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© 2015 Asian Economic and Social Society. All rights reserved
ISSN(P): 2304-1455/ ISSN(E): 2224-4433
Volume 5(8), 175-186
175
STATUS AND CONSTRAINTS OF JUTE CULTIVATION IN BANGLADESH:
AN EXPERIENCE FROM SELECTED UPAZILAS UNDER CHANDPUR
DISTRICT
Mohammad Muzahidul Islam
PhD Candidate; College of Humanities and Development Studies (COHD), China Agricultural
University, Beijing, China
&
Assistant Professor; Department of Management Studies, Patuakhali Science and Technology
University, Bangladesh
Jian Xiaoying
Professor; College of Humanities and Development, China Agricultural University, Beijing, China
Md. Ektear Uddin
Associate Professor; Agricultural Extension and Rural Development, Patuakhali Science and
Technology University, Bangladesh
Faruk Bhuiyan
Assistant Professor; Department of Business Administration, International Islamic University
Chittagong, Bangladesh
Abstract1
This study aimed at exploring the obstacles of jute cultivation under Chandpur Districts in
Bangladesh by the principle component analysis (PCA). The required data were collected through
structured interview schedule from 803 jute farmers sampled conveniently. Though jute diversified
industry has been seen as potential since the last decade in Bangladesh, raw jute production is not
satisfactory due to several obstacles. The study has identified three categories of constraints that
hinder sustainable jute production. These are lack of capital and inputs, knowledge and natural
resources, and market information. Basically farmers cultivate jute for earning profit but they are
not conscious about forward market. Lack of jute diversification knowledge is the unique findings
of this study. The findings may help policy makers and stakeholders for taking effective decision in
addressing the barriers to jute cultivation in Bangladesh.
Keywords: Constraints, jute cultivation, jute diversification, Bangladesh
1. INTRODUCTION
In the history of human civilization jute occupies a unique position in the array of natural fiber. Jute
is very vital sector in agricultural, industrial, and commercial context in Bangladesh. It was once
known as the golden fiber of Bangladesh, since it was the most important cash crop for the country.
Bangladesh enjoys comparative advantages in jute production over other countries in the world. In
Corresponding author‟s
Name: Mohammad Muzahidul Islam
Email address: muzahidhrm@gmail.com
Asian Journal of Agriculture and Rural Development
journal homepage: http://aessweb.com/journal-detail.php?id=5005
Asian Journal of Agriculture and Rural Development, 5(8)2015: 175-186
176
Bangladesh, jute is locally popular by the name Paat. Jute fiber is produced mainly from two
commercially important species, namely White Jute or Deshi Paat (Corchours capsularis), and
Tossa Paat or Boghi Jute (Corchorus olitorius). Jute occupies approximate 6% of the total land
area and about 10% of the total agriculture land area of the country. The share of jute in agriculture
export is around 39% (IJSG, 2012). Bangladesh produces around 33% of the total global jute and is
the largest exporter in the world with around 29% as raw jute. Moreover, Bangladesh earns foreign
currency by exporting raw jute, jute manufactures, jute carpet, yearns and twine. Over the past six
years jute export has been doubled from USD 326.15 million in 2003-2004 to USD787.99 million
in 2009-2010. Moreover, there is increasing raw jute export growth has observed during 2010-11 to
2011-12 by 1.1% and 2.36% respectably but 1.1% decreased in 2012-13 (IJSG, 2013). It indicates
that jute sector is prospective in Bangladesh but with some challenges. In Bangladesh,
approximately 32 million people are involved in agricultural activities of which 12.5% is involved
with jute sector (Molla, 2014). Being ignored by adequate government policy, Bangladesh‟s jute
glory has been grabbed by the mighty synthetic corporate house. As jute sector is economically
important in Bangladesh, any problem of this sector should be studied and solved very carefully as
early as possible. However, the researchers are not much aware any research on jute cultivation in
Chandpur District. Nowadays jute cultivation and raw jute marketing is not satisfactory because of
enormous challenges which are still unaddressed. The recent success of small jute diversified
industries show that diversified finished jute products exporting can revive the weaken jute sector
and livelihoods of the jute famers. However, there are no well documented research evidences of
solving the problems of jute sector through alternative approach other than the raw jute export.
