Key factors influencing tree planting decisions of households: A case study in Hoa Binh province
Economic & Policies
KEY FACTORS INFLUENCING TREE PLANTING DECISIONS OF
HOUSEHOLDS: A CASE STUDY IN HOA BINH PROVINCE
Le Dinh Hai1, Pham Thanh Huong2
1,2Vietnam National University of Forestry
SUMMARY
In coping with significant deforestation and forest degradation, currently in Kim Boi district, Hoa Binh
province, and massive reforestation projects have been implemented. However, when remarkable attempts and
investments have been made in reforestation, interaction of household characteristics and socio-economic
factors with smallscale tree planting decision are still little understood. In this study, we survey 150 households
(including 75 households with tree planting and 75 households without tree-planting) in Nuong Dam commune,
Kim Boi district, Hoa Binh province. The results of stepwise binary logistic regression analysis indicate that the
factors, including: Accessibility to Plantation Sites, Forestland Area, Investment Capital, and Knowledge on
Silviculture have a significant effect on household’s decision on tree planting in the study area. The study
results may provide the basis for proposing solutions to strengthen tree planting of households in the study area.
Keywords: Households, influential factors, stepwise binary logistic regression, tree planting decision.
valuable for informing and supporting related
policy interventions. The perceptions of local
people examine their views on how they
consider tree planting activity. If the incentives
and disincentives to tree planting activities are
understood, it will be easier to improve
participation of smallholders and increase
benefits from tree planting. In this paper, we
analysed the key factors influencing tree
planting decision from local people in the
Nam Nuong commune, Kim Boi district, Hoa
Binh province and provide suggestion in
sustainable management of forest plantation
in the study area.
I. INTRODUCTION
Recent history reveals both that the large-
scale reforestation projects of the 20th century
have often been less successful than anticipated,
and that tree growing by smallholders - as an
alternative means to combat deforestation and
promote sustainable land use - has received
relatively little attention from the scientific and
development communities (Snelder and Lasco,
2008). Related studies have shown that
smallholder tree planting activity is influenced
by socioeconomic characteristics such as access
to land with secure land and tree tenure (Byron,
2001; Emtage and Suh, 2004; Sikor and Baggio,
2014; Tran Thi Mai Anh, 2015); suitable
management skills, knowledge and labour
force; interaction with peer farmers’ through
II. REASEARCH METHODOLOGY
2.1. The study area
Hoa Binh Province is located in the North
of Vietnam is the source of headwater and
major tributaries that influence the lives of
more than 808,200 people inside the province.
It borders Son La and Phu Tho provinces to the
northwest, Ha Noi city to the north and
northeast, Ha Nam province to the southeast,
Ninh Binh and Thanh Hoa provinces to the
south. Hoa Binh is a mountainous province
located on the entrance of the Northwest
region and is proud to be famous with
“Hoabinhian Culture” where human life is
proven to existed here since 10,000 - 2,000
BCE. The topography is combined by
mountains and narrow valleys results in the
either
social
groups
or
cooperative
organizations (Sikor and Baggio, 2014; Tran
Thi Mai Anh, 2015); environmental factors
(Summers et al., 2004; Jagger et al., 2005; Tran
Thi Mai Anh, 2015); and access to markets
(Akinnifesi et al., 2006; Tchoundjeu et al.,
2006; Kallio et al., 2011; Tran Thi Mai Anh,
2015).
In Vietnam, 1.2 million households have
been allocated 4.46 million ha, 70% of which
is production forest land (Phuc and Nghi,
2014). Understanding the socioeconomic
factors and perceptions of smallholders related
to tree planting activities in Vietnam will be
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JOURNAL OF FORESTRY SCIENCE AND TECHNOLOGY NO. 2 - 2018
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climate of this district is representative for
tropical monsoon, which is pretty cold and less
rain in winter but hot and rainy in summer. The
annual temperature varies between 150C to
290C, depending on season. Hoa Binh is in the
region has a high poverty rate and a low
standard of living of the population. The
growth of GDP amounts to 11.8% during 2000
- 2010. The poverty rate was 31.31% in 2005,
and was 14% in 2010, but in 2011 the rate of
poverty has jumped again to 37.68%,
according to the new rate of poverty (Mai Lan
Phuong, 2011). They are a large variety of
ethnic groups, which has 15 ethnic
communities, and 63.4% is Muong ethnic
group. The variety of both culture and
environment leads to diverse land-use systems.
