PROBLEMS AND PROSPECTS OF JACKFRUIT CULTIVATORS - A STUDY WITH REFERENCE TO TAMILNADU
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Asia Pacific Journal of Research Vol: I Issue XVIII, October 2014
ISSN: 2320-5504, E-ISSN-2347-4793
PROBLEMS AND PROSPECTS OF JACKFRUIT CULTIVATORS
- A STUDY WITH REFERENCE TO TAMILNADU
Mr.T.PANDIAN
Research Scholar and Assistant Professor, Department of Business Administration, DDE,
Annamalai University, Annamalainagar-608 002, Mobile-9486344976
Dr.K.SOUNDARARAJAN
Associate Professor and Research Guide, Department of Business Administration, DDE,
Annamalai University, Annamalainagar-608 002
ABSTRACT
Jackfruits are soft and delicate, are more prone to damage and spoilage during handling and
storage. Due to their high perishability, the postharvest management required is also high. It starts right
from harvesting, field handling, transportation to pack house, pre-cooling and subsequent storage. Cold
chains are essential component of horticultural postharvest infrastructure. It ensures maintenance of
freshness of produce for extended period of storage. This article highlights problems and prospects of
jackfruit cultivators-a study with reference to Tamilnadu.
KEYWORDS
Value-Added Food Industry, Jackfruit Cultivation, Harvesting Jackfruit, Problems and Prospects,
Local Natural Resources, Fruit Producing.
INTRODUCTION
Jackfruit cultivation is one of the most important agricultural products in the country, which plays
an important role in the economic development. The jackfruit cultivators are facing many problems in
cultivation and harvesting season. During cultivation period the problem of rainfall, selection seed and
financial assistance and harvest season the fruits losses due to lack of preservation. If the technique of
manufacturing and preserving food subsistence in an effective manner with a view to enhance their shelf
life, improve quality as well as make them functionally more useful. The jackfruit cultivation is based on
local natural resources and indigenous knowledge and skill of the people. This sector directly contributing
to income and employment generation and also induces output and employment growth indirectly through
63Asia Pacific Journal of Research Vol: I Issue XVIII, October 2014
ISSN: 2320-5504, E-ISSN-2347-4793
its linkages with other sectors. Jackfruit processing can be done at home or in food processing industry.
Besides reducing unnecessary wastage and losses of perishable items it helps in value addition, raising
rural income by generating direct and indirect employment and diversifies the rural economy. The most
important point in the jackfruit cultivation is that a substantial portion being rural based and it has very
high employment potential with significantly lower investment.
JACKFRUIT
The scientific name of the jackfruit trees is Artocarpus heterophyllus, of the Moraceae family,
which produces edible fruit.
STATEMENT OF THE PROBLEM
India is the one of the largest and most varied fruit producing nations in the world. Jackfruit is one
of the most significant tropical fruit produced in India. The jackfruit cultivation is many centuries old and
the farmers are unaware of the improved cultivation. They have many problems relating to cultivation,
harvesting and marketing. In the cultivation stage they have problem with decrease in rainfall, natural
calamities causes fluctuation in production and frequent drought conditions hampered the development of
agriculture. In the harvesting stage perishable nature of fruits are wasted due to lack of storage facilities
and lack of effective processing or preservation techniques, leads to high wastage. The pest and disease
problem also results low output and poor quality of fruits. In the marketing stage they have many
problems relating price fluctuation and lack of marketing problems. In Tamil Nadu is far from tapping the
potential of processing and exporting dried jack fruits processors and exporters currently not available.
Dried fruits have a large number of end-users including use in the dried fruit and nut industry. Besides, the
inadequate rainfall of mansoon also causes fluctuation in production and frequent drought conditions
hampered the development of jackfruit cultivation. In this backdrop this study is attempts to understand
the problems and prospects of jackfruit cultivation in Tamil Nadu and to suggest suitable measures to
improve this sector.
