Corresponding Author: Cynthia Karlina
E-mail: [email protected]
INTRODUCTION
The area of degraded forest land in
Indonesia has reached an alarming condition, covering 48.5 million hectares
consisting of 26.6 million hectares of land inside the forest, 21.9 million
hectares of land outside the forest, and 11.40 million hectares of land as
mining concessions (Pudjiharta et al.,
2007); (Nirawati &
Putranto, 2013). Meanwhile, in 2018, the area of
critical land in Indonesia reached 14,006,450 hectares and the
area of critical land in South Sumatra Province reached 733,756
hectares (LHK, 2018).
Forest damage also occurred in South Sumatra Province,
especially in Ogan Komering
Ulu Regency in the KPH XV Bukit Nanti area. The total
critical land in the KPHL Unit XV Bukit later area is 43,745 ha with a
criticality level of 28,554 moderately critical and 15,190 ha critical. The
damage that occurred in the Bukit later area was caused by forest encroachment
in the form of cultivating protected forest areas into coffee plantations,
where the activity was only armed with a permit from the local village head so
that farmers could plant coffee in protected forest areas. This is a serious
concern because new land clearing activities are still ongoing. Communities
plant coffee in protected forest areas because the location has relevant
conditions for planting coffee because according to the Plantation Service
(2018), the conditions for growing coffee plants generally can grow well at an
altitude above 500 meters above sea level with an amount of rainfall of 1,500 �
2,500 mm. /year, dry months 1-3 months, and the average air temperature is
17-210C for arabica and 21-240C for robusta.
The forest and land rehabilitation program aims to
restore critical forest and land conditions with a focus on planting by
involving community participation in its implementation (Agustinus &
Mujahiddin, 2013). The RHL management process has undergone good
changes with the development of capacity in terms of planning, human resources,
technology and supervision. Currently, according to the regulations,
rehabilitation activities are carried out with community involvement. Community
empowerment in Forest and Land Rehabilitation (RHL) activities according to
P.9/Menhut-II/2013 aims to increase community
independence in implementing RHL on their land, both individually and in
groups.
Based on the description of the background above, the
forest and land rehabilitation program is an effort to restore, maintain and
improve the functions of forests and land so that their carrying capacity,
productivity and role in supporting life support systems are maintained (Pertiwi & Marta,
2016). The Forest and Land Rehabilitation Program was carried out on crisis
lands in the Bukit Nanti Protection Forest. Given
that Forest and Land Rehabilitation activities are a priority activity by
involving various related parties, not only the government and the private
sector but also the community, especially the community around the Forest and Land
Rehabilitation location. This is what prompted researchers to be interested in
conducting a study entitled "Analysis of Determinants of Coffee Farmers'
Decisions to Participate in the Forest and Land
Rehabilitation Program in Lengkiti District, Ogan Komering Ulu Regency".
The formulation of the problem in this study is what
factors influence the coffee farmers' decisions to be involved in forest and
land rehabilitation programs in which there are internal and external factors
which are then analyzed what variables are in these factors that influence the
coffee farmers' decisions to participate in forest and land rehabilitation
programs with the aim of being able to analyze what factors influence the
farmers' decisions.
METHOD
This research was
conducted in Lengkiti Subdistrict, in Ogan Komering Ulu Regency, precisely in
Bunga Tanjung Village and Gedung Pakuan Village. The location of this research
was chosen purposively with the consideration that Bunga Tanjung Village and
Gedung Pakuan Village are villages that have forest and land rehabilitation
programs. The method used in this study is a survey method with 120 sample
farmers from 2266 farmer populations. Determination of the sample in this study
using convenience sampling where the samples of farmers who have been taken are
farmers who can be found consisting of farmers who are in protected forest
areas and farmers outside protected forest areas. For more details, the
population sample can be seen in table 1.