Nevertheless, a holistic problems identification is the precondition for looking appropriate ways of
addressing them. As problems are multifaceted, policy intervention for sustainable and vibrant jute
sector deserve knocking at critical gates to improve the lives of farmers and sustainable
development of the country.
1.1. Literature review of jute cultivation constraints
Based on research objectives, researchers have investigated obstacles of jute growers in jute
cultivation in different previous research. Hussain et al. (2002) investigated various problems in
producing jute likely low market price, low demand of jute, farias influence of government
purchasers, and inadequate grading knowledge of the farmers etc. Another scholar has also found
different constraints in jute producing such as physical (limited water availability etc) technological
(lack of more productive technical knowledge and know how) etc. (Hussain, 1969). According to
Skidar and Banerjee (1990), jute farmers face several obstacles such as insufficient irrigation
facilities, high inputs price, insufficient credit facilities, and lack of market and particularly scarcity
of clean water for jute retting in their study. For retting, farmers usually have to go away from his
house; it will incur the cultivation cost. Hossain et al. (2002) found that agricultural knowledge and
attitude are significantly related and these directly impact in farmer‟s decisions making. Raw jute
has been passing through challenges due to low and unstable price at the growers' level (Dass,
1999). Anonymous (2010) found that the jute sector of Bangladesh is hindered by lack of
availability of quality seed, retting problems, mill efficiency, product diversification issues and
market linkages. In Nepal, the area and production of land has decreased in jute production due to
several obstacles such as unstable and low price of raw jute, unavailability of quality seed, labor
shortage during peak season, weed problem, irrigation at sowing period and disease complex (wilt)
etc. (Ghimire and Thakur, 2013). This study has considered some contemporary issues in research
such as diversified use, knowledge and natural resource issues of farmers which contributing jute
cultivation decisions. It is noted that researchers in not much aware any academic research how
these factors are contributing in jute cultivation decisions by jute farmers.
1.2. Status of jute cultivation
Bangladesh produces best quality jute fiber in the world. The land of Bangladesh is suitable for jute
production. It should be noted that Bangladesh stood first position in producing jute among the
countries of the world. For the last couple of years, India took the first position of raw jute
Asian Journal of Agriculture and Rural Development, 5(8)2015: 175-186
177
production defeating Bangladesh. Recently, Bangladesh government has developed “Mandatory
Jute Packaging Act 2010” and “Mandatory Jute Packaging Rules 2013.” Furthermore, demands of
jute products have increased significantly at home and abroad. But raw jute production is not
satisfactory at farm levels. Farmers are reluctant to cultivate jute due to obstacles at different phases
of the cultivation. The statistics shows that jute cultivation area has been declined gradually from
84 hectare in 2011 to 74.36 hectare in 2014. On the other hand, jute production has been reduced
from 7.09 metric tons to 6.66 metric tons during the last four years (Figure 1).
Figure 1: Jute cultivation area and production trends in Bangladesh
Source: IJSG (2012) and DAE (2015)
More than 80% of world jute grows in the Ganges delta of Bangladesh which has been categorized
in to three areas such as- Jat area (Brahmaputra Alluvium), the Nothern area (Teesta Silt) and the
Districts area (Ganges Alluvium). The Jat area covers the grater Dhaka, Comilla and Mymensingh
districts. The Northern area comprises grater Rangpur, Dinajpur, Bogra, and Rajshahi district. The
District areas are composed of greater Faridpur, Jessore, Pabna and Kustia. Jat area contributes to
41.69% of total raw jute production whereas Northern and Districts area contributes 30.08% and
28.23% respectively. But the picture has been changed over the couple of decades. District area
accounted for 63.72% of total jute cultivation area where as Jat area contributed only 17.87%
during 2009-10 (Molla, 2014). The total cultivated land of Chandpur district was 2666.88 hectare
during 2008-09 and 4783.79 hectare during 2010-11. The total jute cultivated land area decreased
more than 446.32 hectare during 2013-14 compare to the year 2010-11/12 (BBS, 2013).
