Critical Factors on Forest Land Management in Vietnam

Abstract

The study aims to determine the influencing factors and their impact on the management of the forest land used for production allotted to peasant households and proposes solutions to improve its management. Secondary data was calculated until the end of 2019 at state agencies. The primary data was collected through 2 steps. To determine the factors influencing forest land management for production, step one involved a survey of 100 households that were given access to forest land. Step 2 involved a survey of 215 households to determine how factors impacted the Likert scale's five levels. The study also used a multivariate regression model and SPSS24.0 software to determine the impact rates of factor groups. The study indicated 43 factors that belong to 11-factor groups affecting the management of forest land for production. The policy and legal factors group is the most influential, with an impact rate of 12.72%, followed by 10 other factor groups with impact rates ranging from 4.08% to 11.74%. The solutions include improving policies and laws, strengthening the dissemination of policies and laws; encouraging investment, completing cadastral work, intensifying inspection, examination, and sanctioning of administrative violations of land, upgrading the infrastructure system, and ensuring enough human resources to manage forest land.

1. Introduction

According to the provisions of the land law of Vietnam, the forest land for production is forested land and land currently being used for forest development for the primary purpose of providing forest products; combined production and business of forestry, agriculture, and fishery; ecotourism, convalescence, and entertainment; providing forest environment services (Ministry of Natural Resources & Environment, 2018). In Vietnam, forest land in particular and land in general, belong to the entire people’s ownership, which is represented and managed by the State. Households, individuals, and organizations are not allowed to own land, only use land through land allocation, land lease, or recognition by the State (National Assembly, 2013). The forest land for production allocation quota for each household does not exceed 30 hectares. The term of land allocation to households directly engaged in agricultural production is 50 years. The policy on forest land allocation to households was implemented in the 90s of the twentieth century. Forest land allocation aims to improve forest quality, increase forest cover and contribute to poverty alleviation and improved livelihoods for upland people (Secretariat of the Party Central Committee, 2017). However, in many countries around the world, including Vietnam, there has been a situation of forest exploitation and unscientific use of forest land, leading to a decrease in the area of forest due to indiscriminate exploitation or forest fire, and many species of animals are hunted and endangered (Kusters & Belcher, 2004; Murray & Clapham, 2020; Rantala et al., 2012).

Up to now, there have been many different studies on forest land. Specifically, some studies have focused on assessing the role of forest land and pointed out forest land plays a particularly important role in the socio-economic development of each country (Lasco & Pulhin, 2006; Li, 2002). Some other studies only focused on evaluating community-based management models for forests and forest land (Pulhin & Pulhin, 2003); or analyzing the strengths and limitations of community-based forest management in different locations and proposing solutions to improving the livelihoods of communities and forest use (Gilmour, 2016; Nath et al., 2016).

Some studies focused on assessing the status of forest land use and management (Bopp et al., 2020; Saint-Macary et al., 2010). Other studies focused on assessing the status of forest land managed by limited liability companies (Hautdidier et al., 2018; Xie et al., 2017). Su et al. (2021) studied the influence of forests on soil erosion. Bopp et al. (2020) studied the influence of forests and reforestation subsidies on people’s income. Beygi et al. (2020) researched the influence of armed conflict on forest land and forests. Some other studies focused on the evaluation of changes in biomass carbon stocks in land use management (Olorunfemi et al., 2019).

Yang et al. (2018) researched climate influences on forest cover in areas where there is a large variation in land cover. Milheiras and Mace (2019) assessed the provision of ecosystem services in tropical areas with high forest cover. Schürmann et al. (2020) assessed the relationship between land tenure issues and changing land cover around forests. Meli and Brancalion (2019) researched and proposed solutions to balance land saving and sharing approaches to promote forest and landscape restoration in agricultural landscapes. Ferreira et al. (2019) studied the role of forests in preventing the negative effects of land use and climate change on water ecosystem services. Bose et al. (2017) studied the rights to forests and land of rural women. Coomes et al. (2017) examined tropical forest constraints of nomadic agriculture and the role of tropical forests. Toumbourou (2020) examined issues, interventions in forest management and decision-making, and formalizes land boundaries.