Kim Boi District, Hoa Binh Province was
chosen to be a case study because of the
following reasons. Kim Boi is considered as the
district with the largest planted forest area in the
province. The total natural area of Kim Boi
district is 54,950 ha, of which 40,562 ha is
forestry land (account for 73% of the district's
natural area), and production forest area
accounts for over 21,000 ha. On average, Kim
Boi district has planted 1,000 - 2,000 ha of
forest annually, mainly production forests and
100 - 200 ha of fruit trees. In 2014, the district
has planted 2001 ha of forest increasing the
forest cover to 49.3%. In 2018, Kim Boi district
plans to plant 1,700 hectares of new forest,
mainly production forests and allocate over
37,000 hectares of forests for people to manage
and protect.
Nuong Dam is a commune with extremely
difficult socio-economic conditions in Kim Boi
district, Hoa Binh province. Nuong Dam
commune lies in the tropical monsoon climate,
with two distinct seasons: rainy and dry
season, average temperature: 23°C, average
humidity: 60%, the average rainfall: 1,800
mm. Land of Nuong Dam commune is
typically with high fertility suitable for many
crops. With hundreds of thousands of hectares
of land including the adjoining plots, land in
Nuong Dam commune can be used for various
purposes, especially afforestation, industrial
crops for the agro-forestry and industrial
development. The Nuong Dam commune
covers an area of 35.66 km² (in 2016), with a
population of 3,381 people in 1999; 4,058
people in 2016, and a population density of
114 persons/km².
Figure 1. Map of Nuong Dam commune, Kim Boi district, Hoa Binh province
Source: People Committee of Nuong Dam commune, 2016
JOURNAL OF FORESTRY SCIENCE AND TECHNOLOGY NO. 2 - 2018 173
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Nuong Dam commnue was chosen to be a case
study because of the following reasons. Firstly,
Nuong Dam commune is a large forested area of
Kim Boi district which is representative for
mountainous area, bounded with streams, rivers,
valleys, and limestone mountains. Secondly, this
area also is a focal point of planting for
headwater which plays an important role for
protecting water resource of whole regions.
2.2. Study method
The attributes of the selected households are
summarised in table 1. The survey was based
on the conceptual model for assessing key
factors affecting the tree planting decision of
households (figure 2). The survey was
conducted by using a questionnaire designed
to collect data on general household
characteristics, factors influencing tree
planting decision of households. A copy of
the questionnaire is available on request. The
questionnaire was administered face-to-face,
usually the head of households.
In this study, we selected 150 households
for survey according to the criteria in table 1.
Figure 2. Factors influence tree planting decision of smallholder
Source: Tran, 2015
households, 25 moderate households and 25
poor households. On the other hand, among 75
households not tree planting, 25 households
are classified as rich, 25 households are
classified as moderate, and 25 households are
classified as poor. The interview design was
followed by a stratified random sampling
method to obtain representative strata by
decision of tree planting and household wealth
ranking.
Kim Boi district has 27 communes and
aninternal town with population of 114,000
people (GSO 2016). We conducted a
household survey in one representative
communes namely Nam Nuong commune, in
which, 150 households including 75
households having decision of tree planting
and 75 households without decision for tree
planting. Within 75 tree planting households,
we divided into 3 sub-group based on
household wealth ranking including 25 rich
Table 1. Number of survey households in the study area
Households wealth ranking
Poor
Moderate
Rich
25
25
Total
75
75
Yes
No
25
25
50
25
25
50
Tree planting
Total
50
150
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Personal interviews were conducted in the
study area. This method allows researchers the
opportunity to ask more questions, longer
questions, more detailed questions, more open-
ended questions, and more complicated or
technical questions. Moreover, face-to-face
surveys also offer advantages in terms of data
quality (Manurung et al., 2008). The survey
was conducted from 1st August 2017 to 20th
August 2017.
IBM SPSS Statistics 23 was used for data
analysis. Bivariate analysis was used to identify
association between ‘Tree planting decisions by
households’ (dependent variable) and factor
(independent variable) (see Table 2 for a full list of
variables included in the analysis). Factors found to
be significantly associated with an independent
variable in the bivariate analyses (p < 0.05) were
considered as candidates in stepwise binary logistic
regressions with independent variables.