Table 1. Sample population in Bunga Tanjung Village and
Gedunga Pakuan Village
|
Research sites
|
Farmer
Characteristics
|
Total
Population
|
Number
of Samples (n)
|
Percentage
(%)
|
|
Desa Bunga Tanjung
|
Farmers
in HL. Area
|
740
|
30
|
25
|
|
|
Farmers
outside the HL. Area
|
691
|
30
|
25
|
|
|
Desa Gedung Pakuan
|
Farmers
in HL. Area
|
284
|
30
|
30
|
|
|
Farmers
outside the HL. Area
|
551
|
30
|
30
|
|
|
|
|
2266
|
120
|
100
|
|
|
|
|
|
|
|
|
To answer the
problem formulation regarding what factors influence coffee farmers' decisions
to be involved in forest and land rehabilitation programs in this study, a
regression model was used which in this study used a binary logistic regression
model. The binary logistic regression model is a data analysis method used to
find the relationship between the response variable (y) which is binary
(Khairunnisa et al., 2022), namely in this study y1 = participation and y2 = not
participating with predictor variable X (Khotimah & Sutiono,
2014). To find out to what extent these factors influence
farmers' decisions to participate in forest and land rehabilitation programs
mathematically:
�................................... (1)
Information:
𝑃𝑖 = Individual Opportunity in Making Decisions
𝐵0 = Intercept
𝐵1 = Regression
Coefficient
Xi �= Independent Variable
The first estimate is obtained by multiplying both sides of
equation (1) with 1+e-zi to get (1+e-zi) Pi = 1.....................................(2)
Equation (2) divided by Pi then subtracting 1 will give equality:
�‒1= 
Or it can be expressed in the form of an equation:
�......................................................................................................................
(3)
Equation (3) is then transformed into a natural logarithm model so
as to produce the following equation:
�.................................................................................................................
(4)
With In ezi = Zi , then equation (4) can
be written as follows:
�.........................................................................................
(5)
Equation (5) above is known as the
logit model or logistic regression model. So if written in the logit model it
becomes:
In = Zi
= Yi = 0 + 1X1 + 2X2 + 3X3 + 4X4 + 5 D1 + 6 D2 + 7D3
Where:
Pi
= probability of RHL program by farmer
1-Pi
= probability that farmers are not involved in RHL
Zi = farmer's decision to engage in RHL
Yi = choice of RHL involvement
β0 = Intercept
Internal
X1 = Revenue
X2 = Expenditure
X3 = Land Area
X4 = Education
X5 = Labor Availability
X6 = Age
X7 = Location
External
X8 = Transportation Availability
X9 = Communication Availability
X10 = Market Availability
X11 = Capital Availability
RESULTS AND DISCUSSION
1.
Characteristics of Coffee Farmers in Lengkiti District
The characteristics of coffee farmers in Lengkiti
District, Ogan Komering Ulu Regency can be seen in Table 1. Below. There are
several socio-economic characteristics such as income, expenditure, land area,
education, availability of labor, age.
Table 2. Characteristics of Coffee
Farmers in Lengkiti District
|
Component
|
Number of Farmers
|
Persentase (%)
|
|
|
GP farmer
|
BT farmer
|
GP farmer
|
BT farmer
|
|
Lokasi
|
|
|
|
|
|
Protected
Forest Area
|
30
|
30
|
25
|
25
|
|
Non
Protected Forest Area
|
30
|
30
|
25
|
25
|
|
Income
(Rp/Year)
|
|
|
|
|
|
10.000.000
- 19.999.999
|
16
|
34
|
13,33
|
28,33
|
|
20.000.000
� 29.999.999
|
21
|
4
|
17,50
|
3,33
|
|
30.000.000
� 39.999.999
|
15
|
6
|
12,50
|
5,00
|
|
>40.000.000
|
8
|
16
|
6,67
|
13,33
|
|
Expenditure
(Rp/year)
|
|
|
|
|
|
10.000.000
- 19.999.999
|
6
|
6
|
5,00
|
5,00
|
|
20.000.000
� 29.999.999
|
34
|
38
|
28,33
|
31,67
|
|
30.000.000
� 39.999.999
|
19
|
12
|
15,83
|
10,00
|
|
>40.000.000
|
1
|
4
|
0,83
|
3,33
|
|
Land
area (ha)
|
|
|
|
|
|
1
|
13
|
7
|
10,83
|
5,83
|
|
2
|
19
|
24
|
15,83
|
20,00
|
|
3
|
21
|
22
|
17,50
|
18,33
|
|
>4
|
7
|
7
|
5,83
|
5,83
|
|
Education
|
|
|
|
|
|
No
school
|
4
|
9
|
3,33
|
7,50
|
|
Primary
school
|
38
|
31
|
31,67
|
25,83
|
|
Junior
high school
|
14
|
18
|
11,67
|
15,00
|
|
Senior
High School
|
4
|
2
|
3,33
|
1,67
|
|
Availability
T.K (org)
|
|
|
|
|
|
3-4
|
10
|
9
|
8,33
|
7,50
|
|
5-6
|
24
|
21
|
20,00
|
17,50
|
|
7-8
|
24
|
22
|
20,00
|
18,33
|
|
>9
|
2
|
8
|
1,67
|
6,67
|
|
Age
(years)
|
|
|
|
|
|
20-29
|
3
|
3
|
2,50
|
2,50
|
|
30-39
|
13
|
11
|
10,83
|
9,17
|
|
40-49
|
20
|
13
|
16,67
|
10,83
|
|
>50
|
24
|
33
|
20,00
|
27,50
|
GP farmers are farmers from Gedung Pakuan Village and BT
farmers are farmers from Bunga Tanjung Village, Lengkiti District who are
included in the villages in the target of implementing forest and land
rehabilitation programs in 2019 this is because in this area there are
protected forest area which is included in the working area of
KPH Bukit later. In addition to being the target village of the
forest and land rehabilitation program in this sub-district, there is also the
opening of protected forest land by the community which is intended for coffee
plantations.
