User’s opinion in scientific forest management implementation in Nepal – a case study from Nawalparasi district

Abstract

While much emphasis has been placed on scientific and policy issues in forest management, there is a lack of clarity on users’ perspectives concerning the implementation of scientific forest management. To clarify this nuance, this study explores users’ opinion on scientific forest management implementation, focusing on four criteria—ecological, social, economic, and technical. Twelve key informant interviews and six focus group discussions were conducted in three selected communities within Nawalparasi District. This was further complemented by six expert interviews. The analytic hierarchy process (AHP) was employed to rank within and between groups of developed factors and their criteria. Users’ identified increased forest products (58%), community development (55%), employment opportunities (65%), and intense silviculture management (51%) as the dominant factors linked to the ecological, social, economic, and technical criteria, respectively. For between groups, economic (52%) and social (33%) criteria got the highest ranking. The findings suggest that the long-term success of this modality cannot be achieved if the users largely view it as economically and socially profitable. This study calls for tailor-made interventions to enhance ecological and technical knowledge linked to scientific forest management. The paper also makes a succinct request for further studies (including quantitative investigations) to ground this assertion.

Keywords:

• technical forestry
• criteria
• analytic hierarchy process
• community users
• Terai

PUBLIC INTEREST STATEMENT

Community forest users are the key front-line implementers of community forest management in Nepal. A number of debates have ensued with regards to the implementation of scientific forest management (SciFM), including the technical and timber centric approach after it is introduced in community forest user groups in Nepal. In this study using four criteria (ecological, social, economic, and technical), we sought to explore users’ perspectives on SciFM implementation. We found that SciFM intervention in community forests is largely driven by economic and social concerns and interests, rather than ecological concerns. Equally, it has been presented as intense Silviculture management that does not match with the capacity of users. While users are the key managers of forests in community forestry, without their active and meaningful involvement, SciFM science would not register success. The paper suggests the need to incorporate community needs and interests while ensuring their participation in the equitable and efficient implementation of SciFM.

Competing Interests

The authors declare no competing interest.

1. Background

Forests constitute the most reliable source of natural resource that provides benefits to the local people of Nepal (Joshi et al., 2018; B. K. Pokharel et al., 2007). More than 40% of the country’s landmass is covered with forests (DFRS, 2015), albeit it challenging management approaches. Nepal has a long history of decentralized forest management involving local communities (Chhetri et al., 2012; Kimengsi et al., 2019). It is one of the pioneer countries to institute community forestry (Pokharel, 2009) and sustainable forest management is the stated aim of its community forestry program (Acharya, 2002). The Forest Act of 1993 captures the devolution of bundle of rights to community forest (CFs) users as expressed through the Operational plan and the constitution. While the constitution defines the rules and regulations for the users, the plan suggests the activities and inventory that should be employed inside the forests (Rutt et al., 2015). Moreover, operational plans (OPs) play a vital role in shaping forest management practices and dynamics (Toft et al., 2015). Users, therefore, have the responsibility to follow the rules and apply collective action to execute the prescribed activities in the OPs. These approved documents are binding for the users with regards to the provision of product utilization, benefit-sharing mechanism, and management activities. Users in community forests are central stakeholders who are managing the community forests since its inception. They are the community who live in close proximity to the forests, and can manage the forest effectively (Brosius et al., 1998). The users form the community forest user groups who collectively engage in the management of forest, making them key actors in the functioning of community forests (Negi et al., 2018).

In Nepal, community forestry has been a major approach to fulfill the basic needs of forest users. However, this management system was highly skewed towards the conservation of the forests rather than its active management. The use of a similar management practice under different forest conditions created more obstacles in the process of sustainable forest resource management (Larson et al., 2010). Furthermore, actively managed forests contribute to biodiversity conservation and sustainable management, which requires an in-depth study of ecological responses with Silviculture interventions (De Avila et al., 2015). Owing to this, a passive management system became commonplace leading to degradation. In response to this, the government launched the scientific (active) forest management (SciFM) approach in a bid to enhance sustainable forest management.