Furthermore, the targeted area is only 4714 hectare in 2015-16 (DAE, 2015). Despite conducive
environment for jute cultivation, the size of jute cultivation land is decreasing in Chandpur Districts
over the last few years. The government and policy makers needs to be well informed about the
status of jute cultivation as well as causes of deviation that undermine jute production and
livelihoods of the farmers. If farmers get the motivation and benefits, they will cultivate jute. So it
is very urgent to address farmers‟ problems and obstacles in growing jute. Bangladesh can enjoy
and lead the natural fiber market through jute production and by making diversified eco-jute
products. The policy makers and authorities may take urgent steps to resolve jute cultivation
obstacles.
4.48
4.57
4.37
4.08
3.9
4.02
4.19
4.41
4.21
4.17
7.09
7.6
6.81
6.66
7.57
6.73
45
47
44
44
59
46
48
46
46
50
84
80.03
76.11
74.36
80.14
75.01
0
10
20
30
40
50
60
70
80
90
Area
('00000h
a)
Asian Journal of Agriculture and Rural Development, 5(8)2015: 175-186
178
2. MATERIALS AND METHODS
The study was conducted in purposively selected four upazillas (sub-district) of Chandpur Districts.
These four Upazillas are Chandpur Sadar, Matlab Dakkhin, Kachua and Hajiganj. Purposive
selection was favoured because maximum numbers of jute growers are located in these Upazillas.
Moreover, the areas are familiar to the researcher. All jute farmers under these four upzillas were
considered as population. To get generalizable data in short time 803 jute growers were sampled
following a convenience sampling. Data were collected through a structured interview schedule
from accidentally meet available jute farmers during February to March of 2015. The researchers
were guided by local NGO workers who have informal relationship with the jute farmers. A five
point Likert scale was used to get the ratings against 31 selected constraints. Data were entered into
computer package SPSS 17 version for analysis. Descriptive statistics like frequency distribution,
mean and standard deviation were used to analyze the socio-economic status of the jute growers
whereas factor analysis was performed to identify the major constraints for jute cultivating in the
selected areas.
3. RESULT AND DISCUSSION
A summary of nine characteristics of the respondents has been presented in table 1. The
information of the table shows that the majority of the respondents (54.1%) were at middle aged
(31-50 years), 5.8% respondents were young (up to 30 years), the remaining were old age (above
51 years). Women respondents of the survey were about 8.1%. Medium sized (5-9) family (65%)
dominates in the locality. About 75.3 % household possess 1-3 family labor. In addition, more than
half of the respondent‟s farm size is between 10.1-20 decimal (0.0409 to 0.081 hectares) and almost
50% of the respondents possess up to up to 6 decimals (0.0243 hectares) land for jute cultivation.
The table 1 also indicates that 84.6% respondents have more than 10 years experiences in jute
cultivation. About half of the respondents have annual income BDT.35000 to 75000. Traditionally
most of the respondents (57.90%) prefer to cultivate native varieties of jute locally named as Shutli.
3.2. Constraints of Jute cultivation
Based on literature review and expert opinion, the study has chosen thirty one (31) probable
constraints of jute cultivation in Bangladesh (Table 4). Respondents‟ opinions regarding the
constraints were matched with a 5-point Likert scale. Principal Component Analysis (PCA) has
been operationalized to extract perceived constraints of jute cultivation in reduced form. From the
correlation matrix [appendix table-1], it has been seen that some of the items have scores more than
0.3 and hence it is appropriate for factor analysis (Tabachnick and Fidel, 2007). In addition, for
checking data normality and sample advocacy, Kaiser-Mayer-Olkin (KMO) measure and Bartlett
test of Sphericity is frequently used by different practitioners and researchers. If Bartlett‟s test of
Sphericity is large and significant and the KMO is greater than 0.6 then factorability is assumed.
High value of KMO between 0.5 and 1.0 indicates the data feasibility for factor analysis (Leech et
al., 2005). As the study found 0.631 from KMO test and significant Bartlett‟s test of Sphericity as
P< 0.01 (see table-2), hence the study goes for operating factor analysis.
