest patches, followed by weather conditions. Compared to low elevation areas, vegetation types at the high elevation areas (dominated by Masson pine) are more singular, with higher fuel loads, thus resulting in a more regularly-shaped distribution of highly severe burning patches. A detailed understanding of burn severity patterns and driving factors in a landscape can help develop sustainable forest management and restoration strategies. Practically, fire managers should conduct mechanical fuel treatments or thinning of forests at high-elevation areas to reduce the potential of severe fire behavior and the continuity of fire spread.

Mapping Forest Abrupt Disturbance Events in Southeastern China—Comparisons and Tradeoffs of Landsat Time Series Analysis Algorithms

Forest change monitoring is a fundamental and routine task for forest survey and planning departments, and the resulting forest change information acts as an underlying asset for sustainable forest management strategy development, ecological quality assessment, and carbon cycle research. The traditional ground-based manual monitoring of forest change has the disadvantages of high time and labor costs, low accessibility, and poor timeliness over wide regions. Remote sensing technology has become a popular approach for multi-scale forest change monitoring due to its multiple available spatial, spectral, temporal, and radiometric resolutions and wide coverage. Particularly, the free access policy of long time series archive data of Landsat (around 50 years) has triggered many automated analysis algorithms for landscape-scale forest change analysis, such as VCT, LandTrendr, BFAST, and CCDC. These automated algorithms differ in their principles, parameter settings, execution complexity, and disturbance types to be detected. Thus, selecting a suitable algorithm to satisfy the particular forest management demands is an urgent and challenging task for forest managers in a given forested area. In this study, Lishui City, Zhejiang Province, a typical plantation forest region in Southern China where forest disturbance widely and frequently exists, was selected as the study area. Based on the GEE platform, the algorithmic adaptability of VCT, LandTrendr, and CCDC in monitoring abrupt forest disturbance events was compared and assessed. The results showed that the kappa coefficients of the abrupt disturbance events detected by the three algorithms were at 0.704 (LandTrendr), 0.660 (VCT), and 0.727 (CCDC), and the corresponding overall accuracies were at 0.852, 0.830, and 0.862, respectively. The validated disturbance occurrence time consistency reached nearly 80% for the three algorithms. In light of the characteristics of forest disturbance occurrence in southeastern China as well as the algorithmic complexity, efficiency, and adaptability, LandTrendr was recommended as the most suitable one in this region or other similar regions. Overall, the forest change monitoring process based on GEE is becoming more simplified and easily implemented, and the comparisons and tradeoffs in this study provide a reference for the choice of long time series forest monitoring algorithms.

Vegetation density and altitude determine the supply of dry Afromontane forest ecosystem services: Evidence from Ethiopia

Mapping forest ecosystem services (ES) and identifying both natural and anthropogenic factors influencing their supply and distribution is a key step for sustainable and climate-smart forest management planning. This is particularly important for dry and mountain forests which are among the most threatened and neglected ecosystems globally. These forests are facing increasing demands for different land uses and ES with rising conflicts with each other.This study identifies, quantifies, and maps the spatial distribution of one supporting, three regulating, and four provisioning forest ES. Furthermore, a set of potential natural and anthropogenic factors influencing ES distribution in Desa’a forest, a dry Afromontane Forest in Tigray, Ethiopia, was assessed. The supply of some of the assessed ES depended on vegetation density, while the supply of others depended on woody species diversity, which resulted in a clustering of ES in two different geographical areas. Both natural and anthropogenic factors were important for explaining variations in the spatial distribution of ES, of which altitude and vegetation density were the most influential factors.Our findings can contribute to the development of sustainable forest management strategies for dry and mountain forests by providing knowledge on the spatial supply of ES. The ES maps can be used as a basis for developing dedicated zoning plans which could improve forest management.

Modeling potential distribution and above-ground biomass of Scots pine (Pinus sylvestris L.) forests in the Inner Anatolian Region, Türkiye.

