Key factors influencing tree planting decisions of households: A case study in Hoa Binh province

gram Yes 53 64 117 78 
 Total 75 75 150 100 
 No 13 8 21 14 
Land Tenure Yes 62 67 129 86 
 Total 75 75 150 100 
 Source: Household survey, 2017 
 Results from table 4 show that there are between households decided to planting trees 
only significant differences at 5% level in ‘Age and households decided not planting the trees. 
of household head’ and ‘Forest land area’ 
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 Table 4. Descriptive statistics of quantitative variable 
 Tree Planting Decision Total P value for t-
 Parameter No Yes test of Mean 
 Mean Std. Dev. 
 Mean Std. Dev. Mean Std. Dev. (2 tailed) 
Age of household head 51.37 7.23 49.01 5.76 50.19 6.626 0.029 
Forest land Area 0.53 0.40 2.28 2.92 1.40 2.259 0.000 
Education 7.11 1.88 7.35 1.69 7.23 1.788 0.413 
 Source: Household survey, 2017 
3.2. Key drivers influencing tree planting Knowledge on Silviculture). The full model 
decision of surveyed household containing all predictors was statistically 
 Direct stepwise binary logistic regression significant, χ2(4, N = 150) = 93.74, p < .001, 
was performed to assess the impact of a indicating that the model was able to 
number of factors on the likelihood that distinguish between respondents who decided 
households would report that they had a and did not decide tree planting. The model as 
decision of planting trees or not. The model a whole explained between 46.5% (Cox and 
contained four independent variables Snell R squared) and 62.0% (Nagelkerke R 
(Forestland area, Investment Capital, squared) of the variance in the decision of tree 
Accessibility to Plantation Sites, and planting, and correctly classified 86.0% of cases. 
 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 1.307 10.392 0.073* 
Forestland area 1.117 0.344 3.056 0.001*** 
Investment capital 0.678 0.193 1.970 0.000*** 
Accessibility to plantation sites -1.613 0.377 0.199 0.000*** 
Knowledge on silviculture 1.239 0.509 3.452 0.015** 
Dependent variable: Tree planting decision by households 
Number of Observations 150 
Omnibus Tests of Model Coefficients: 
· Chi-square 93.74 
· df 4 
· Sig. 0.000 
Model summary: 
· -2 Log likelihood 114.205*** 
· Cox & Snell R Square 0.465 
· 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 
 As shown in table 6, four independent decide to plant trees were improved by about 
variables (Forestland Area, Investment Capital, 5.025 times if Accessibility to Plantation Sites 
Accessibility to Plantation Sites, and of household decrease one level from “difficult 
Knowledge on Silviculture) were statistically level” to “easy level”, by about 3.452 times if 
significant in distinguishing between household has ‘Knowledge on Silviculture’, by 
households decide or did not decide to plant about 1.970 times if Investment Capital 
trees. The odds of households decide or did not increases one level (table 6). 
 JOURNAL OF FORESTRY SCIENCE AND TECHNOLOGY NO. 2 - 2018 177
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 Table 6. Determining importance of variables in the multiple linear regression model 
 Dependents B Exp(B) Exp(B)adjusted Ranking 
Forestland area 1.117 3.056 3.056 3 
Investment capital 0.678 1.970 1.970 4 
Accessibility to plantation sites -1.613 0.199 5.025 1 
Knowledge on silviculture 1.239 3.452 3.452 2 
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 area was found to be significantly and 
‘Knowledge on Silviculture’, ‘Forestland positively related to tree planting decision of 
Area’, ‘Investment Capital’ variables have a households. Byron (2001), Kallio (2013) and 
positive influence on the tree planting decision Tran Thi Mai Anh (2015) found that tree 
of local households, and ‘Accessibility to planters were generally with more land, higher 
Plantation Sites’ variable is negatively value of total assets and more active 
influenced on tree planting decision of local participation in tree planting than non-tree 
households in the study area. Ordinal planters. 
influential factors are represented as following: 3.3.3. Investment capital 
(1) Accessibility to Plantation Sites; (2) Funding from self-investment was found to 
Knowledge on Silviculture; (3) Forestland be significantly and positively related to tree 
Area; and (4) Investment Capital. planting decision of households. Byron (2001), 
3.3. Discussions and Policy Implication Sikor and Baggio (2014), and Tran Thi Mai 
3.3.1. Accessibility to plantation site Anh (2015) found that better-off households 
 Accessibility to plantation site was found are more likely to possess forestland, grow 
to be significantly and negatively related to trees, and invest in plantations than poor ones. 
tree planting decision of households. Dupuy In addition, land plantations, and investment 
and Mille (1993) indicated that accessibility tend to be larger for the better-off than the 
of the planted area is a parameter that cannot poor. Better-off households are in a better 
be overlooked, for it is important only in position to engage in tree plantations due to, 
reforestation per se, but also in the follow-up among other factors, the institutional 
(tending, thinning, and wildfire protection, mechanisms differentiating household access 
etc.) and in taking out harvested products. to land and finance. Sandewall et al. (2010) 
Therefore, the improvement of infrastructure, revealed that many poor farmers had received 
such as roads, as part of forest plantation forest land through the Forest Land Allocation 
programs is important to success, particular (FLA), but their possibility to benefit from 
where plantation sites are isolated and the plantations was limited. They had usually 
improved infrastructure can assist received land late in the process of FLA, as 
communities to reliably access tree planting they initially declined to become involved; 
inputs and product markets. Infrastructure their plantations were small and far away, 
development is very expensive and not all which complicated management and 
projects are able to fulfil fund it, therefore protection; they had to harvest prematurely to 
lower-cost options for infrastructure secure the necessary cash flow, and they did 
improvement are vital. not have the necessary finances to maintain the 
3.3.2. Forestland area plantations. There were very limited credit 
 Result of this study indicated that forestland facilities. Therefore, the forest administration 
178 JOURNAL OF FORESTRY SCIENCE AND TECHNOLOGY NO. 2 - 2018 
 Economic & Policies 
such as the Department of Forestry households in the study area. Therefore 
Development and the Forest Protection focusing on performance indicators alone will 
Stations at District level, mainly had not improve our understanding of why 
regulatory, supervisory and monitoring tasks. households decide to plant or not plant trees. 
3.3.4. Knowledge of household head about Therefore, it is essential to develop 
silviculture infrastructure that can help farmers to easily 
 Knowledge on silviculture had significantly access of plantation sites, better access to 
positive effects on tree planting decision of credit, provide farmers with more agroforestry 
households. Salam et al. (2000) and Tran Thi extension activities. 
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 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 : 02/3/2018 
 Revised : 23/3/2018 
 Accepted : 03/4/2018 
 180 JOURNAL OF FORESTRY SCIENCE AND TECHNOLOGY NO. 2 - 2018