Asian Journal of Agriculture and Rural Development, 5(8)2015: 175-186
179
Table 1: Demography and Characteristics of jute farmers
Characteristics of jute farmers
f
%
Range
M
SD
Age (in full years)
Below 30
48
5.8
31-50
437
54.1
20-69
47.90
7.86
51 Above
318
39.5
Gender
Male
738
91.9
-
-
-
Female
65
8.1
Family Members
1-4
43
5.3
5-9
525
65.4
2-16
8.69
2.24
10 and above
235
29.27
Household Labor
1-3
605
75.3
1-5
2.63
0.89
4-6
198
24.7
Total Farm size (in decimal)
Below 10
18
2.22
10.1-20
408
50.9
3-180
28.25
24.93
20.01-30
250
31.1
Above 30
127
15.8
Jute cultivation Area ( in decimal)
Below 6
401
49.94
6-12
386
48.07
3-40
7.63
3.76
Above 12
16
1.99
Experience of jute production
Less than 5 years
56
7.0
5-10 years
68
8.4
1-40
21.96
9.73
Above 10 years
679
84.6
Annual House Income (000‟ BDT)
Less than 35
228
28.4
35 -75
428
53.3
15-200
65.78
38.81
Above 75
147
18.3
Name of varieties
Jute (Deshi )
465
57.90
Jute (Tossa)
298
37.12
-
-
-
Jute (Mesta /kenaf)
40
4.98
Source: Field Survey
Table 2: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy
0.631
Bartlett's Test of Sphericity
Approx. Chi-Square
3475.567
Df
465
Sig.
0.000
To identify the optimum number of components, authors have tested two different options. Kaiser‟s
criterion, as one of two options, suggests extracting the factors with Eigen value of equal to or more
than one. The PCA shows that there are 12 factors consist of Eigen values more than 1 [see table-
3]. These twelve factors explain 59.528 percent of variance (from cumulative percentage column in
total variance explained table). However, scree-plot is another option that can facilitate researchers
to extract the right factors for the further study. From the scree-plot, it has been seen that 5
components exist above the third elbow [appendix figure-1]. Therefore, it suggests five categories
of factors significantly hinder jute cultivation in Bangladesh.
Asian Journal of Agriculture and Rural Development, 5(8)2015: 175-186
180
Table 3: Total Variance Explained
Component
Initial Eigen values
Extraction sums of squared
loadings
Rotation sums of
squared loadingsa
Total
% of
Variance
Cumulative
%
Total
% of
Variance
Cumulative
%
Total
1
3.728
12.025
12.025
3.728
12.025
12.025
3.266
2
1.730
5.581
17.605
1.730
5.581
17.605
1.915
3
1.607
5.185
22.790
1.607
5.185
22.790
2.168
4
1.466
4.730
27.520
1.466
4.730
27.520
1.586
5
1.442
4.650
32.170
1.442
4.650
32.170
1.495
6
1.372
4.427
36.597
7
1.340
4.324
40.921
8
1.239
3.997
44.918
9
1.191
3.842
48.760
10
1.163
3.752
52.512
11
1.135
3.661
56.173
12
1.040
3.355
59.528
13
0.974
3.142
62.670
14
0.947
3.055
65.725
15
0.895
2.887
68.612
16
0.861
2.778
71.391
17
0.853
2.752
74.143
18
0.798
2.575
76.718
19
0.737
2.378
79.097
20
0.722
2.329
81.425
21
0.707
2.281
83.707
22
0.690
2.225
85.932
23
0.654
2.111
88.042
24
0.596
1.922
89.965
25
0.569
1.835
91.799
26
0.511
1.647
93.446
27
0.475
1.533
94.980
28
0.448
1.445
96.425
29
0.404
1.305
97.730
30
0.377
1.215
98.944
31
0.327
1.056
100.000
Extraction Method: Principal Component Analysis
a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance
From the total variance explained table [see table 3], it has been seen that five components
cumulatively explain 32.17 percent variance whereas twelve components cumulatively explain
59.528 percent variance. In addition, to interpret five components elaborately, Oblimin rotation was
performed which is given in the structure matrix table 4.