Scots pine (Pinus sylvestris L.) holds a substantial position as a tree species designated for biomass energy within European forests, covering a significant part of Türkiye's forests. We used the machine learning technique, namely, maximum entropy (MaxEnt), to estimate the suitable areas for Scots pine and to investigate its potential future distribution under various climate change scenarios in Inner Anatolian Region, Türkiye. The distribution data of Scots pine was utilized, and a set of 20 variables was chosen from spectral, topographic, and bioclimatic datasets to train the MaxEnt model. A map depicting the potential distribution of Scots pine in the area was generated, and alterations in its spatial distribution under SSP2-4.5 and SSP5-8.5 climate change scenarios were predicted. The results showed that the most effective factors for the distribution of Scots pine in the region were normalized difference vegetation index (NDVI), Red band of the imagery, and Bio19 variables, and the contribution percentages were 45.6%, 18.5%, and 18.1%, respectively. Current conditions have indicated that 81.11% of the region is not suitable for Scots pine. Highly suitable areas for Scots pine constituted 0.88% of the total area in the east and southeast parts of the region. Considering the SSP2-4.5 and SSP5-8.5 scenarios, it has been determined that there may be a partial increase in highly suitable areas. The above-ground biomass (AGB) data generated based on potential distribution areas were predicted between 0.04 and 168.76 t ha-1, and the areas with dense biomass over 120 t ha-1 were identified in the west, north, and northeast parts of the region. While actual AGB of Scots pine was 6.92 MT, its potential AGB was estimated 125.93 MT in total area. The difference may well be attributed to the wide potential distribution of Scots pine stands in the area apart from the current forest lands. Nevertheless, this research contributes to the holistic management of forests and provides substantial values for formulating well-suited silvicultural interventions, developing sustainable forest management strategies, and furthering research aimed at estimating biomass reserves.

Forest products monetary contribution to households’ income: A means to improve the livelihood of a low-income rural community in South Ethiopia

Forest products and forest-based activities in Ethiopia play a substantial economic contribution to the livelihoods of rural households. Despite its pivotal role, empirical data are inadequate on forests' monetary contribution across a range of ecological locations and social situation, particularly for rural agrarian communities of the developing nations, like Ethiopia. We estimated the economic contributions of forest products and forest-based activities to forest-dependent rural households’ income and highlighted key socioeconomic characteristics in Wolaita, Ethiopia. We collected demographic, socioeconomic, and forest use data using a semi-structured questionnaire survey of 384 households, and surveyed markets to determine the prices of forest products for the valuation of forest use in three districts. We used descriptive statistics, the Chi-square test, the two-sample t-test, and one-way ANOVA to analyze the data. Findings showed that all respondents were engaged in one way or another in forest-based activities, while 84.6 % were involved in farming. The annual average income from forest products was 252.7US per household, contributing to 28.1 % of the total yearly average income (989.4US$), and 38.3 % relative forest income (RFI). Annual mean household income from crops was 648.1US$. Average annual household income from grazing, charcoal, firewood, woodcutting, cut-and-carry, NTFP, seed selling, medicinal plants, and seedling selling was 77.3 %, 58.4 %, 50.2 %, 24.1 %, 18.5 %, 12.7 %, 7.1 %, 6.8 % and 5.4 % of the total annual mean income, respectively revealing a significant difference (p < 0.05). Family size, farmland size, distance to the forest, and occupation were the determinants of the contribution of forest products to household income. In general, poor households derive the highest relative forest income implying high dependence of the poorer on forest resources in the study area. The findings provide useful information for developing sustainable forest management policies and strategies to enhance the economic and ecological benefits of forests, and highlight the need for funding projects to pay attention to the specific household variables that affect forest use. Mainstreaming conservation activities in development sectors and integrating development and conservation projects may improve the livelihood of the low-income family in the region.

Local Economy and Forest-based Livelihood in Gandhamardan Region of Western Odisha

Natural forests play a key role in maintaining the planet’s ecosystems and are also crucial to rural livelihoods, particularly in less developed nations. Indigenous groups still need access to contemporary conveniences and possibilities for employment in remote parts of less developed nations. As a result, the use of forests significantly contributes to household subsistence and income. The economic impact of forest resources on the millions of rural households close to forests has recently been the research focus. This study aims to quantify the distributional influence of forest revenue on economic inequality among rural families in the Gandhamardan Hill area of the Balangir and Bargarh districts of Odisha. It also sought to ascertain absolute and relative forest income. The primary data or information were gathered by utilizing focus groups, direct interviews, structured household-level surveys, and key informant interviews. The results showed that 61.9% of the households in the sample engaged in activities related to the forest. Most of the local population participates in agriculture and forestry as the main source of livelihood. After agriculture revenue, forest income represented the second-most significant income portfolio. The income share for bushmeat was larger than the income share for other forest products. The economic disparity among the studied families was reduced by forest income. Therefore, restricting forest access would negatively affect rural people’s welfare and widen economic gaps. The knowledge from the current study is crucial for creating sustainable forest management policies and strategies that will preserve and increase the economic value of using forests without jeopardizing biodiversity preservation.