Tuffour-Mills et al. (2020) used a mixed approach to investigate trends and dynamics of land use and land cover change. Rosa et al. (2020) investigated the rehabilitation of recreational use of forest land by landmine rehabilitation. Bicudo da Silva et al. (2020) researched land use and cover in mountainous areas. Ingalls et al. (2018) examined risky commodity trade in forests and land transactions. Pichler et al. (2020) studied the influence of forest conversion on environmental and ecological issues. Ioki et al. (2019) studied land-use planning with the participation of local communities in the buffer zone, to enhance the implementation of the Human and Biosphere Program. Pham and Pham (2014) and Pham (2015) studied community-based forest land management in terms of theory and practice. Research by Bui et al. (2019) analyzed the factors affecting the mobilization of financial resources for the development of natural forests and the advantages and disadvantages related to this issue. Sikor and Ngoc (2007) studied the results of the decentralization of forest land management in the Central Highlands of Vietnam through the allocation of forest land and forests to local people for management. Thuy and Lam (2020) focused on assessing the current status of forest land use allocated to households in two highland villages in Cho Don district, Bac Kan province. Clement and Amezaga (2009) analyzed the gap between policy intentions and outcomes when afforestation and forestland allocation in Northern Vietnam. Pham (2020) studied community-based forest land use and management solutions in Hoa Binh province.

The above studies have assessed one or several issues related to forest land management but have not explored in depth yet the factors affecting forest land management, including the forest land for production. Therefore, this study aims to answer the following questions: What factors affect the management of forest land for production, and to what extent? What policies should be proposed to improve the management of forest land for production in the coming time?

Affecting factors, in general, and factors affecting forest land management, in particular, are components that change things and phenomena, including forest land phenomenon (Fuadah et al., 2020; Hoang & Kien, 2020; Navavongsathian et al., 2020). Forest land management is a type of management that is the influence of the manager on the person being managed to achieve the manager’s goals (Kulchittivej et al., 2020; Potharla et al., 2021). Forest land management in Vietnam is managed by the State with the participation of the people to use forest land effectively and protect the environment.

2. Data and Methods

The study selected Lai Chau province (Figure 1) as the pilot site for the model to determine the factors affecting the management of forest land for production that was allocated to households for management. This is because the largest area of allocated forest land for production (60,998.96 ha) is managed by 5,230 households in the research area. In particular, the management of forest land for production, besides the advantages, still has many difficulties and shortcomings, but up to now, there has not been any in-depth study to address these issues (People’s Committee of Lai Chau province, 2020).

Figure 1: Geographical Location Map of Lai Chau Province

The secondary data on natural and socio-economic conditions was collected at the Bureau of Statistics of Lai Chau province; the secondary data on land management, including forest land management, were collected at the Department of Natural Resources and Environment of Lai Chau Province. The primary data was collected through 2 steps. The first step was to identify factors that might affect the management of forest land for production allocated to households. Specifically, the survey was randomized with pre-printed questionnaires of households that were allocated forest land for production. The content of the survey included basic household information and assessment opinions on hypothetical factors affecting the management of forest land for production. The hypothetical factors were inherited by the other authors from relevant studies and consultation with village elders, village leaders, and experts related to forest land for production management. In addition to the assumptions that were included in the questionnaire, respondents could also add other factors that they think would affect it. The affecting factors selected (Table 1) to investigate their impact rates in the second step were the factors with an evaluation rate of more than 50% of the total number of survey respondents. The number of respondents to the survey was determined by formula 1 (Yamane, 1967).

Table 1: Factor Groups Affecting the Management of Forest Land for Production

\(\begin{aligned}n=\frac{N}{1+N * e^{2}}\end{aligned}\)      (1)

Where: n - number of respondents to the survey; N - number of households that are allocated forest land for production; e - tolerance (e = 5–15%). Choosing e = 10% (mean value of tolerance) and for N = 5,230 households, there is n = 98, rounding n = 100. The survey results show that 43 factors had an evaluation rate of over 50% of the total number of respondents and are divided into 11 groups of factors according to the characteristics of the factors (Table 1).

In the second stage, survey respondents were asked to rate the impact of the first step’s chosen factors on managing productivity on forest land. The level of influence of each factor was assessed on a 5-level Likert scale (Very influential - 5 points; fair influential - 4 points; medium influential - 3 points; little influential - 2 points; very little influential - 1 point) (Likert, 1932).

The multivariate regression model evaluates the influence of groups of factors on forest land for production management as formula 2 (Figure 2).