Asian Journal of Agriculture and Rural Development, 5(8)2015: 175-186
181
Table 4: Structure matrix
Constraints
Component
1
2
3
4
5
Lack of capital
0.650
No purchasing of jute by govt. and mills
-0.635
Unavailability of quality seeds
0.545
Insufficient credit facilities
0.517
0.483
Lack of transport facilities
-0.507
Lack of subsidies from government
0.484
Lack of suitable land
-0.469
Cyclone
-0.456
Lack of knowledge of crop rotation
-0.397
0.303
High price of inputs
0.335
0.318
Hail storm
-0.353
-0.535
Lack of knowledge on diversified use
0.503
Drought
-0.309
0.498
Lack of knowledge on retting methods
0.485
0.343
Lack of knowledge for jute cultivation
0.385
0.300
Distance market
No grade difference in pricing
0.509
Unavailability of buyer
0.485
Fixed soil (unproductive soil)
-0.372
-0.449
Inadequate knowledge on ecosystem
-0.429
Low price of jute
0.420
Lack of information on market price
-0.359
Strong syndicate among local buyers
0.356
-0.307
Weak extensions service
-0.537
Lack of storage facilities
-0.524
Lack of bargaining power with buyer
-0.460
Highly laborious
0.446
0.358
Scarcity of water
0.314
-0.412
Lack of monitoring in fake seed import
0.620
Lack of processing facilities
0.504
Shortage of knowledge on environmental benefits
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization
The component correlation matrix (table 5) shows the strength of correlation between two
components. Correlation among the variables of the existing study varies from -0.056 to 0.119. It
shows week correlations among the variables as it is less than 0.2. Moreover, the components are
almost independent from or no relation to one another. Hence, the decision of Oblimin‟s five factor
solution is appropriate for the study.
Table 5: Component correlation matrix
Component
1
2
3
4
5
1
1.000
0.119
0.162
-0.036
-0.026
2
0.119
1.000
0.041
-0.038
0.006
3
0.162
0.041
1.000
-0.036
-0.056
4
-0.036
-0.038
-0.036
1.000
0.040
5
-0.026
0.006
-0.056
0.040
1.000
Extraction Method: Principal Component Analysis
Rotation Method: Oblimin with Kaiser Normalization
Asian Journal of Agriculture and Rural Development, 5(8)2015: 175-186
182
From the structure matrix (table 4), it has been seen that eight variables loaded under identifiable
component-1 with negative scores (such as; -0.635, -0.507, -0.372, -0.353, -0.309, -0.469, -0.456, -
0.397) which indicate an opposite relationship between the observed variables and component-1.
As the study deals with the constraints of jute cultivation at selected upzillas under Chandpur
districts in Bangladesh, these negatively loaded variables have excluded from the component-1.
However, component-1 loads five variables with high positive scores of more than 0.4 each: lack of
capital, lack of subsidies from government, insufficient credit facilities (as strongly correlated at
.517 with component-1 than 3), lack of subsidies from government and high price of inputs. By
considering the nature of and the literature on the extracted variables, authors argue financial and
inputs obstacle as one of the constraints behind the jute cultivation in study area.
Similarly, by excluding the variable „hail storm‟ as it is negatively loaded under component-2, the
study realizes another constraint which consists of five variables: lack of knowledge of crop
rotation, lack of knowledge on diversified use, drought, and scarcity of water, lack of knowledge on
modern retting methods (as it is strongly correlated with component-2 than 3), and lack of
knowledge for jute cultivation. Hence, the second broad constraint of jute cultivation in Bangladesh
is knowledge and natural resources obstacle. Similarly, the third broad constraint of jute cultivation
is market and information obstacle which consists of four closely related variables: strong syndicate
among local buyers, low price of jute, no grade difference in pricing, and unavailability of buyers.
Most of the variables under component 4 and 5 are negatively correlated with each other and the
rest of the variables under each component (only 1 and 2 under component- 4 and5 respectively)
are considered as constraints of jute cultivation in study area due to significant constraints. Thus a
fourth constraint is highly labourious. Farmers think that whole process of jute cultivation is
labourious work compare to other crops. Finally the fifth as others constraints are lack of
monitoring in fake seed import and lack of processing facilities. Therefore, broad barriers of jute
cultivation at Chandpur district are lack of sufficient finance and other inputs, knowledge and
natural resources, and market and information
4. CONCLUSIONS
The main findings of this study have generated some important obstacles which have been faced by
jute growers of Chandpur Districts such as lack of capital and inputs, knowledge and natural
resource constraints, and market and information obstacles. These obstacles make difficult in
decision making of jute cultivation in study area. Problems could be minimized by providing
sufficient jute crop credit facilities and subsidies on inputs from government for sustainable jute
cultivation. Awareness program on modern jute retting and cultivation methods, diversified uses of
jute and adoption of climate change effects should be addressed by the concern authority to
minimize the knowledge and natural resource obstacles. However, most of the obstacles could be
removed by promoting jute diversified entrepreneurs and develop its market at home and abroad.