OBJECTIVES OF THE STUDY-
The objectives of the study are problems and prospects of jackfruit in select district of Tamilnadu.
However, the following are the specific objectives of the study:
1. To identify the important problems faced by jackfruit cultivators in Tamil Nadu.
2. To ascertain the important prospects of jackfruit cultivators in Tamil Nadu.
TESTING OF HYPOTHESES
The following null hypothesis are framed and tested
1. Ho1: There is no significant difference between problems of Jackfruit cultivators on the basis of
demographic profile of the respondents.
2. Ho2: There is no significant difference between prospects of Jackfruit cultivators on the basis of
demographic profile of the respondents.
METHODOLOGY
The study is based on both primary and secondary data. The sources of secondary data are
publications and seasonal crop report in Tamil Nadu and other Research Reports, Books, Journal articles
and so on. Primary data are collected for understanding the problems of jackfruit cultivators in Tamil
Nadu. In Tamil Nadu jackfruit cultivation are mainly concentrated in Cuddalore, Kanyakumari, Dindigul,
Ariyalur and Pudukottai districts and they account for 73.21 per cent of area under jackfruit cultivation in
64Asia Pacific Journal of Research Vol: I Issue XVIII, October 2014
ISSN: 2320-5504, E-ISSN-2347-4793
Tamil Nadu. In which Cuddalore account 27.22 per cent, Kanyakumari account 23.79 per cent and
Dindigul account 13.81 per cent of the total jackfruit cultivation area of Tamil Nadu. Therefore Primary
data were collected from these three districts. Here the study, based on primary survey, concentrates only
on large area of jackfruit cultivation in Tami Nadu.
SAMPLING DESIGN
The Proportionate Stratified Sampling Method was used to select the respondents in jackfruit
cultivators in Tamilnadu. This sampling involved in drawing sample from each stratum in proportion to
the latter‟s share in the total jackfruit cultivators. 2 per cent of each category of jackfruit cultivators
selected for districts namely Cuddalore, Kanyakumari and Dindigul were selected for the study. The
sample size constituted 2 per cent of the universe i.e., 540 entrepreneurs. The universe constituting 27,000
jackfruit cultivators, were classified as shown in the following Table 1.
Table 1 Selection of Sample Respondents
S. No Name of the Districts Total Jackfruit Cultivators Selection of Sample Size
(2%)
1. Cuddalore 12,450 249
2. Kanyakumari 9,910 198
3. Dindigul 4,640 93
Total 27,000 540
Source: Office Records for Committees -2014.
ANALYSIS OF PROBLEMS AND PROSPECTS OF JACKFRUIT CULTIVATORS
The collected data were summarised and scrutinized carefully for statistical analysis using SPSS
package, is computer software for analyzing social science data. In order to achieve the meaningful
conclusions, tabular technique of analysis was intensively used because of its simplicity. Finally, relevant
Tables were prepared according to the requirement of data presentation to meet the objectives of the study.
FACTOR ANALYSIS FOR PROBLEMS OF JACKFRUIT
Analyses were done with the main objectives to find out the underlying common factors among 9
variables included in this study. Principal component factoring method with variance rotation was used for
factor extraction. A two factors solution was derived using a score test.
Table shows the results of the factor analysis. Name of all the 9 variables and their respective
loadings in all the two factors are given in the table. An arbitrary value of 0.38 and above is considered
significant loading. A positive loading indicates that greater the value of the variable greater is the
contribution to the factor. On the other hand, a negative loading implies that greater the value, lesser its
contribution to the factor or vice versa. Keeping these in mind, a study of the loadings indicates the
presence of some significant pattern. Effort is made to fix the size of correlation that is meaningful, club
together the variables with loadings in excess of the criteria and search for a concept that unifies them,
with greater attention to variables having higher loadings. Variables have been ordered and grouped by the
size of loadings to facilitate interpretation and shown in table.