A revised forest policy in 2000 laid the foundation for scientific forest management. SciFM is a Silviculture system-based forest management involving aspects such as shelterwood system, selection system, coppice system, and seed tree systems. In Terai sal (Shorea robusta) forest, it has been applied through the irregular shelterwood system (Joshi et al., 2018). The forest area is divided into eight periodic blocks assuming the 80-year rotation age and 10-year regeneration interval (Subedi et al., 2018), and treated under irregular shelterwood system. Different activities are carried out in different blocks; for instance, regeneration felling, intermediate felling, and final felling are conducted in one periodic block, whereas in other blocks thinning and cleaning are conducted. The main activities in SciFM are mother tree selection, tagging, harvesting, thinning, fencing, cleaning, weeding, and fire line development, among others. SciFM was officially implemented from 2012 with the guidelines formulated in 2014 (GON, 2014; Poudel et al., 2017). At present, it has been implemented in 285 community forest user groups (CFUGs) across Nepal (Baral & Dhakal, 2018). Scientific forest management is perceived to have contributed to varying outcomes in terms of the ecological condition of the forest, social development, economic outcome, and technical use. From an ecological perspective, regeneration and community-level species richness for seedlings and saplings increase under active management systems (Subedi et al. 2018). Besides, active management requires the accurate measurement of the forest variables with exact growing stock and annual allowable cut (Rutt et al., 2015). This suggests the use of technology in the management modality. The activities, if followed in the prescribed manner, would benefit the forest with improved regeneration (Malik & Bhatt, 2015), greater species diversity and richness (Poudyal et al., 2020), and enhanced carbon sequestration (Bluffstone, 2018; Luintel et al., 2018), leading to sustainability (Pokharel et al., 2015).

Many scholars define scientific forest management as a tool for the centralization of forests in Nepal (Basnyat et al., 2018a; Hull et al., 2010; Sunam et al., 2013). When technical knowledge dominates the context, forest users, most of whom do not master the technology, are rendered inactive—this increases the power of certain actors (Krott et al., 2014). Moreover, different policies of Nepal are accused of having contributed to increase knowledge dominance, leading to the centralization of forests (Baral et al., 2018; Basnyat et al., 2018b).

While forest dynamics must be considered during the execution of technocratic management practices, the role of the community and community-based institutions must be considered to enhance sustainability (Kimengsi & Balgah, 2017). However, the application of complex methods and techno-centric knowledge on almost every aspect of forest management undermines forest users’ participation. Ultimately, in the long term, this will exclude forest users from the management process (Basnyat et al., 2018b). Furthermore, the SciFM policy seemingly undermines the principle of sustainable forest management (Joshi et al., 2018). Several scholars present opposing arguments regarding different aspects of SciFM implementation. For instance, Paudel et al. (2018) label it as a “program” in the name of active utilization implemented without considering institutional aspects. They argue about its outcomes claiming that outcomes are limited on the ground. Basnyat et al. (2018a) describe it as the madness of Silviculture imposing new policy restrictions to users. Rutt et al. (2015) question the technical management plan arguing it as flawed and suggests that it should be subjected to sufficient scrutiny, in the context of participatory forestry. Baral et al. (2018) and Basnyat et al. (2018b) consider it as a complex mechanism which rather promotes centralization that disfavors forest users. While much emphasis has been placed on scientific and policy issues linked to active forest management and users’ participation, there is a lack of clarity regarding users’ perspectives on the implementation of scientific forest management. To contribute to clarify this nuance, this study explores users’ opinion on scientific forest management implementation, focusing on four criteria—ecological, social, economic, and technical. The results are relevant for the (re)orientation of research and practice linked to scientific forest management in Nepal.

2. Materials and methods

2.1. Study area

The study was conducted in Nawalparasi District that lies in the inner Terai region of Nepal. Terai is located in a sub-tropical climatic zone characterized by hot and humid summers, intense monsoon rain, and dry winters. Out of the total land area (2,016,998 ha) of the Terai region, forest covers 411,580 ha (20.41%) of the total land area (DFRS, 2014). The forest in the Terai is dominated by productive hardwood sal (Shorea robusta) forest. In terms of population, Terai region accounts for more than 50% of the population of Nepal (CBS, 2012). The study district is an exemplary district in the implementation of SciFM in CFs of Nepal. Till date, 47 CFs have adopted SciFM practice in the district. This has become the learning site for other CFUGs in neighboring districts who are replicating the practice. We selected three CFs for our study, namely, Janajaagaran, Madhyabindu, and Nawadurga community forest user groups (Figure 1). The selected forests are different in terms of area and number of users (Table 1). However, the forest type and geographical features are almost identical. The selection of the CF was done considering the implementation of scientific forest management with more than three-year and having a forest area of more than 100 ha under scientific forest management. Thus, three samples of CFs were selected randomly, by narrowing down the population from the above condition. All the selected community forest user groups consist of heterogeneous communities with different castes and socio-economic conditions. Most of the users are forest dependents mostly for timber and fuelwood. Majority of the households are engaged in agriculture. Regarding their knowledge of SciFM, key leaders, including members of executive committee, are aware of SciFM implementation while a majority of the general users are not aware of it.