As a result, automatically farmers will get high price, buyer and market benefits. Besides,
promoting farmers to be an entrepreneur or ensuring strong linkage among jute diversified
industries will also minimize the market related obstacles. Finally, it is expected that the results of
this study will be useful for jute farmers, policy makers and researchers to address the
contemporary obstacles of jute cultivation in effectively.
4.1. Recommendations
In view of the concluding remarks, the following suggestion are given for addressing several
obstacles in jute cultivation and for sustainable jute production on the basis of analysis as well as
information gathers from the selected areas.
1. Department of agricultural extension should identify and address farmers problem in
effective way. They can set farmers complain box at local level and respond accordingly.
2. NGO‟s should offer seasonal loan to the jute growers without collateral.
Asian Journal of Agriculture and Rural Development, 5(8)2015: 175-186
183
3. Government can disseminate knowledge through different media or agencies on modern
jute cultivation and environmental changes to address knowledge and natural resources
obstacles.
4. Government also should take initiative for farm mechanization instated of traditional
inefficient agricultural tools to reduce before and after the process of jute cultivation. It
will reduce the lengthy process and make the work easy for farmers. Besides government
should monitor all imported inputs such as seeds, machines, fertilizers etc strictly.
5. Government should take massive roadmap for enhancing jute cultivation at farmer‟s level
by using jute wing of Department of Agricultural Extensions. Extension workers may give
suggestions before and after crop cultivation in selection of quality seed and fertilizer.
Government can be more active by taking help from DAE and Bangladesh Jute Research
Institute for identifying effective strategies in jute cultivation.
6. Contract farming can help to reduce hindrances of the jute cultivation. Through contract
farming farmers will be benefited and they will get security in jute cultivation. It will help
to ensure sustainable jute production.
7. Government should develop and implement Minimum Price Support policy for the poor
jute growers.
8. Government could distribute and subsidies agricultural inputs to the poor jute farmers and
give reward on the basis of jute production.
9. Promoting farmers to be entrepreneurs of diversified jute products through training and
providing other facilities. Here Jute Diversification Promotion Centre (JDPC) can play a
good role. Ultimately jute farmers will be motivated in jute production.
Views and opinions expressed in this study are the views and opinions of the authors, Asian Journal of
Agriculture and Rural Development shall not be responsible or answerable for any loss, damage or liability
etc. caused in relation to/arising out of the use of the content.
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interpretation. Mahwah, NJ: Lawrence Erlbaum.
Molla, M. M. U. (2014). An Economic Analysis of Jute Sector in Bangladesh: Problems and
Prospects. PhD Thesis, Department of Agribusiness and Marketing, Bangladesh
Agricultural University, Mymensingh, Bangladesh.
Skidar, M. F. S., & Banerjee, B. N. (1990). Jute Cultivation in India and Bangladesh: A
Comparative Study, Mital Publication, New Delhi PP 25.
Tabachnick, B. G., & Fidel, L. S. (2007). Using multivariate statistics. 5th edition, Boston; Pearson
Education.