Factor analysis was done among 9 variables used in the study. The principal component analysis
with varimax rotation was used to find out the percentage of variance of each factor, which can be
grouped together from the total pool of 9 variables considered in the study. The results are given in table
and column 1 shows the serial number, „2‟ shows the name given for each factor, „3‟ shows variables
loaded in each factor, „4‟ gives the loadings, „5‟ gives the communality for each variables, „6‟ gives the
Eigen value for each factor and „7‟ gives the percentage of variance found out through the analysis.
65Asia Pacific Journal of Research Vol: I Issue XVIII, October 2014
ISSN: 2320-5504, E-ISSN-2347-4793
Table 2 Communalities
Variables Initial Extraction
Choosing the seedlings 1.000 .827
Purchase of seedlings 1.000 .429
Financial Problem 1.000 .514
Maintenance Problem 1.000 .722
Reasons for low yield in Jackfruit 1.000 .572
Labour Problems 1.000 .496
Selling problem 1.000 .726
Problems in Direct selling of regulated markets/ Mundy 1.000 .627
Selling problems with brokers 1.000 .881
Extraction Method: Principal Component Analysis.
Source: Computed from the primary data
From the Table 2 shows that in the data interpretation on “problems of jackfruit cultivators”
through factor analysis, out of nine variables, “selling problems with brokers,” variable got high
communality value (0.881). It means extracted factors are able to explain low variance in that the variable
more effectives than other variables and “purchase of seedlings” variable got lowest communality value
(0.429). It means that the extracted factors are not able to explain much variance in that variable. Such
variable may be dropped from the analysis.
Table 2(a)
Total Variance Explained
Initial Eigen values Extraction Sums of Squared Loadings
Component Total % of Variance Cumulative % Total % of Variance Cumulative %
1 3.478 38.641 38.641 3.478 38.641 38.641
2 1.240 13.780 52.421 1.240 13.780 52.421
3 1.076 11.953 64.374 1.076 11.953 64.374
4 .903 10.029 74.402
5 .709 7.877 82.279
6 .621 6.895 89.174
7 .455 5.056 94.230
8 .307 3.414 97.644
9 .212 2.356 100.000
Extraction Method: Principal Component Analysis.
Source: Computed from the primary data
Table 2 (a) shows that percentage of variance in respect of 9 variables in problems in jackfruit
cultivation. These variables have been rotated to ascertain cumulative percentage of variance. The factor 1
causes 38.641 per cent of variance factor 2 causes 13.780 per cent of variance and factor 3 causes 11.953
per cent of variance in problems in jackfruit cultivators. The overall three factors cumulatively contribute
64.374 per cent.
66Asia Pacific Journal of Research Vol: I Issue XVIII, October 2014
ISSN: 2320-5504, E-ISSN-2347-4793
Table 2 (b)
Component Matrixa
Factors Components
1 2 3
Choosing the seedlings .193 .448 .767
Purchase of seedlings .652 -.055 -.028
Financial Problem .666 .206 -.167
Maintenance Problem .785 .315 -.076
Reasons for low yield in Jackfruit .500 -.185 .536
Labour Problems .616 .000 -.342
Selling problem .825 -.212 -.004
Problems in Direct selling of regulated
.789 -.041 -.056
markets/ Mundy
Selling problems with brokers -.150 .902 -.211
Extraction Method: Principal Component Analysis, a. 3 components extracted.
Source: Computed from the primary data
The factors are arranged based on the Eigen value viz
F1 (Eigen value 3.478)
F2 (Eigen value 1.240)
F3 (Eigen value 1.076)
These three factors are described as this model has a strong statistical support and the Kaiser-Maya-
Olkin (KMO) test of sampling adequacy concurs that the sample taken to process the factor analysis is
statistically sufficient (KMO value = 0.9241).
CORRELATION ANALYSIS
Correlation analysis deals with the relationship between two or more variables. Correlation
analysis significantly related to prospects of jackfruit cultivation and Sub Factors.