The role of social forestry in achieving NDC targets: Study cases of Lampung and DI Yogyakarta

Social Forestry (SF) in Indonesia has emerged as a keystone strategy for Sustainable Forest Management. By allocating 12.7 million ha of forest to be managed by local communities, the government has set in motion an ambitious plan for SF to reduce poverty, empower local people, and improve forest conditions. More recently, SF is framed for its opportunity to contribute to climate change mitigation and adaptation. This study focused on examining the contribution of SF to the Nationally Determined Contribution (NDC) goals in Yogyakarta and Lampung. By analyzing spatial data of SF areas and land cover changes using ArcView 10.8, the study assessed the carbon stock potential in SF areas. Carbon stock calculations were based on the 2022 National Forest Reference Level (FRL) for the periods before and after SF implementation. The finding of the study indicated that the carbon stock of SF areas in Yogyakarta and Lampung ranged from 9,214,381 to 9,923,420 ton CO2eq prior to SF, while the current carbon stock ranges from 8,703,489 to 9,393,706 ton CO2eq, representing a decrease (around 5.4%) rather than an increase. Overall, the changes in carbon stock were relatively small and localized, and the magnitude of the increase was insufficient to offset the overall decrease. To achieve the objectives of SF, such as meeting emission targets and achieving sustainable land use, it is crucial to carefully manage forest edges and fragmented forests, as they can contribute to carbon stock losses. Additionally, further studies and research are needed to improve the accuracy of carbon stock calculations, particularly for non-forest categories, which have higher uncertainty in the reference levels

Developing a sustainable community forest management strategy in the mountainous areas of Tanggamus, Lampung, Indonesia

Abstract. Morizon, Nurrochmat DR, Maharijaya A, Putra PK. 2023. Developing a sustainable community forest management strategy in the mountainous areas of Tanggamus, Lampung, Indonesia. Biodiversitas 24: 4503-4513. The Community Forest (HKm) program allows local communities to legally manage forests to strengthen their rights and reduce conflicts over forest resources. However, the program has yet to be successful in achieving its goals. This study aims to provide recommendations for HKM management strategies. The research involved surveys, field observations, and literature studies. Respondents were selected using purposive sampling and data analysis applied qualitative methods, including triangulation and SWOT analysis. The study found that the social and institutional aspects of the HKM program were not functioning well, with poor relationships among members of the farmers' group (Gapoktan). From an ecological perspective, there has been a decline in plant species. At the same time, the economic aspect shows that only 15% of members benefited from HKM, leaving the rest feeling its negative impact. The study recommends institutional restructuring, coaching, and capacity building for human resources, implementing agroforestry and organic schemes for coffee cultivation, providing guidance on alternative crops such as cardamom, and optimizing the role of cooperatives within the group. Key players must actively participate in HKM management. These recommendations could lead to the effective implementation of the HKM program.

Optimizing Carbon Sequestration in Forest Management Plans Using Advanced Algorithms: A Case Study of Greater Khingan Mountains

The Paris Agreement aims to combat climate change by reducing greenhouse gas emissions, with bioenergy identified as a potential solution. However, concerns remain about its impact on carbon stocks and the optimal timing for implementation. To address these challenges, we propose a comprehensive multi-objective optimization model for forest management that maximizes carbon sequestration and economic benefits. Our model integrates three key components: (1) a sophisticated carbon-sequestration model encompassing living plants, wood forest products, and soil and microbial carbon uptake, (2) dynamic factors such as forest fires and extreme weather events, and (3) an economic benefits model focused on wood-processing products. We optimized the forest-management strategy over ten years by leveraging the simulated annealing and Karush–Kuhn–Tucker (KKT) algorithms. Through simulations using data from China’s Greater Khingan Mountains region, we explored the optimal logging plans for maximizing carbon sequestration without external factors. Our results revealed that the optimized logging plans significantly enhance carbon sequestration compared to proportionally averaged logging plans. Next, we investigated the impact of external factors on forest management, specifically wildfires and extreme weather events. Our findings demonstrate that wildfires have a more-substantial detrimental effect on the absolute value of carbon sequestration and the extent of improvement achieved through model optimization. At the same time, extreme cold primarily affects the growth rate of carbon sequestration. We employed a linear-weighting approach and the Analytic Hierarchy Process (AHP) to address the trade-offs between carbon sequestration and economic benefits to transform the multi-objective optimization function into a single objective. The results showed that the optimized harvesting schedule can lead to improved economic benefits compared to uniformly harvesting trees. Moreover, the joint optimization approach enabled us to identify optimal solutions that balance carbon sequestration and economic benefits, offering sustainable forest management strategies. Our study provides valuable quantitative insights into forest management strategies that balance carbon sequestration and economic benefits, making it highly relevant for real-world applications.