Figure 1. Map of the study area.

Table 1. Description of study sites

S.N.	Particulars	Janajaagaran CFUG	Madhyabindu CFUG	Nawadurga CFUG
1	Address (VDC-ward)	New Belhani-7	Kawasoti-16	Kawasoti-2
2	Date of handover of SciFM Plan	2073–01-12 BS (April, 2016)	2073–10-28 BS (January, 2017)	2073–10-28 BS (January, 2017)
3	Total Area (hectares)	232.87	369.23	151.41
4	Area under SciFM (hectares)	113.38	359.37	148.26
5	Household number	278	717	317
6	Total population	1522	3383	1666
7	Forest type	Mixed sal forest	Mixed sal forest	Mixed sal forest
8	Major forest species in CF	Shorea robusta, Terminalia tomentosa, Tectona grandis	Shorea robusta, Terminalia tomentosa,	Shorea robusta, Tectona grandis, Dalbergia sissoo

(Source: Field survey, 2019)

2.2. Data collection

2.2.1. AHP analysis

The prioritization of measured direct observations or judgements in decision-making requires the use of relative scale (Saaty, 1990) and AHP is one kind that uses relative scale. AHP compares the criteria (and within them factors, if any) within as well as between pairs, focusing the concentration on only two criteria at a time (Saaty and Vargas, 2012; Saaty, 1990). This makes the final decision more accurate. Hence, our study used this method to obtain users’ decisions in SciFM implementation. For our study, the criteria were ecological, social, economic, and technical outcomes of scientific forest management. We used the process of AHP to rate the above criteria quantitatively based on their importance. This method has been employed in several studies (Joshi et al., 2018; KC et al., 2014). Twenty key-informants of different categories (five from each category of researchers, NGOs, government officials, academicians) were selected for developing the potential factors of the criteria for the users through structured email-administered questionnaires, surveys, and phone call interviews. The questionnaires included their views regarding ongoing SciFM, the factors for the different criteria, and some future recommendations. Only 12 key informants, 4 researchers, 3 NGOs, 3 government officials, 2 academicians, granted the interviews. Similar factors were merged as one factor for group discussions to form a list for all potential factors of each criterion. This list was further modified and verified by reviewing the existing literature and reports on scientific forest management.

Figure 2. Methodological flow chart.

After the identification of the factors, focus group discussions were conducted. We conducted six focus group discussions—two in each user group for criteria selection and within-group comparisons (Figure 2). The focus group consisted of 10−15 people with mixed participants representing gender, castes, well-being ranking, and leadership in CFUGs. These CFUGs were contacted in advance to schedule a meeting and were prior informed on the context of the research and discussion process. In each focus group discussion, the potential list of factors was made available through a predefined format to users with the explanation of work and method. Subsequently, participants were requested to participate in the discussion on selecting the factors from the preliminary site-specific list. After that, the final list of four factors inside each criterion was developed with the condition of the dominancy of preliminary factor in the three forests (Table 2). The higher the number of users who select the factor, the more the chance of being selected on the final list. The factors receiving equal numbers were contextualized with the other users, and a consensus of selection was made. Then, we prepared for final focus group discussions with three selected community forests. We asked them to join us at a single venue of Janajaagaran CF with the same people that were at the discussion for the preparation of the final list. The consensus of selecting the factors was also made at that time. Then, the users were divided into groups following their community forests, and requested to make pairwise comparisons within the same category of criteria, using the 9-point Likert scale (Saaty, 1977).