Figure 2: Research Model of Factors Affecting Management of Forest Land for Production

Y = β₁ * PL + β₂ * NA + β₃ * IN + β₄ * EC + β₅ * HO + β₆ * CA + β₇ * HU + β₈ * MA + β₉ * FI + β₁₀ * IM + β₁₁ * PT + β_o (2)

Where: Y – The dependent variable represents the level of influence on the forest land for production management, which has been allocated to the household; the independent variable represents the influence of forest land for production management; β₁; β₂; β₃; β₄; β₅; β₆; β₇; β₈; β₉; β₁₀; β₁₁: Regression coefficients of the corresponding independent variables are policy and legal; natural; infrastructure; economic; household; cadastral; human; market; financial; implementation; plot factor groups; β_o: Constant; PL, NA, IN, EC, HO, CA, HU, MA, FI, IM, PT: The independent variables, respectively are the groups of policy and legal; natural; infrastructure; economic; household; cadastral; human; market; financial; implementation; plot factors.

The number of samples was determined based on the requirements of the exploratory factor analysis (EFA) and multivariate regression with at least 5 observations for 1 measurement variable (Hoang & Nguyen, 2008). Therefore, with 43 measurement variables, the number of samples was 215. For multivariate regression analysis, the minimum sample size to achieve was 50 + 8 * p (p is the number of variables - p = 11) (Tabachnick & Fidell, 1996), so the minimum number of samples to be surveyed was 50 + 8 * 11 = 138. To ensure both the minimum requirement of exploratory factor analysis and multivariate regression analysis, the survey investigated 215 people.

The survey data on affecting factors and their influences were processed by SPSS24.0 software. The reliability of the scale is verified by Cronbach’s Alpha coefficient. The data ensures reliability when Cronbach’s Alpha coefficients are in the range from 0.60 to 0.95 (Hair et al., 1998), total correlation coefficient > 0.3 (Hair et al., 1998). The exploratory factor analysis is used to shorten many measurement variables into a set of variables (factors) to make them more meaningful but still contain most of the information of the original set of variables (Hair et al., 1998). The EFA is assessed through KMO appropriate coefficient, Bartlett test, Eigenvalue coefficients, total explanatory variance, and load factor. The variables are only accepted when KMO is in the range from 0.5 to 1.0, and its weight factors in other factors are less than 0.35 (Igbaria et al., 1989) or the distance between two load weights (Factor loading) the same variable in 2 different factors greater than 0.3. According to Hair et al. (1998), with a sample size of about 200, weights of 0.40 should be chosen, so for sample size 215, in this study, we choose a load weight greater than 0.40. Besides, the scale is only accepted when the total variance explained (Total variance explained) is greater than 50%; Barlett’s coefficient with a significance level less than 0.05 to ensure the factors are correlated with each other; Eigenvalue coefficients are valued from 1 to ensure the groups of factors are different.

3. Results and Discussion

Lai Chau province is about 450 km from Hanoi capital. By December 31, 2019, Lai Chau province had 456,300 people from 20 ethnic groups, of which: Thai people accounted for 33.5%, H’Mong people accounted for 23.6%, Kinh people accounted for 11.2%, Ha Nhi people accounted for 5.6%, the rest of the other ethnic groups. Labor in the age of working in the economic sectors of the province is 274,610 people, of which urban workers are 45,120 people, accounting for 16.67% of the total workforce; workers in rural areas are 229,490 people, accounting for 83.33% of the total number of employees. The labor force is quite abundant, but the quality of the labor force is not high, especially the rate of trained workers in the agricultural and forestry production sector is low, so the ability to access and apply scientific advances. The technology in agricultural - forestry production in the locality is limited. The total area of rice and corn is 54,475 ha, the total area of tea is 6,183 ha; the area of rubber trees put into exploitation is 3,446 ha (People’s Committee of Lai Chau province, 2020).

The energy industry is the sector that accounts for the largest proportion of the industrial production value of the whole province (accounting for over 90%). The province has 8 tea processing enterprises and 3 cooperatives, in addition, there are small wood processing and manufacturing establishments in the area, mainly producing to serve the needs of the province and some neighboring provinces. Agricultural and forestry product production and processing enterprises are gradually renovating their processing equipment and technology towards applying high technology to improve product quality and value. Tourism service activities continue to grow strongly, and tourism promotion and promotion are concerned. Cultural tourism activities and communities develop rapidly. Some investment projects in tourism have been completed and put into operation, attracting domestic and foreign tourists to visit (People’s Committee of Lai Chau province, 2020).