Plant Diversity, Regeneration and Conservation of Sal Forests: A Review

The current scenario of the Indian sal forest is in alarming situation. The disturbance caused by humans and excess exploitation of its resources seriously impacts the regional sal forest. Associates of Sal is commonly harvested for household purposes, fire-wood, timbers, gums, tannins, paper plates and medicinal purposes. The greatest threat to the sal forest ecosystem is the rapid expansion of urbanization. The Sal-dominated forest is losing biodiversity at an alarming rate due to illegal cutting, encroachments and illegal trading of wildlife. The paper reviews the sal forest’s species diversity, regeneration status and conservation strategy. This study has been conducted after an immense literature survey and interpretation of published data concerning current disturbances to the sal forests of India. This study aims to adopt a sustainable forest management strategy based on a scientific understanding of professional silviculture practices. Proper forest conservation calls for a policy that would involve the mass participation of not only government officials but also the locals of the area.

Life after fire - Long-term responses of 20 timber species in semi-deciduous forests of West Africa

Understanding the response of commercial timber species to fire is fundamental to designing sustainable forest management strategies in West Africa. In this area of high deforestation for agriculture, semi-deciduous tropical forests have been reduced to a trickle in 50 years and are now subject not only to logging but also to increasingly frequent fires. This paper aims to study the long-term (37 years) demographic and growth responses of 20 timber species after a spatially heterogeneous fire in a West African semi-deciduous forest. We (i) inventoried, in 2020, 100 hectares of plots already established before the 1983 fire, (ii) built demographic and growth models, in a Bayesian framework, to estimate the vulnerability to fire of each species and (iii) linked this vulnerability to functional traits: wood density (WD) and bark thickness (BT). Our results show that most species have lower current abundances but higher growths in burnt areas than in fire-preserved areas. We identified three categories of vulnerability to fire: 1 non-vulnerable species, 16 species with vulnerable demography, and 3 highly vulnerable species. Species with high wood density or thin bark show higher demographic vulnerability to fire. Our results show that fire management is a top priority in West African semi-deciduous forests to maintain long-term populations of timber species and ultimately the forest industry. The differences in vulnerability observed, and the strong predictive power of the functional traits studied, should make it possible to (i) better define harvesting rates and adapt them to fire risk and (ii) better select commercial species to be promoted for reforestation or natural (assisted) regeneration.

Analysis of Forest Cover Change and Its Drivers in Biodiversity Hotspot Areas of the Semien Mountains National Park, Northwest Ethiopia

Forests provide multiple ecosystem services ranging from local livelihoods and socio-economic benefits to global ecological services. Despite these benefits, human activities have put immense strain on forest resources, resulting in forest degradation, impoverishment of the environment, and loss of livelihoods. Hence, monitoring forest cover change and identifying its drivers are essential for developing sustainable forest management strategies that restore forest resources and ecosystem services. This study examined the trends of forest cover changes and its drivers in and around the Semien Mountains National Park, Northwest Ethiopia, from 1984 to 2020. Data from Landsat satellite images of 1984, 1996, 2008, and 2020 were used for forest cover mapping and quantifying the changes using remote sensing techniques. A household survey and key informant interviews were also used to identify the drivers of forest cover change. Results showed an overall decline in forest cover and grasslands while cultivated lands, bare lands and built-up areas have registered gains in the study area. The percentage area under forests declined by about 31% (33,084 ha) over the past 36 years. This loss translates to an annual average forest loss of about 1.02% (919 ha). Conversely, cultivated land, bare land, and built-up areas have experienced an increase of about 159% (33,387 ha), 220% (10,121 ha), and 366% (6356 ha), respectively, over the last 36 years. Analysis of the 36-year change detection matrix revealed that about 62% of the study area experienced a transition, of which 21% and 41% were attributable to net change and swap change, respectively. The observed reduction in forest cover was driven by agricultural expansion, population growth, growing demand for fuelwood, livestock pressure, and forest fires. Such drastic changes in forest cover have a detrimental impact on the community’s well-being, biodiversity, climate, and the availability of forest ecosystem services. Thus, the study recommends concerted efforts by stakeholders to develop participatory forest management and public awareness that consider the co-existence of nature conservation and sustainable livelihoods.