Table 2. List of final selected factors with their respective criteria

Criteria	Selected factors
Ecological	Loss of forest biodiversity
	Increased forest produce
	Increased environmental risk
	Reduced fire and risk of hazards
Social	Community development
	Corruption
	Lower user involvement
	Inadequate efficient workers
Economic	Employment opportunities (wood-based)
	Increased cost on maintenance of forest
	Increased income of user groups
	High cost on extraction
Technical	Lack of technology for harvesting
	Based on science and sustainability
	Intense silvicultural management
	Loss of traditional management system

We also requested users to provide their views on selecting such factors vis-a-vis others. Comparisons were based on the importance of one factor over another and from this; the local priority value of each factor within criteria was developed. Four most important factors that have the highest local priority value (one from each category) were selected for the comparisons between categories to determine the scaling factors. The overall priority for each factor was determined by multiplying the scaling factor and local priority value. Similarly, the six expert interviews were carried with academicians and forest officials on each of the criteria and factors. This provided more evidence to further buttress the results and discussion.

2.3. Data analysis

The analysis of the data obtained was done based on Saaty (1977). Eigenvalue method was used to calculate the local priority value of each factor of the entire set of categories. In this method, these values are represented in reciprocal matrix (Equation 1(1) $A=a_{ij}=\left(\frac{w_1}{w_1}\frac{w_2}{w_1}\frac{w_1}{w_2}\frac{w_2}{w_2}\dots .\dots ..\frac{w_1}{w_n}\frac{w_2}{w_n}\dots \frac{w_n}{w_1}\dots .\frac{w_n}{w_2}\dots .\dots .\dots .\frac{w_n}{w_n}\right)$(1) ) to calculate the relative priority, where the relative weight of factor is entered as “a_ij” and the weight of relative priorities “w” in the matrix.

(1) $A=a_{ij}=\left(\frac{w_1}{w_1}\frac{w_2}{w_1}\frac{w_1}{w_2}\frac{w_2}{w_2}\dots .\dots ..\frac{w_1}{w_n}\frac{w_2}{w_n}\dots \frac{w_n}{w_1}\dots .\frac{w_n}{w_2}\dots .\dots .\dots .\frac{w_n}{w_n}\right)$(1)

The overall priority of factor was calculated by the following (Equation 2(2) $Overallpriorityoffactor=localpriorityvalueoffactor\times ScalingfactorofSWOTcategory$(2) ) formula.

(2) $Overallpriorityoffactor=localpriorityvalueoffactor\times ScalingfactorofSWOTcategory$(2)

This overall priority value determined was used to rank the factors acquired, based on their importance. The total sum of overall priority value was one and the highest value was assigned the highest importance. Since more than one focus group discussion was carried out, the AHP weightings were calculated from geometric mean method (Xu, 2000). As the AHP does not depend on the sample size (Saaty, 1977), it was important to calculate the consistency ratio (CR) of the results (Equation 3(3) $CR=\frac{CI}{RI}$(3) ) to ensure the reliability of the AHP findings (Kurttila et al., 2000). We accepted the consistency ratio ≤10% and the ratio more than 20% re-examined before making the judgement (Margles et al., 2010). Moreover, between this, it was received as a tradeoff judgement. The calculation of consistency ratio was from

(3) $CR=\frac{CI}{RI}$(3)

where CR = consistency ratio;

CI = consistency index;

RI = Random consistency index.

The CI (Equation 4(4) $CI=\frac{{\lambda }_{max}-n}{n-1}$(4) ) was calculated from

(4) $CI=\frac{{\lambda }_{max}-n}{n-1}$(4)

where ${\lambda }_{max}$ = the highest eigenvector value and

n = the number of factors on which comparison is made.

And, RI (Table 3) was used as developed by (Saaty, 1977).

Table 3. Random consistency index values

n	1	2	3	4	5	6	7	8	9	10
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49

Here, n = number of factors used for comparison and RI = random consistency index.

3. Results

3.1. Priorities within criterion

We got different rankings on each of the factors in each criterion from different focal group discussions drawn from the three different CFUGs. We then calculated the geometric mean for each factor for within criteria and for across criteria to situate the user's opinion on scientific forest management implementation. Table 4 shows the factorial priority of different factors inside their respective criteria, scaling factors (across group comparison), and global priority (each factor’s priority with respect to entire 16 factors) analyzed through the Analytic Hierarchy Process.