Table 2. Description of variables
No
1
Variables
Ethnicity
Description
Ethnicity of household
States (range)
Muong = 1; Kinh = 2
2
Age
Age of household head
Education of
householdhead
Forestland area (ha) Forest land area of each household
3
4
5
Education level assigned for each level
Forestry program = 1; Bank
= 2; Self investment = 3
Long time (> 5 years) = 1;
Short time (1 - 5 years) = 2
Yes = 1; No = 0
Investment capital
Sources of investment for tree planting
6
7
Length of rotation
Experience
Type of tree used for planting
An experience that tree planter has before
Easy, accessible with car =
1; Medium, accessible by
motorbike = 2; Difficult,
have to walk = 3
Accessibility to
plantation site
8
Accessibility to the plantation site
Idea of tree planter about climate condition
that influences to treeplanting
9
Climate condition
Suitable = 1; Unsuitable = 0
Knowledge of tree planter on silviculture by
applying fertilizers & pesticides, as well as
practicing silvicultural plantation
Knowledge of tree planter about forestry
program, and how to register forestry program
Land tenure of the household
Knowledge on
silviculture
10
11
Good = 1; Bad = 0
Knowledge about
forestry program
12 Land tenure
Yes = 1; No = 0
Yes = 1; No = 0
Yes = 1; No = 0
Tree planting
decision
13
Tree planting decision of the household
Factors were entered into the stepwise
regressions if the significance of their
relationship with an independent variable was
p < 0.05 and removed from the stepwise
regression if the significance of their
relationship with an independent variable
became p ≥ 0.10. Factors were entered into the
stepwise regressions in order of their
correlation with a dependent variable, from
most strongly (highest Pearson’s correlation)
to least strongly correlated (lowest Pearson’s
correlation) (Brace et al., 2006; Ho, 2006). A
set of significant factors for a dependent
variable was the result of the stepwise binary
logistic regression. Stepwise regression is an
appropriate analysis for this study because
there are many variables (12 independent
variables) in the binary logistic regression
model and we are interested in identifying a
useful subset of the predictors.
III. RESULTS AND DISCUSSION
3.1. Descriptive statistics on surveyed households
In general, almost all of households
surveyed are Muong ethnicity (88%). The
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results in table 4 show that approximate 60%
of the respondents having good knowledge on
silviculture and roughly 40% of total
households admit that they have little or even
no knowledge on this field. In addition, most
of interviewees said an extension officer from
government forestry program is very important
in training and educating communities on tree
planting practices. The more the farmers
interact with them, the more likely it is for
them to gain knowledge on silvilcuture. The
fact that, ‘Knowledge about forestry program’
for those who did not have knowledge about
forestry program was a quarter of who have
‘Knowledge about forestry program’. And the
accessibility
from
accommodation
to
forestland area is easy and moderate account
for 12.7% and 49.3%, respective. The rest is a
difficult accessibility accounted for 38%.
Table 3. Relationship between independent variables and tree planting decision of households
Tree planting
Independent variables
(%)
No
66
9
Yes
66
9
Total
132
18
88
Muong
Kinh
Ethnicity
12
Total
75
75
150
100
26.67
No investment
33
7
40
Forestry program
Bank
Self-Investment
Total
7
7
14
24
72
150
96
9.3
16
48
100
64
Investment capital
12
23
75
12
49
75
Long time (> 5 years)
48
48
Length of rotation
Experience
36
100
26.67
27
75
30
45
75
8
27
75
10
65
75
11
54
150
40
110
150
19
Short time ( 1 5 year)
Total
No
Yes
73.33
100
12.7
Total
Easy, accessible with car
Medium, accessible by motorbike
Difficult, have to walk
Total
16
51
75
58
6
75
74
57
150
49.3
38
100
32
Accessibility to plantation sites
Unsuitable
27
21
48
Climate condition
Suitable
Total
Bad
48
75
42
54
75
19
102
150
61
68
100
40.67
Knowledge on silviculture
Good
Total
No
Yes
Total
33
75
22
53
75
13
56
75
11
64
75
8
89
150
33
117
150
21
59.33
100
22
Knowledge about forestry
program
78
100
14
No
Land Tenure
Yes
62
67
129
86
Total
75
75
150
100
Source: Household survey, 2017
Results from table 4 show that there are
only significant differences at 5% level in ‘Age
of household head’ and ‘Forest land area’
between households decided to planting trees
and households decided not planting the trees.
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Table 4. Descriptive statistics of quantitative variable
Tree Planting Decision Total
No Yes
P value for t-
test of Mean
(2 tailed)
Parameter
Mean Std. Dev.
Mean Std. Dev. Mean Std. Dev.
Age of household head 51.37
7.23
0.40
1.88
49.01
2.28
7.35
5.76
2.92
1.69
50.19
1.40
7.23
6.626
2.259
1.788
0.029
0.000
0.413
Forest land Area
Education
0.53
7.11
Source: Household survey, 2017
Knowledge on Silviculture). The full model
containing all predictors was statistically
significant, χ2(4, N = 150) = 93.74, p < .001,
indicating that the model was able to
distinguish between respondents who decided
and did not decide tree planting. The model as
a whole explained between 46.5% (Cox and
Snell R squared) and 62.0% (Nagelkerke R
squared) of the variance in the decision of tree
planting, and correctly classified 86.0% of cases.