Characterizing and assessing key ecosystem services in a representative forest ecosystem in Turkey

Characterizing and managing multiple ecosystem services is vital for decision-making in sustainable forest management. This study focuses on characterizing and assessing the biophysical and spatial distribution of ecosystem services such as wood production, biodiversity and carbon sequestration in the Kumluca state forest (Turkey). The key ecosystem services were thematically characterized, economically valued, spatially mapped and mobilized as a knowledge-base for ecosystem based planning. Average wood production is higher (135.06 m3/ha) than the overall average (72 m3/ha) in Turkey. The average carbon stock is 119 tC/ha, indicating a medium capacity. The habitat potential for biodiversity is low to medium, based on the 15 target species. Total economic value of the key ecosystem services is estimated at US$624,748,191 (US$6132/ha). The composition and spatial distribution of vegetation determines the extent and potential of ecosystem services. This study emphasizes that comprehensive quantification as well as valuation of ecosystem services can facilitate underpinnings for efficient communication and good decision making in sustainable resource management for multiple values. The orchestrated vision of conservation and sustainable use of ecosystem services based on a comprehensive characterization, modeling, valuing and participatory approach is a strategy for sustainable forest management.

DYNAMICS OF LAND PRODUCTIVITY IN TERMS OF NET PRIMARY PRODUCTION AS SUB-INDICATOR FOR LAND RESTORATION IN ETHIOPIA: IMPLICATION FOR IMPLEMENTATION OF SUSTAINABLE DEVELOPMENT GOAL-15

Land productivity is described as biological productive capacity of a land used as source of food, fiber and fuel to sustain human needs. Land degradation is one of global challenges of our time resulting to food insecurity and migration of people due to lack of inappropriate land use plan and management practices. Sustainable forest land scape restoration interventions can improve land productivity in meeting for Sustainable Development Goal-15. Study was conducted to analyze dynamics of land productivity in terms of net primary production on six land use and cover class from 2001-2015 using secondary information from Global Earth Trend Observation under Praise-3 portal of UNCCD. Ethiopia has total land mass of 1,119,715.5 km2. The dynamics of land productivity net primary production on six different land use and land cover class were analyzed from baseline year 2000 to target year 2015. The analyses of land productivity change from 2000-2015 showed that a land mass 204,462.9 km2 accounting 18.26% was improved, while, 328,213 km2 accounting 29.31% was degraded and land mass of 555,726.8 km2 accounting 49.63% was remained stable. Result indicated that specifically tree-covered area exhibited the highest productive land by 85,126.99km2, while the least land improvement was exhibited on artificial land by 95.40 km2. Similarly, grass land exhibited the highest stable land by 97,547.88 km2, while the least was exhibited on water bodies by 184.90km2. On the other hand, grass land exhibited the highest stressed land by 97,547.88 km2, while the least was exhibited by water body 184.90km2. Grass land exhibited the highest declined land by 37,936.88 km2, while the least was exhibited on artificial land by 98.88km2. Even though; Ethiopia has made progressive land restoration intervention through sustainable forest/land management, the proportion of improved land is 18.26% which is much lower than the area proportionally degraded by 29.31%. There was fast trend of urbanization in rural area with drastic population growth where large area is being under pressure due to degradation which implies more efforts is needed in reversing land degradation and increase the productivity for increasing the biological and economic return from land. Therefore; additional sustainable forest management strategies and programs are needed to be put in place to bring more land under improved in productivity in line with meeting the national land degradation neutrality targets of Ethiopia by 2030; that ultimately reverse land degradation and combat desertification.