Table 4. Different factors with their values (λ_max = maximum eigenvalue, CR =consistency ratio)

S.N.	Factors	Factor priority	Scaling factor	Overall priority
	Ecological criteria (${\lambda }_{max}$= 4.191), CR =0.07		0.095
A1	Loss of Forest Biodiversity	0.11		0.010
A2	Increased Forest produce	0.58		0.055
A3	Increased environmental risk	0.07		0.007
A4	Reduced fire and risk of hazards	0.24		0.023
	Social criteria (${\lambda }_{max}$=4.234), CR = 0.089		0.332
B1	Community development	0.55		0.183
B2	Corruption	0.12		0.040
B3	Low user involvement	0.28		0.093
B4	Inadequate manpower (efficient)	0.05		0.017
	Economic criteria (${\lambda }_{max}$= 4.205), CR = 0.08		0.519
C1	Employment opportunities	0.65		0.337
C2	Increased cost on maintenance of forest	0.05		0.026
C3	Increased income of user group	0.17		0.088
C4	High cost on extraction	0.13		0.067
	Technical criteria (${\lambda }_{max}$= 4.202), CR = 0.074		0.054
D1	Lack of technology for harvesting	0.07		0.004
D2	Based on science and sustainability	0.11		0.006
D3	Intense Silviculture management	0.51		0.028
D4	Loss of traditional management system	0.31		0.017

3.1.1. Ecological criteria

In terms of ecological criteria, users view the increase in forest produce/products (58%) as one of the key ecological outcomes of scientific forest management implementation followed by a reduction in fire and hazard risk (24%). Loss of biodiversity and increased environmental risks were prioritized as low (Table 4 and Figure 3). Usually, in the process of implementing SciFM, large numbers of trees are harvested that yield high amounts of timber and fuelwood (Poudel et al., 2017). Notably, tree harvesting increased after SciFM implementation in community forests and this signals an improvement in forest conditions (Gurung et al., 2013). Besides, SciFM has been fulfilling the timber and other forest product demands of users compared to traditional forest management (Khanal, 2017).

Users identified reduced fire and the risk of hazards as the second major outcome of scientific forest management. This might be due to the development of fire lines at the onset of implementation as well as due to the regular monitoring of the forest. However, they mentioned that these risks, which had been in place intentionally or unintentionally, are zero after the implementation of SciFM. They do not see the loss of forest biodiversity in terms of species. Most of the time after regeneration felling, mixed sal forests are transformed into pure sal forests, due to its high value (Banjade, 2012). However, in contrast to this, one of the members of CFUG explained, “Shorea robusta is not the only species that is selected for mother trees, other associated species inside the forest are also chosen as they pass the necessary requirement for the selection of mother tree”. Furthermore, rotation age of these associated trees cannot be ignored. At last, increased environmental risks got the lowest priority.

3.1.2. Social criteria

Community development received the highest priority (55%) as a within-group factor. CF’s contribution to community development is mirrored through the establishment of community infrastructures since its inception (Bhandari et al., 2019; KC et al., 2014). Furthermore, after the implementation of SciFM, increases in incomes of CFUGs ultimately contributed to community development. Though this management requires substantial engagement of the users, user ranked decreased user involvement after SciFM as a second (28%) among the factors (Figure 3). This supports the argument of Paudel et al. (2018), who claims that scientific forest management implementation requires complex and more technical knowledge that will decrease the participation of users in the decision-making process. In the same line, one of the CFUG committee members explained that:

Before SciFM, we used to look after and decide what is required for forest management based on our knowledge and as per the operational plan. In some cases, we used to ignore what is written in the OP against what is required for users. However, after the introduction of SciFM, we have to do what is written and prescribed in the operational plan without any modification. Equally, forest technician’s verification and decisions are required in most of the activities. This has decreased users’ involvement in forest management decisions making.

This has been explained by Krott et al. (2014) who construe it as actor-centered power in terms of dominant information. The claim that foresters have more knowledge on the forest management than users remains an issue of contest, as in many cases, it has relegated users to the background, leading to behavior alteration for users. In addition, participatory forestry with the incorporation of scientific principles reduces the participation of communities; hence, it can be called a paradox (Lund, 2015). Furthermore, many scholars define scientific forest management as a techno-bureaucratic dominant scheme that is imposed on users (Basnyat et al., 2018b; Rutt et al., 2015; Sunam et al., 2013), which will recentralize the forest rights in the long run. Users ranked corruption as the third major factor. Corruption has been the most dominant when there are harvesting and sale of high-valued timber (Banjade, 2012). This result resonates the results of Joshi et al. (2018), who also explained that government officials do not consider this as a major weakness of SciFM implementation. Inadequate manpower has been least prioritized in case of harvesting, logging, transporting, and accounting as most of the community forest committees are well trained to carry out most of the forest management activities or they can easily manage it.