From Table 2. It can be seen that the level of education
in Gedung Pakuon Village can be said to be low at 3.33% or as many as 4 people
who graduated from high school, this also applies in Bunga Tanjung Village
which has a percentage of high school graduates of 1.67 or as many as 2 person.
This can be influenced by the absence of SMA in the two villages so that if
they want to continue their education, they must go to another village.
Farmers' income is one of the factors that can influence
coffee farmers' decisions to participate in forest and land rehabilitation
programs. This is because income will affect the welfare of coffee farmers,
with coffee farmers participating in forest and land rehabilitation programs,
they will get additional income from activities carried out by forest and land
rehabilitation programs such as receiving wages for transportation services
(both goods/people), farmers who participate in the rehabilitation program will
also receive assistance in the form of plant seeds, and other activities that
will increase farmers' income. Farmers' income can also be seen in Table 2.
The next thing that can be a determining factor for
coffee farmers' decisions to participate in forest and land rehabilitation
programs is land area. From Table 2. it can be seen that the land area of
the majority of farmers is in the range of 2-3 ha. This is also
in line with the habit of the community where the wider the land, the more
likely they are to clear land in the protected forest area. So that later it
will be in line with the location, where the location of the protected forest
area will be a priority target in the forest and land rehabilitation program.
2.
Analysis of Determinants of Coffee Farmers' Decisions to
Participate in the Forest and Land Rehabilitation Program in Lengkiti District,
Ogan Komering Ulu Regency
Based on primary data processing, to determine the
factors of income (X1), expenditure (X2), land area (X3), education (X4), labor
availability (X5), age (X6), location (X7), transportation (X8 ), Communication
(X9), market (X10), and Capital (X11) on the decision makers of coffee farmers
to participate in the Forest and Land Rehabilitation Program in Lengkiti
District, Ogan Komering Regency. The results of calculations using SPSS 26 with
binary logistic regression analysis can be seen in the following sub-chapter.
a. Logistics Regression Interpretation with SPSS
In this study, the sample used was 120 samples, it can be
seen from table 3. Case Processing Summary. Where in this table it is known
that there is no miss in the data entered.
Table
3. Case Processing Summary
|
Unweighted
Cases a
|
|
N
|
Percent
|
|
Selected Cases
|
Include in Analysis
|
120,0
|
100,0
|
|
|
Missing Cases
|
0
|
.0
|
|
|
Total
|
120,0
|
100.0
|
|
Unselected Ceses
|
|
0
|
.0
|
|
Total
|
|
120
|
100.0
|
b. Odds Ratio
The following is the interpretation of the odds ratio in
this case, it can be seen from the variable in Equition of each predictor
variable that is included in the model of factors that influence coffee's
decision to participate in forest and land rehabilitation programs.