Asian Journal of Agriculture and Rural Development, 5(8)2015: 175-186
185
Appendix
Table 1: Correlation Matrix
LKCR
UB
LSF
LTF
LPF
HL
SW
DM
LBP
NGP
LMP
LKR
LKD
IKE
SKEB
LKJC
WES
LPJ
LSG
LMFS
NPJG
D
H
C
UQS
FS
ICF
HPI
SSLP
LC
LSL
LKCR
1.000
-.074
-.121
.079
.074
.011
.023
.057
-.030
-.007
.180
.018
-.074
.035
-.087
.063
-.007
.000
-.188
.048
.183
.192
-.021
.014
-.160
.183
-.146
-.129
-.041
-.155
.071
UB
-.074
1.000
.006
-.076
-.072
-.025
.029
.037
-.095
.110
-.083
.080
-.061
-.048
.038
.095
-.122
.193
.016
.006
.031
-.055
-.080
-.004
-.009
-.207
.079
.021
.052
.139
.000
LSF
-.121
.006
1.000
-.053
.058
-.105
.130
.018
.091
-.010
-.178
.074
.048
-.072
-.077
.099
.071
.121
-.025
.086
-.065
-.033
-.029
-.089
.118
-.093
.164
.022
.168
.098
-.015
LTF
.079
-.076
-.053
1.000
.009
.089
-.039
.181
-.055
.004
.132
.006
-.018
.004
-.045
.003
.065
-.210
-.150
.041
.232
.148
.159
.213
-.232
.128
-.200
-.091
-.024
-.227
.131
LPF
.074
-.072
.058
.009
1.000
.013
-.083
-.124
.041
.079
.053
-.113
.037
.042
.055
.098
-.152
.033
-.025
.140
.106
.099
-.107
.059
-.041
.054
-.014
-.063
.033
.079
.070
HL
.011
-.025
-.105
.089
.013
1.000
-.163
-.021
-.003
-.046
.072
-.079
-.211
.118
.040
.046
-.064
-.190
.034
.072
.034
.093
.092
.142
.016
.025
.004
.063
-.140
-.046
.037
SW
.023
.029
.130
-.039
-.083
-.163
1.000
-.040
.046
-.025
.071
.149
.180
.005
.031
.050
.140
.104
.061
-.033
-.066
.017
-.057
-.087
.127
-.139
.020
.036
.021
.114
-.014
DM
.057
.037
.018
.181
-.124
-.021
-.040
1.000
-.016
.033
.022
.015
-.191
.051
-.043
-.019
.036
-.052
-.117
-.001
.102
-.115
.151
.094
-.015
.027
-.073
-.048
.006
-.195
.011
LBP
-.030
-.095
.091
-.055
.041
-.003
.046
-.016
1.000
-.016
-.016
-.027
.038
.111
-.004
-.036
.090
.058
-.062
.039
-.098
.057
.090
.102
-.006
.056
-.049
-.042
.008
.101
.120
NGP
-.007
.110
-.010
.004
.079
-.046
-.025
.033
-.016
1.000
-.123
.108
.039
-.125
.069
-.037
.044
.093
-.024
.056
.012
-.021
-.032
-.049
-.088
-.058
.173
.106
.022
-.029
.110
LMP
.180
-.083
-.178
.132
.053
.072
.071
.022
-.016
-.123
1.000
-.056
-.058
.053
.086
-.002
-.041
-.079
.022
.083
.187
.147
.131
.156
-.091
.198
-.151
-.111
-.011
-.255
-.029
LKR
.018
.080
.074
.006
-.113
-.079
.149
.015
-.027
.108
-.056
1.000
.143
.018
.053
.128
-.032
.264
.063
.010
-.095
.080
-.215
-.169
.154
-.160
.322
.175
.027
.178
-.086
LKD
-.074
-.061
.048
-.018
.037
-.211
.180
-.191
.038
.039
-.058
.143
1.000
-.030
-.022
.101
.069
.021
.079
-.073
-.041
.192
-.130
-.058
.005
-.067
.116
.112
-.010
.194
.006
IKE
.035
-.048
-.072
.004
.042
.118
.005
.051
.111
-.125
.053
.018
-.030
1.000
-.027
.073
.080
-.073
.060
-.022
.074
.074
-.069
.027
.085
.080
-.143
-.085
-.127
-.071
.108
SKEB
-.087
.038
-.077
-.045
.055
.040
.031
-.043
-.004
.069
.086
.053
-.022
-.027
1.000
-.003
-.001
.039
.094
.053
-.071
.054
.081
-.014
.059
-.035
.007
.055
.057
-.039
.046
LKJC
.063
.095
.099