3.1.3. Economic criteria

In the economic criteria, users prioritized employment opportunities as an important outcome of scientific forest management. As scientific forest management demands a large number of workers for harvesting and transportation, it generates short term and long-term employment (Khanal, 2017; Khanal et al., 2017) with most of the users being involved in non-skilled labor during the harvest season. One CFUG committee member mentioned, “For cleaning activities only, nearly 20–30 users go to the forest on daily basis. This number increases, as other management activities like cleaning, weeding, lopping, thinning, pruning, and harvesting is carried in the forest. Employment opportunities increased substantially after SciFM implementation. We provide cash remuneration to the users on daily basis”.

Providing cash to the people suggests the changing direction of CF, from a voluntary mechanism to economic-centered approach. Furthermore, the processing and seasoning of a large number of harvested trees and the development of lumber also increase employment outside the CFUGs, through the establishment of sawmills. Users ranked increased income of user groups and the high cost of extraction in the second and third positions, respectively. These close values signify equal importance of cost on extraction with the income of forest users, supporting the view that SciFM potentially contributes to income provision for user groups. However, they have least prioritized the increased cost on forest maintenance, which advocates their knowledge on the expenses and cost on the maintenance that will multiply annually with constant benefits. This might be due to the implementation of SciFM at an initial stage, thereby contributing to short-term benefits in terms of income.

3.1.4. Technical criteria

Intense Silviculture management was identified as the most important factor followed by the loss of traditional forest management system. However, in terms of sustainability and technicality, users shared it as minor factors. The SciFM modality has been initiated as an intense Silviculture system that requires knowledge that is more technical. In this context, users shared that the introduction of Silviculture science in forest management could increase the role of technicians and could result in the loss of traditional management practices which they used to follow before.

Interestingly, they identify the technical knowledge of this system to be the lowest factor within technical criteria. Succinctly, one of the CFUG members described, “We are familiar with the idea of scientific forest management. Though it required more Silviculture activities than before, if rightly trained, we can carry all required so called technical activities”. This implies that if users are provided with knowledge on scientific forest management, they can learn and implement it as guided by the operational plan.

Figure 3. AHP analysis among different factors within four criteria.

3.2. Priorities between criteria

From the analysis of within-group priorities, we selected foremost-prioritized factors, namely, increased forest produce (ecological), community development (social), employment generation (economic), and intense Silviculture management (technical). Users identified employment generation as the most dominant factor (52%) across groups. Furthermore, most part of this management mechanism has been around income generation through timber sale from mature and over-mature trees that provide a support for the selection of economic criterion as dominant criteria across groups for SFM implementation. Following this, community development got the second ranking across groups (33%). Although the mechanism is costly, however, the income generated from the management modality has been playing a crucial role in community development.

Figure 4. Priority between criteria.

Users identified increased forest produce and intense Silviculture management as third (10%) and fourth (5%) dominant factors in SciFM implementation (Figure 4). The result point to the fact that SciFM focused more on the economic and social aspects, and largely ignored the ecological and technical aspects.

3.3. Overall priorities

All the 16 factors were ranked together to determine their ranking among every other factor (Figure 5). From the calculation of global priority, employment opportunities became the dominant among the 16 factors with 33.7% overall priority, followed by community development that accounted for 18.3%. When combined, 52% of the overall priorities go with only these two factors. Hence, this concludes that users’ perception regarding SciFM implementation is positive. Priorities of the other factors are below 10%, indicating that users have focused on the tangible benefits of the SciFM.

Figure 5. Overall priority.

4. Discussion

The three different layers of the analysis show users’ opinions in scientific forest management are highly skewed towards economic benefits. For within factor, employment opportunities got the highest ranking by the users with 65% of the user’s opinion on it. It is followed by increased forest produce (58%), community development (55%), and intensive silviculture intervention (51%). Similarly, between the four criteria, economic criteria were ranked highest by the user SciFM outcomes with 52% of users’ opinion on it. The ecological (10%) and technical (5%) criteria were less prioritized by the users during the implementation of SciFM. In addition, the overall analysis of the priorities also puts economic and social outcomes of SciFM implementation in the user’s perspective. Our qualitative results resonate with the quantitative results of Subedi et al. (2018) and Khanal et al. (2018), that the implementation of SciFM increased forest products harvesting, contributed to generate employment opportunities and enhanced investment in community development activities in the user groups. Nonetheless, users perceive it as an intense Silviculture intervention in forest management, which has largely ignored the traditional management system.