Woody species dynamics in Sheka Forest Biosphere Reserve, Southwest Ethiopia

Understanding the dynamics of woody species diversity is the foundation for designing sustainable biodiversity management strategies. Despite an extensive history of cross-sectional ecological studies, scientific gen on the dynamics of woody species diversity over time and space is rare and mostly unexplored in the last remaining Eastern Afromontane Forests. This study aimed to examine the spatial and temporal dynamics of woody species composition, structure, and diversity in the Sheka Forest Biosphere Reserve (SFBR). Data were collected from a total of 43 permanent plots with a dimension of 10 m × 50 m that were laid along the altitudinal gradient. In both 2000 and 2021, the same permanent plots were used and the same approach was followed. Diversity indices and Wilcoxon paired-sample test were employed for the comparison of changes. One hundred one woody species that belong to 84 genera and 41 families and 62 woody species that belong to 54 genera and 32 families were recorded in 2000 and 2021, respectively. During the present study, 47 woody species from the 2000 inventory list including three endemics were not encountered, while eight new species were recorded. The aggregated density and total basal area of woody species decreased from 2590 ± 536 ha−1 to 2454 ± 300 ha−1, and 101.3 m2ha−1 to 72m2ha−1, respectively. The dominant woody species was Ficus sur in 2000, while it was shifted to Coffea arabica in 2021. Woody species richness, evenness, and diversity significantly reduced during the study period. In the mid and lower altitudes with lower slope gradients, where settlement and agricultural expansion were prevalent, the greatest decline in woody species richness, diversity, and evenness, and highest turnover were recorded. Unsustainable use for construction, energy production, creation of job opportunities (income generation), as well as the conversion of forestland to farmland resulted in the rapid decline and massive turnover of woody species diversity. It is important to develop and execute comprehensive sustainable forest management strategies, which include diversification of energy sources and use of energy-saving technologies, strengthening tree planting and management practices for household use and income generation, and diversification of eco-friendly livelihood options. The conservation of endemic and priority species is indispensable to maintaining ecosystem integrity and species diversity.

Sustainability Strategy for Industrial Plantation Forest Management in Riau Province, Indonesia

Riau Province is known as an area rich in forest resources. Most of the forest area is used for industrial plantation forest management. The existence of this industrial plantation forest also supports the forest product processing industry and other industries. This study aims to develop a strategy for the sustainability of industrial plantation forest management based on 4 dimensions of sustainability. To develop a sustainable strategy for industrial plantation forest management, Multidimensional Scaling (MDS) analysis is used with the help of RapHTI (modified Rapfish) software and prospective analysis. The results showed that the sustainable management of industrial plantation forests is an effort to accelerate national development from the ecological, economic, social, and institutional aspects. Strategies for empowering rural communities, strengthening village institutions, strengthening inter-institutional cooperation, and optimizing economic benefits are strategic steps in realizing sustainable management of industrial forest plantations. The proposed strategy for sustainable industrial plantation forest management is in line with the sustainable development goals (SDGs) in Riau Province, Indonesia.

Assessing changes in soil quality between protected and degraded forests using digital soil mapping for semiarid oak forests, Iran

Soil quality, defined as the capacity of a soil to function, is one of the most important characteristics of soil. Methods for modelling and monitoring soil quality are needed for sustainable soil management and evaluating soil degradation. In Iran, resource demands have led to the deforestation of the mixed semiarid oak forests; however, the impacts of these activities on the spatial patterns of soil quality remains unclear. This study calculates a soil quality index (SQI) from an integrated suite of soil biological, physical, and chemical properties and compares the SQI between a paired degraded/deforested area and a protected forested area in Iran using a digital soil mapping (DSM) approach via geostatistical and machine learning techniques. Here, 50 soil samples were acquired for each of the degraded/deforested and protected forested areas, whereby 14 soil attributes were measured. Results showed that the soil organic carbon, total nitrogen, available potassium, cation exchange capacity, pH, clay, saturated water content, and basal respiration in the protected area were significantly higher than the degraded forest area. Furthermore, the soil quality in the protected area was substantially higher than the degraded area. To select the best modelling approach for mapping SQI, machine learning approaches using regression tree (RT), artificial neural networks (ANN), and Random Forest (RF) models were compared against geostatistical approaches using inverse distance weighted interpolation, global polynomial interpolation, radial basis function interpolation, local polynomial interpolation, and kriging (ordinary, simple, and universal). Of the machine learning techniques, the RF model (R2 = 0.66) outperformed ANN and RT, while Universal Kriging outperformed all geostatistical approaches (R2 = 0.71). By comparing the SQI maps between the degraded/deforested and protected forested areas, the soil quality was substantially higher for the protected areas. This study demonstrates a framework for assessing the impacts of deforestation on the spatial patterns of soils using DSM techniques, which will facilitate effective land use planning and sustainable forest resource management strategies.