.003
.098
.046
.050
-.019
-.036
-.037
-.002
.128
.101
.073
-.003
1.000
.116
.257
.075
.053
-.064
.033
-.183
-.046
.068
-.039
.088
-.047
.027
.148
.008
WES
-.007
-.122
.071
.065
-.152
-.064
.140
.036
.090
.044
-.041
-.032
.069
.080
-.001
.116
1.000
.055
-.002
.160
.032
.121
.032
-.038
-.031
.092
.048
-.029
.038
-.003
-.080
LPJ
.000
.193
.121
-.210
.033
-.190
.104
-.052
.058
.093
-.079
.264
.021
-.073
.039
.257
.055
1.000
.078
.032
-.110
-.114
-.097
-.086
.137
-.178
.140
.136
.104
.217
-.117
LSG
-.188
.016
-.025
-.150
-.025
.034
.061
-.117
-.062
-.024
.022
.063
.079
.060
.094
.075
-.002
.078
1.000
.078
-.241
-.093
-.067
-.166
.104
-.061
.216
.079
-.045
.174
-.194
LMFS
.048
.006
.086
.041
.140
.072
-.033
-.001
.039
.056
.083
.010
-.073
-.022
.053
.053
.160
.032
.078
1.000
.041
-.031
.118
.025
-.159
.045
.042
-.105
-.173
-.026
.131
NPJG
.183
.031
-.065
.232
.106
.034
-.066
.102
-.098
.012
.187
-.095
-.041
.074
-.071
-.064
.032
-.110
-.241
.041
1.000
.184
.151
.322
-.161
.240
-.218
-.110
.021
-.329
.206
D
.192
-.055
-.033
.148
.099
.093
.017
-.115
.057
-.021
.147
.080
.192
.074
.054
.033
.121
-.114
-.093
-.031
.184
1.000
.000
.091
-.097
.023
-.120
-.031
-.039
-.117
.073
H
-.021
-.080
-.029
.159
-.107
.092
-.057
.151
.090
-.032
.131
-.215
-.130
-.069
.081
-.183
.032
-.097
-.067
.118
.151
.000
1.000
.280
-.227
.236
-.266
-.146
-.078
-.239
.091
C
.014
-.004
-.089
.213
.059
.142
-.087
.094
.102
-.049
.156
-.169
-.058
.027
-.014
-.046
-.038
-.086
-.166
.025
.322
.091
.280
1.000
-.268
.135
-.194
-.198
-.142
-.180
.118
UQS
-.160
-.009
.118
-.232
-.041
.016
.127
-.015
-.006
-.088
-.091
.154
.005
.085
.059
.068
-.031
.137
.104
-.159
-.161
-.097
-.227
-.268
1.000
-.130
.211
.169
.037
.315
-.229
FS
.183
-.207
-.093
.128
.054
.025
-.139
.027
.056
-.058
.198
-.160
-.067
.080
-.035
-.039
.092
-.178
-.061
.045
.240
.023
.236
.135
-.130
1.000
-.303
-.145
-.087
-.251
.114
ICF
-.146
.079
.164
-.200
-.014
.004
.020
-.073
-.049
.173
-.151
.322
.116
-.143
.007
.088
.048
.140
.216
.042
-.218
-.120
-.266
-.194
.211
-.303
1.000
.353
.179
.394
-.232
HPI
-.129
.021
.022
-.091
-.063
.063
.036
-.048
-.042
.106
-.111
.175
.112
-.085
.055
-.047
-.029
.136
.079
-.105
-.110
-.031
-.146
-.198
.169
-.145
.353
1.000
.091
.179
-.126
SSLP
-.041
.052
.168
-.024
.033
-.140
.021
.006
.008
.022
-.011
.027
-.010
-.127
.057
.027
.038
.104
-.045
-.173
.021
-.039
-.078
-.142
.037
-.087
.179
.091
1.000
.016
-.013
LC
-.155
.139
.098
-.227
.079
-.046
.114
-.195
.101
-.029
-.255
.178
.194
-.071
-.039
.148
-.003
.217
.174
-.026
-.329
-.117
-.239
-.180
.315
-.251
.394
.179
.016
1.000
-.310
LSL
.071
.000
-.015
.131
.070
.037
-.014
.011
.120
.110
-.029
-.086
.006
.108
.046
.008
-.080
-.117
-.194
.131
.206
.073
.091
.118
-.229
.114
-.232
-.126
-.013
-.310
1.000
Asian Journal of Agriculture and Rural Development, 5(8)2015: 175-186
186
Figure 1: Scree Plot