This shows that SciFM implementation in CFs of Nepal is driven by economic and social concerns ignoring the ecological aspects and laden with the technical science in user’s perspectives. The results resonate the findings of Basnyat et al. (2018a); Poudel et al. (2017) who equally observed that the SciFM has focused on timber harvesting and employed technical aspects which could be a burden to the forest users and disfavors forest users (Baral et al., 2018). Additionally, Poudyal et al. (2020) observe that though SciFM supports increased income and employment there are more risks associated with this approach particularly to maintain post-harvesting management and it is more critical in low income and ineffective CFUGs with bad governance performance. However, De Avila et al. (2015) held a view that actively managed forests contribute to biodiversity conservation and sustainable management, which requires an in-depth study of ecological responses with silviculture interventions. The SciFM initiation in Nepal is steered by the emphasis of traditional forest management on the harvesting of the dead, dying, and deformed trees only. Which has resulted in the domination of forest by over-mature trees with a lack of proper age class (Yadav et al., 2009). Equally, this has limited the forest product supply to local users resulting in the export of millions of hectors of timber from abroad and ecological loss of the forest (Subedi et al., 2018). The failure of managing the Terai forest scientifically has been considered a lost economic opportunity (Joshi et al., 2018). Similarly, there is acknowledgment among the stakeholders that the protection-oriented forest management cannot bring the needed economic prosperity in the country (Banjade, 2012). Thus, this context has resulted in SciFM implementation in CFs much concerned with timber harvesting (Basnyat et al., 2018b; Poudel et al., 2017; Subedi et al., 2018). Equally, the benefits around timber are huge (Banjade, 2012) that has been increased massively after SciFM implementation in CFs (Joshi et al., 2018). The users after SciFM implementation get a considerable amount of timber and they invest time for getting timber from the forest. However, Basnyat et al. (2018a) have argued that this has decreased user’s participation in decision-making activities. Likewise, users are getting less opportunity to involve in key bio-physical activities in SciFM like mother tree selection, tagging, harvesting, thinning, fencing, cleaning, weeding, fire line development, regeneration felling, etc. (Basnyat et al., 2018a). Thus, the focus on economic and social aspects could imply the rise or sustenance of powerful and dominant economic actors who further capture decision-making and benefits linked to SciFM in community forestry. Equally, this may lead to poor governance of CFUGs resulting in the CFUGs concern only on the direct economic incentives rather than investing in sustainable outcomes. On the similar note, the recent study by Basnyat (2020) discusses that the economic rationale has been smartly used by forest bureaucracy in Nepal to encourage users in SciFM implementation through incentivizing CFUGs towards timber production while ignoring the user’s needs and their ability. Equally, he argues that commodification of timber will generate profit while it could relegate the poor and forest dependents reducing their access and increasing domination of elites.

On the other hand, users seem unaware of the negative impacts of the SciFM like multiplying cost with respect to constant benefits, high environmental risk, corruption around the sale and harvesting of timber by the powerful actors, and technicality of the modality (Banjade, 2012; Basnyat et al., 2018b). Similarly, the forest technicians also argue it as their subject that needs their technical knowledge and skills rather it is not the users’ business. This might also be the opportunity for forest technicians to decrease the timber import. However, they have to be equally concerned about the ecological consequences and users’ needs and interests. There are questions on forest technicians concerns on timber around SciFM. Banjade (2012) explains it like the discussion among the technicians (especially foresters) is about timber trade and dealing with traders and related actors rather than the ecological condition/diversity or environmental services of the forest. Moreover, these things distorted in the communication of mass media influence the public (users) perceptions (Banjade, 2012). While the advantage of SciFM is seen higher with respect to timber harvesting by users it is seen as neglecting forest biodiversity among stakeholders (Poudyal et al., 2020). Similarly, the domination of technical knowledge over local knowledge and practices is visible in SciFM. As most of the literatures (Acharya, 2002: Bhandari et al., 2019; Chhetri et al., 2012) showed that community forestry was successful in improving ecological functions, with little emphasis on income benefits, their equitable distribution for the forest-dependent groups. However, SciFM implementation on CFs has raised the concerns on neglecting biodiversity among the stakeholders (Poudyal et al., 2020). The threat of ecological loss among the stakeholders is equally increasing.