By the end of December 31, 2019, the area of forest land for production allocated to 5,230 households for management was 60,998.96 ha (Table 2). The percentage of land area certified is 98.70%. Due to the border or land use right dispute that has not been resolved, a portion of forest land (1.30%) has not been certified. The main crops are trees for timber (camellia) and cardamom, and tea (People’s Committee of Lai Chau province, 2020).

Table 2: Results of Forest Land for Production Allocation and Certification

The results of assessing the reliability of the scale through Cronbach’s Alpha coefficients for 11-factor groups show that Cronbach’s Alpha coefficients range from 0.741 to 0.897. The correlation coefficient of the total variable is greater than 0.3 (Table 3). Thus, the scale used to evaluate the factors affecting forest land management is reliable and suitable for subsequent analysis.

Table 3: Results of Reliability Analysis of the Scale

The EFA’s suitability test is done through KMO appropriate coefficient. The research results have determined that KMO = 0.887 and satisfies the condition of 0.5 < KMO < 1, making it appropriate to analyze the discovery factor using actual data. Besides, Barlett test results give Sig values equal to 0.00 and less than 0.05 (Table 4). This proves that the measurement variables are linearly correlated with the representative factor. The load factor of the components is greater than 0.60 (Table 5), so EFA has practical significance. independent variables ensure the accuracy included in the regression analysis model to determine the impact rates of factor groups on the management of forest land for production.

Table 4: KMO and Bartlett’s Test Results

Table 5: Weights of a Rotation Matrix

The results of multivariate regression analysis in Table 6 show the Sig. coefficient equals 0.00 less than the significance level of α = 1%, so the regression model is significant, and the independent variables affect the dependent variable Y. The adjusted R² value equal to 0.801 shows the independent variables affect 80.1% of the change of the dependent variable; the remaining 19.9% is due to non-model variables and random errors. In addition, the Durbin Watson coefficient has a value of 1.791, ranging from 1.5 to 2.5, so no first-order correlation occurs. The variance inflation factor (VIF) of all variables included in the model is less than 2, so the research model does not have multi-collinear phenomena. In addition, the variables included in the study are statistically significant (Sig. equals 0.00 and is less than 0.05). From the standardized regression coefficient, the regression equation has been determined as follows:

Table 6: Results of Regression Analysis

Y = 0.892 * PL + 0.710 * NA + 0.629 * IN + 0.823 * EC + 0.749 * HO + 0.772 * CA + 0.286 * HU + 0.381 * MA + 0.656 * FI + 0.675 * IM + 0.438 * PT – 5.608

The data processing results show that all 43 factors belonging to 11 groups of factors satisfy the test requirements. The degree of influence of the groups of factors on the management of forest land for production varies (from 4.08% to 12.72%) (Figure 3). The group of factors of policy and law has the largest influence (accounting for 12.72%). The reason is that some regulations on land allocation and management of forest land are still limited, such as regulations on land allocation terms and limits, making it difficult for households to access forest land and invest in forest development. Besides, policies to support the production and sale of forest products have not been given due attention. Households have not yet been guided about production in combination with typical product development or tourism. Financial support policies or attracting and encouraging resources to participate in developing production forests are not strong enough to attract investors. In addition, ethnic minorities’ understanding and observance of the law on land and forest protection are still limited due to their low educational level, and limited awareness and understanding of legal provisions. This has a negative influence on the efficient management of forest land.

Figure 3: Influence Ratio of the Factor Groups on the Forest Land for Production Management

The group of factors having the second-largest influence is the group of economic factors with an impact rate of 11.74%. The affecting factors in this group include the source of income, the level of income, and the level of the household’s living standards from the management of forest land. The survey shows that income from using forest land for production is not sufficient for households because income from forests is low and mainly from forest product exploitation. The cadastral factor group has an impact rate of 11.01% and is ranked in the third position due to the problem of cadastral records, the updating of cadastral records on forest land is still not a good, and incorrect determination of the boundaries of many land parcels. Therefore, it has led to disputes, encroachment, and forest land encroachment, causing instability of security and order. In addition, many forest land plots have not been certified.