3.2. Key drivers influencing tree planting
decision of surveyed household
Direct stepwise binary logistic regression
was performed to assess the impact of a
number of factors on the likelihood that
households would report that they had a
decision of planting trees or not. The model
contained
(Forestland
four
area,
independent
Investment
variables
Capital,
Accessibility to Plantation Sites, and
Table 5. Model summary for key drivers affecting tree planting decision of surveyed households
Independent variables
B
S.E.
Exp(B)
Sig
(Constant)
2.341
1.117
0.678
-1.613
1.239
1.307
0.344
0.193
0.377
0.509
10.392
3.056
1.970
0.199
3.452
0.073*
0.001***
0.000***
0.000***
0.015**
Forestland area
Investment capital
Accessibility to plantation sites
Knowledge on silviculture
Dependent variable: Tree planting decision by households
Number of Observations
150
Omnibus Tests of Model Coefficients:
· Chi-square
· df
93.74
4
· Sig.
0.000
Model summary:
· -2 Log likelihood
114.205***
0.465
· Cox & Snell R Square
· Nagelkerke R Square
0.620
· Predicted Percentage Correct (%)
86.0
Note: *** p < 0.01, ** p < 0.05, * p < 0.10, NS Not significance (two-tailed tests).
Source: Household survey, 2017
decide to plant trees were improved by about
5.025 times if Accessibility to Plantation Sites
of household decrease one level from “difficult
level” to “easy level”, by about 3.452 times if
household has ‘Knowledge on Silviculture’, by
about 1.970 times if Investment Capital
increases one level (table 6).
As shown in table 6, four independent
variables (Forestland Area, Investment Capital,
Accessibility to Plantation Sites, and
Knowledge on Silviculture) were statistically
significant
in
distinguishing
between
households decide or did not decide to plant
trees. The odds of households decide or did not
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Table 6. Determining importance of variables in the multiple linear regression model
Dependents
Forestland area
Investment capital
Accessibility to plantation sites
Knowledge on silviculture
B
Exp(B)
3.056
1.970
0.199
3.452
Exp(B)adjusted
3.056
Ranking
1.117
0.678
-1.613
1.239
3
4
1
2
1.970
5.025
3.452
Note: Ranking with 1: highest, 4 smallest; if B > 0 then Exp(B)adjusted = Exp(B); and if B < 0, then
Exp(B)adjusted = 1/Exp(B).
Source: Household survey, 2017
Exp(B)adjusted in table 6 shows that
‘Knowledge on Silviculture’, ‘Forestland
Area’, ‘Investment Capital’ variables have a
positive influence on the tree planting decision
of local households, and ‘Accessibility to
Plantation Sites’ variable is negatively
influenced on tree planting decision of local
households in the study area. Ordinal
influential factors are represented as following:
(1) Accessibility to Plantation Sites; (2)
Knowledge on Silviculture; (3) Forestland
Area; and (4) Investment Capital.
area was found to be significantly and
positively related to tree planting decision of
households. Byron (2001), Kallio (2013) and
Tran Thi Mai Anh (2015) found that tree
planters were generally with more land, higher
value of total assets and more active
participation in tree planting than non-tree
planters.
3.3.3. Investment capital
Funding from self-investment was found to
be significantly and positively related to tree
planting decision of households. Byron (2001),
Sikor and Baggio (2014), and Tran Thi Mai
Anh (2015) found that better-off households
are more likely to possess forestland, grow
trees, and invest in plantations than poor ones.
In addition, land plantations, and investment
tend to be larger for the better-off than the
poor. Better-off households are in a better
position to engage in tree plantations due to,
among other factors, the institutional
mechanisms differentiating household access
to land and finance. Sandewall et al. (2010)
revealed that many poor farmers had received
forest land through the Forest Land Allocation
(FLA), but their possibility to benefit from
plantations was limited. They had usually
received land late in the process of FLA, as
they initially declined to become involved;
their plantations were small and far away,
3.3. Discussions and Policy Implication
3.3.1. Accessibility to plantation site
Accessibility to plantation site was found
to be significantly and negatively related to
tree planting decision of households. Dupuy
and Mille (1993) indicated that accessibility
of the planted area is a parameter that cannot
be overlooked, for it is important only in
reforestation per se, but also in the follow-up
(tending, thinning, and wildfire protection,
etc.) and in taking out harvested products.