Table 4. Odds Ratio
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
Income
|
.000
|
.000
|
4.993
|
1
|
.025
|
1.000
|
1.000
|
1.000
|
|
Expenditure
|
.000
|
.000
|
.017
|
1
|
.897
|
1.000
|
1.000
|
1.000
|
|
Land area
|
-2.853
|
1.278
|
4.982
|
1
|
.026
|
.058
|
.005
|
.706
|
|
Education
|
-.766
|
1.057
|
.526
|
1
|
.468
|
.465
|
.059
|
3.689
|
|
Kindergarten Availability
|
.041
|
.351
|
.014
|
1
|
.907
|
1.042
|
.524
|
2.071
|
|
Age
|
.114
|
.081
|
1.991
|
1
|
.158
|
1.120
|
.957
|
1.312
|
|
Location
|
15.137
|
6.157
|
6.045
|
1
|
.014
|
3750204.202
|
21.551
|
652595802150.573
|
|
Transportation
|
-2.280
|
1.883
|
1.466
|
1
|
.226
|
.102
|
.003
|
4.100
|
|
Communication
|
.161
|
1.254
|
.016
|
1
|
.898
|
1.175
|
.101
|
13.712
|
|
Market
|
-1.410
|
4.019
|
.123
|
1
|
.726
|
.244
|
.000
|
643.219
|
|
Capital
|
-4.149
|
2.659
|
2.435
|
1
|
.119
|
.016
|
.000
|
2.894
|
|
Constant
|
-8.729
|
9.482
|
.847
|
1
|
.357
|
.000
|
|
|
|
a. Variable(s) entered on step 1: Income,
Expenditure, Land area, Education, Kindergarten Availability, Age, Location,
Transportation, Communication, Market, Capital.
|
The independent variables that significantly influence
the farmer's decision to participate in the forest and land rehabilitation
program are the income variable, land area, and location variable. The real
level used in this study is 5% so that the independent variable is declared to
have a significant effect if it has an error value of less than 5%. Other
variables, namely expenditure, education, availability of labor, age,
transportation, communication, market, and capital have no significant effect
on farmers' decisions to participate in forest and land rehabilitation
programs. The model obtained from the results of the logistic regression
analysis with the coefficient values in Table 5. is as follows:
�-8.729 + 0.0001X1
+ 0.0001X2 � 2.853X3 � 0.766X4 + 0.041X5
+ 0.114X6 + 15.137X7 � 2.280X8 + 0.161X9
� 1.410X10 � 4.149X11
From Table 4. It is known that the significant variable
is the income variable (X1) with a significant value of .025 which can be
interpreted that a decrease or increase in farmers' income can affect the
coffee farmers' decision to participate in forest and land rehabilitation
programs, this is in line with the research. conducted by Yohan Putirulan
(2017) where the lower the income, the higher the participation. The land area
variable (X3) with a significant value of .026, and the location variable with
a significant value of .014.
In logistic regression analysis, it is
necessary to test the classical assumption in the form of a multicollinearity
test. If among the independent variables there are no independent variables
that have a high correlation with each other, it can be concluded that there is
no multicollinearity disorder in the research model.
Table 5. Multicollinearity Test
|
Coefficientsa
|
|
|
Model
|
Unstandardized Coefficients
|
Standardized Coefficients
|
t
|
Sig.
|
Collinearity Statistics
|
|
|
B
|
Std. Error
|
Beta
|
Tolerance
|
VIF
|
|
|
1
|
(Constant)
|
.086
|
.201
|
|
.427
|
.670
|
|
|
|
|
Income
|
5.391E-9
|
.000
|
.170
|
3.053
|
.003
|
.630
|
1.587
|
|
Expenditure
|
-9.687E-11
|
.000
|
-.001
|
-.025
|
.980
|
.737
|
1.357
|
|
Land
area
|
-.075
|
.025
|
-.162
|
-3.034
|
.003
|
.682
|
1.466
|
|
Education
|
-.013
|
.032
|
-.019
|
-.401
|
.689
|
.907
|
1.102
|
|
Kindergarten
Availability
|
.001
|
.014
|
.004
|
.080
|
.936
|
.936
|
1.068
|
|
Age
|
.001
|
.002
|
.024
|
.514
|
.608
|
.880
|
1.136
|
|
Location
|
.844
|
.046
|
.850
|
18.247
|
.000
|
.899
|
1.113
|
|
Transportation
|
-.023
|
.059
|
-.018
|
-.387
|
.700
|
.910
|
1.098
|
|
Communication
|
.033
|
.047
|
.033
|
.704
|
.483
|
.880
|
1.137
|
|
Market
|
-.057
|
.064
|
-.041
|
-.889
|
.376
|
.913
|
1.096
|
|
Capital
|
-.016
|
.056
|
-.013
|
-.293
|
.770
|
.964
|
1.037
|
|
a. Dependent Variable: Opt-in
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
The basis for taking the multicollinearity
test can be seen from the tolerance value which if it is greater than > 0.10
then it means that multicollinearity does not occur, apart from the
multicollinearity tolerance value can also be seen from the VIF value which if
the VIF value is less than <10.00 then it means that there is no
multicollinearity .