The group of household factors including the total number of people, the total number of main employees, and the qualifications of the main workers, also influence forest land management with an impact rate of 10.68%. The main reason is that income from forests has not yet secured a living, people lack sufficient qualifications, and they do not want to change the way of production and business. The natural group with an impact rate of 10.13% also affects the management of forest land due to the complicated topography and the poor traffic, thus adversely affecting the management of forest land. Especially the quality of the forest and the ability to bring income to the people is also limited. The group of factors of organization and management to implement policies and laws on forest land for production has the 6th influence with an impact rate of 9.63%. Specifically, the propaganda and dissemination of policies and laws on forest land management have not been paid attention to, so the regulations have not been fully grasped by the people. Besides, inspection, examination, and sanctioning of administrative violations are not good; the settlement of disputes related to forest land is still slow and incomplete.

The financial group includes the loan amount, bank interest, loan term, mortgage procedures, and forest management funds that have an influence on forest land management with an impact rate of 9.36%. Among the borrowed money, the bank interest rate and mortgage procedures are considered by households to be the most influential when they need to borrow capital to invest in production because currently, access to credit is a problem. The group of infrastructure factors has an impact rate of 8.97% and ranks eighth in the groups of factors affecting the management of forest land for production. Factors belonging to the group of infrastructure factors are transport, irrigation, and communication. The group of land parcel elements includes the area of the allocated forest land, the distribution of the land plots, the type of forest land, and the type of forest that affects the management of forest land. This group of factors has an impact rate of 6.25% and occupies ninth place. The group of market factors includes factors such as the type of forest product, its price, the supply and demand for that commodity, and the distribution channel that influences the use of productive forest land in proportion to the influence of 5.43%. The group of human resources, including the qualifications and the number of cadres and civil servants involved in the management of forest land, also affect the management of forest land with the smallest impact rate (4.08%).

For the management of forest land allocated to households to be more complete, it is necessary to implement some priority measures based on the impact rate of the groups of factors indicated above. First of all, it is necessary to improve policies and laws related to the management of forest land such as removing the quota of forest land allocation, the term of use of forest land for production, and implementing forestland allocation according to the user needs of the family hold and according to the local forest land fund. In addition, it is necessary to promulgate policies to support plants and animals for producers to combine with forest protection and increase income. When households have capital needs, they need to apply preferential interest rates with simple mortgage lending procedures and quick disbursement. In particular, it is necessary to attract capital investors and households doing business on forest land for productions such as forest product exploitation, livestock husbandry, tourism, and other activities. In addition, it is necessary to strengthen the propaganda and dissemination of policies and laws on forest land management so that people can understand and comply. Regarding the group of cadastral factors, it is necessary to continue to measure and place landmarks on land plots of land users and issue land use right certificates for the unlicensed forest land to limit disputes and land encroachment. In terms of implementation, it is necessary to strengthen the directing role of all levels and sectors in forest land management and regularly inspect, examine, and sanction violations of forest land legislation, and at the same time, decide to end all land disputes. In terms of infrastructure, it is necessary to support localities in upgrading rural roads, irrigation, and communication systems for forest production and protection, as well as developing other businesses. Besides, it is necessary to ensure enough human resources in terms of quantity and professional qualifications to meet the requirements of forest land management.

4. Conclusion and Recommendations

All 43 hypothetical factors belonging to 11 groups of factors included in the analysis model affect the management of forest land for production with different impact rates (from 4.08% to 12.72%). The group of factors of policy and law on forest land management has the largest impact rate (12.72%), followed by the 10 groups of factors: economics; cadastral; family; nature; implementation of management; finance; Infrastructure; parcel of land; forest product market; human resources with the corresponding impact rates of 11.74%; 11.01%; 10.68%; 10.13%; 9.63%; 9.36%; 8.97%; 6.25%; 5.43%; 4.08%. To manage productive forest land better, it is necessary to perfect policies and laws; strengthen the dissemination of policies and laws; encourage investment; complete cadastral work; intensify inspection, examination, and sanctioning of administrative violations of land; to upgrade the infrastructure system; to ensure enough human resources to manage forest land. The research method proposed in this paper could be used as a reference when assessing the impact of factors on the management of forest land for production in other areas of Vietnam and other countries around the world.