Therefore, the improvement of infrastructure,
such as roads, as part of forest plantation
programs is important to success, particular
where plantation sites are isolated and the
improved
infrastructure
can
assist
communities to reliably access tree planting
inputs and product markets. Infrastructure
development is very expensive and not all
projects are able to fulfil fund it, therefore
which
complicated
management
and
protection; they had to harvest prematurely to
secure the necessary cash flow, and they did
not have the necessary finances to maintain the
plantations. There were very limited credit
facilities. Therefore, the forest administration
lower-cost
options
for
infrastructure
improvement are vital.
3.3.2. Forestland area
Result of this study indicated that forestland
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such as the Department of Forestry
Development and the Forest Protection
Stations at District level, mainly had
regulatory, supervisory and monitoring tasks.
3.3.4. Knowledge of household head about
silviculture
households in the study area. Therefore
focusing on performance indicators alone will
not improve our understanding of why
households decide to plant or not plant trees.
Therefore, it is essential to develop
infrastructure that can help farmers to easily
access of plantation sites, better access to
credit, provide farmers with more agroforestry
extension activities.
Knowledge on silviculture had significantly
positive effects on tree planting decision of
households. Salam et al. (2000) and Tran Thi
Mai Anh (2015) indicated clearly that farmers’
awareness of forestry extension programs is
slight, and the contribution of forestry workers
to motivate farmers to plant trees has been
negligible. To maximize the potential of
homestead forestry, forestry professionals and
extension workers should broaden their
activities and work more closely with local
farmers. They should disseminate technical
information to tree growers, supply quality
seedlings suitable for the area, provide
effective institutional support, and arrange for
efficient marketing facilities of the farm forest
products so that poor farmers can come
forward to enhance tree production and get
proper returns from production. Therefore,
reforestation education, information or
awareness building campaigns also provide
market information, and marketing support for
timber and other forest products that can help
to increase the cash income of farmers, which
in turn can lead to better site management and
protection, and reduced erosion and landslide
risk (Le et al., 2014).
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institutional and management factors influence
tree planting decision of household in Kim Boi
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Hoa Binh Province, Vietnam with Bayesian Networks.
Bachelor, Vietnam National University of Forestry.
17. Sandewall, M., B. Ohlsson, R. K. Sandewall and
CÁC NHÂN TỐ ẢNH HƯỞNG ĐÁNG KỂ ĐẾN QUYẾT ĐỊNH TRỒNG RỪNG
CỦA CÁC HỘ GIA ĐÌNH: NGHIÊN CỨU ĐIỂM TẠI TỈNH HÒA BÌNH
Lê Đình Hải1, Phạm Thanh Hương2
1,2Trường Đại học Lâm nghiệp
TÓM TẮT
Để ứng phó với sự mất rừng và suy giảm tài nguyên rừng nghiêm trọng, đã có nhiều dự án khôi phục rừng đã
được triển khai trên địa bàn huyện Kim Bôi, tỉnh Hòa Bình. Tuy nhiên, khi mà những nỗ lực và đầu tư đáng kể
vào khôi phục rừng, thì sự tương tác giữa đặc điểm của hộ gia đình và các yếu tố kinh tế xã hội có liên quan
đến trồng rừng qui mô hộ gia đình còn được biết đến một cách hạn chế. Trong nghiên cứu này chúng tôi khảo
sát 150 hộ gia đình (bao gồm 75 hộ trồng rừng và 75 hộ không trồng rừng) trên địa bàn xã Nuông Dăm, huyện
Kim Bôi, tỉnh Hòa Bình. Kết quả phân tích ứng dụng mô hình hồi qui Stepwise Binary Logistic Regression đã
xác định được 4 yếu ảnh hưởng đáng kể đến quyết định trồng rừng của hộ gia đình trên địa bàn nghiên cứu, bao
gồm: khả năng tiếp cận rừng trồng, diện tích đất lâm nghiệp, vốn đầu tư và kiến thức về kỹ thuật lâm sinh. Kết
quả nghiên cứu có thể làm cơ sở cho việc đề xuất các giải pháp làm tăng cường và mở rộng trồng rừng qui mô
hộ gia đình trên địa bàn nghiên cứu.
Từ khóa: Hộ gia đình, mô hình hồi qui logit chọn từng bước (stepwise binary logistic regresion), nhân tố
ảnh hưởng, quyết định trồng rừng.
Received
Revised
Accepted
: 02/3/2018
: 23/3/2018
: 03/4/2018
180
JOURNAL OF FORESTRY SCIENCE AND TECHNOLOGY NO. 2 - 2018
9 trang
11220
Free
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