This clearly visualizes the framing of SciFM implementation by the forest technicians and policymakers to users as a highly beneficial forest management system where they can get fast benefits like forest products (e.g., timber) and employment opportunity. The SciFM initiation in CFs was more guided by the economic and social benefits that have been always the awaited scheme for CFUGs since the past 40 years, which they have invested in forest conservation and development. This has been injected and taught and what they perceive from different actors, as dominant information (Krott et al., 2014).

Overall analysis shows that users see SciFM approach as an active forest management that increases forest product supply, generates employment, and increases the annual income of CFUGs. This consequently increases investment in social and community development activities. However, they seem less knowledgeable about the ecological and biodiversity concern, which is equally discussed by Poudyal et al. (2020). On the other hand, the investment of increased income seems largely diverted to community and infrastructural development rather than on forest development and management. This may reduce the investment in stand management, which will increase as SciFM implementation goes further.

The experts shared that the application of SciFM has the potential to maximize harvesting of forest products while maintaining the long-term ecological integrity of the forest. There are opportunities to develop long-term markets based on guaranteed harvesting of forest products. Similarly, there is potential to increase the technical skills of user groups to apply SciFM (subject to appropriate capacity building). However, SciFM implementation requires skilled technical input to develop and apply management and harvesting plans. Such skills are generally not available in communities and require substantial input from outside the user groups. In addition, maximizing harvesting of forest products (which is presented as the heart of SciFM) may not be a prime objective of forest users.

On the other hand, the application of SciFM could dilute the authority of user groups to manage CFs and place more power in the hands of technical forest agencies. This could alienate user groups and result in a loss of local interest in community forestry with a perception that the government has “taken away the forests”. This could lead to a loosening of local protection of CFs and an increase in “illegal” harvesting and possibly forest degradation and even deforestation.

5. Conclusion

Though SciFM implementation is a step towards fulfilling increasing timber demand, it seems that it is largely driven by economic and social concerns and interests, rather than ecological concerns. Equally, it has been a highly technical science, with intense Silviculture management that is being presented as beyond the capacity of users. The need for Silviculture science cannot be ignored in forest management. However, it should equally consider the community needs and interests, forest context, and most importantly, the user’s awareness and decisions in its implementation driven by community needs and ecological contexts. Users should be made aware of its ecological and technical science and on the possibility of changing ecological benefits to economic and social benefits. The upgrading of traditional community knowledge and bridging with contemporary science is equally important. Also, the future Silviculture interventions, need of skilled manpower for it, and investment on it are not yet clearly understood. The fast and increased benefits as of now have largely occupied the interest of users and implementers. Thus, we strongly suggest considering the forest context, community forest users’ needs, and simplifying technical science to users' understanding and implementation. Equally, the forest bureaucracy should ensure participatory and accountable governance to better facilitate equitable and efficient forest management. This study only brings user perspectives as a key frontline implementer of SciFM in the community forest of Nepal. There is a need to further test this modality among diverse CFUGs in terms of income, skills, and accessibility, before implementing in all community forests. Equally, the learning for the last 10 years of implementation should be well reflected and acknowledged in SciFM policies.

Additional information

Funding

Open Access Article Publishing Charge (APC) for this article was provided by the Staats- und Universitätsbibliothek Dresden (SLUB).

Notes on contributors

Prabin Bhusal

Prabin Bhusal is an assistant professor of natural resource management, with over seven years of research and teaching experience in community based forestry and forest governance. He currently works in Institute of Forestry, Pokhara Campus Tribhuvan University, Nepal.

Kavi Raj Awasthi

Kavi Raj Awasthi conducts research on community based forestry and forest management at the Institute of Forestry, Pokhara Campus Tribhuvan University, Nepal.

Jude Ndzifon Kimengsi

Jude Ndzifon Kimengsi is Principal Scientist for the CamForst/DFG Project at the Faculty of Environmental Sciences, Institute for Tropical Forestry and Forest Products, TU-Dresden, Germany. He is Associate Professor in Resource and Conservation Geography at the University of Bamenda, Cameroon.