From Table 5. It is known that the entire
tolerance value of the independent variable is greater than > 0.10 and the
VIF value is < 10.00 it can be said that the independent variable does not
occur multicollinearity.
The significant influence of each independent
variable on the coffee farmer's decision to participate in the forest and land
rehabilitation program in Lengkiti District, Ogan Komering Ulu Regency can be
explained in detail as follows:
1)
Income (X1)
Income is a conjecture variable where income
shows the amount of coffee farmers' income per year. Based on the results of
the logistic regression analysis, the income variable (X1) has a sig value. of
0.025 or an error value of 2.5%. This value indicates that at the 95%
confidence level, the income variable has a significant effect on the coffee
farmers' decision to participate in the forest and land rehabilitation program.
The coefficient of the results obtained is positive (+0.0001) and the value of
the ood ratio or Exp (β) is 1,000. This means that if income increases by
Rp. 1, it will increase farmers' opportunities to participate in forest and
land rehabilitation programs. In other words, the chances of farmers
participating in the forest and land rehabilitation program are 1 times higher
than not participating in the forest and land rehabilitation program assuming
other variables are held constant.
2)
Land Area (X3)
Land area is one of the factors that are
thought to influence coffee farmers' decisions to participate in forest and
land rehabilitation programs. Based on the results of the logit regression
analysis, the variable land area (X3) has a sig value. of 0.026 or an error
value of 2.6%. This value indicates that at the 95% confidence level, the
variable area of land has a significant effect on the coffee farmer's decision
to participate in the forest and land rehabilitation program. The coefficient
of the results obtained is negative (-2.853) and the odd ratio or Exp (β)
value is 0.058. This means that if the land area is reduced by 2 ha, the opportunity
for coffee farmers to participate in forest and land rehabilitation programs
will increase by 58 times. In other words, the chance of farmers participating
in the forest and land rehabilitation program is 58 times higher than not
participating in the forest and land rehabilitation program assuming other
variables are held constant.
3)
Location (X7)
Location is one of the factors thought to
influence coffee farmers' decisions to participate in forest and land
rehabilitation programs. Based on the results of the logit regression analysis,
the location variable (X7) has a sig value. of 0.014 or an error value of 1.4%.
This value indicates that at the 95% confidence level, the location variable
has a significant effect on the coffee farmer's decision to participate in the
forest and land rehabilitation program. The coefficient of the results obtained
is positive (+15,137) and the odd ratio or Exp (β) value is 3750204,202.
This means that if the location increases by 15,137, the opportunity for coffee
farmers to participate in forest and land rehabilitation programs will increase
by 3750204 times. In other words, the chance of farmers participating in the
forest and land rehabilitation program is 3750204 times higher than not
participating in the forest and land rehabilitation program assuming other
variables are held constant.
CONCLUSION
Based on the results of research and
discussion of the analysis of the determinants of coffee farmers' decisions to
participate in the Forest and Land Rehabilitation Program in Lengkiti District, Ogan Komering Ulu Regency, it can be concluded that as many as
120 coffee farmers from the selected sample, it is known that 53 coffee farmers
participated in the Forest Rehabilitation Program and Land In Lengkiti Subdistrict, Ogan Komering Ulu Regency, as
many as 67 coffee farmers who did not participate in the Forest and Land
Rehabilitation Program in Lengkiti Subdistrict, Ogan Komering Ulu Regency, were classified by the model by
55.8%. The variables that influence the coffee farmers' decision-making factors
to take part in the Forest and Land Rehabilitation Program in Lengkiti District, Ogan Komering Ulu Regency, are income variable (X1) with a sig
value of .025, land area variable (X3) with a sig value of .0.26, location
variable ( X7) with a sig value of .014. Variables that do not affect the
determinants of coffee farmers' decisions to take part in the Forest and Land
Rehabilitation Program in Lengkiti District, Ogan Komering Ulu Regency, namely
the expenditure variable (X2), education variable (X4), labor availability
variable (X5), age variable (X6 ), transportation variable (X8), communication
variable (X9), market variable (X10) and capital variable (X11).
REFERENCES
Agustinus, I. B., & Mujahiddin, D. E. (2013).
Implementasi kegiatan penghijauan dalam program rehabilitasi hutan dan lahan
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Received:
01-07-2022�������������������� ��������������� Accepted: 10-07-2022���������������������� ��������������� Published: 26-07-2022������ 