Table 3 Correlation Analysis
Prospects in Financial Marketing Technical
Factors
Cultivation Prospects Prospects Prospects
Prospects in
.273** .404** .162**
Cultivation
Financial Prospects .273** .313** .282**
Marketing Prospects .404** .313** .250**
Technical Prospects .162** .282** .250**
Source: Computed from Primary data
It is noted from the Table 3 shows that the prospects jackfruit cultivation and marketing indicates
significantly and positively correlated to all sub factors. It indicates the relationship between the prospects
in cultivation and significantly correlated to prospects in cultivation, financial prospects, marketing
prospects and technical prospects.
67Asia Pacific Journal of Research Vol: I Issue XVIII, October 2014
ISSN: 2320-5504, E-ISSN-2347-4793
FACTOR ANALYSIS FOR PROSPECTS OF JACKFRUIT
Analysis was done with the main objectives to find out the underlying common factors among 4
variables included in this study. Principal component factoring method with variance rotation was used for
factor extraction. Any two factors solution was derived using a score test.
Table shows the results of the factor analysis. Name of all the 4 variables and their respective
loadings in all the two factors are given in the table. An arbitrary value of 0.42 and above is considered
significant loading. A positive loading indicates that greater the value of the variable greater is the
contribution to the factor. On the other hand, a negative loading implies that greater the value, lesser its
contribution to the factor or vice versa. Keeping these in mind, a study of the loadings indicates the
presence of some significant pattern. Effort is made to fix the size of correlation that is meaningful, club
together the variables with loadings in excess of the criteria and search for a concept that unifies them,
with greater attention to variables having higher loadings. Variables have been ordered and grouped by the
size of loadings to facilitate interpretation and shown in table.
Factor analysis was done among four variables used in the study. The principal component
analysis with varimax rotation was used to find out the percentage of variance of each factor, which can be
grouped together from the total pool of 9 variables considered in the study. The results are given in table
and column 1 shows the serial number, „2‟ shows the name given for each factor, „3‟ shows variables
loaded in each factor, „4‟ gives the loadings, „5‟ gives the communality for each variables, „6‟ gives the
Eigen value for each factor and „7‟ gives the percentage of variance found out through the analysis.
Table 4 Communalities
Variables Initial Extraction
Prospects in cultivation 1.000 .474
Financial Prospects 1.000 .476
Marketing Prospects 1.000 .564
Technical Prospects 1.000 .337
Source: Computed from Primary data, Extraction Method: Principal Component Analysis.
From the Table 4 shows that the data interpretation on “prospects of jackfruit cultivators” through
factor analysis, out of four variables, “marketing prospects,” variable got high communality value (0.564).
It means extracted factors are able to explain low variance in that the variable more effectives than other
variables and “technical prospects” variable got lowest communality value (0.337). It means that the
extracted factors are not able to explain much variance in that variable. Such variable may be dropped
from the analysis.
Table 4 (a) Total Variance Explained
Initial Eigenvalues Extraction Sums of Squared Loadings
Component % of % of
Total Cumulative % Total Cumulative %
Variance Variance
1 1.851 46.267 46.267 1.851 46.267 46.267
2 .873 21.837 68.103
3 .692 17.308 85.412
4 .584 14.588 100.000
Source: Computed from primary data
Extraction Method: Principal Component Analysis.
68Asia Pacific Journal of Research Vol: I Issue XVIII, October 2014
ISSN: 2320-5504, E-ISSN-2347-4793
Table 4 (a) shows the percentage of variance in respect of four variables in prospects of jackfruit
cultivation. These variables have been rotated to ascertain cumulative percentage of variance. The factor
causes 42.267 per cent of variance in problems in jackfruit cultivators.
Table 4 (b) Component Matrixa
Variables Component
1
Prospects in cultivation .688
Financial Prospects .690
Marketing Prospects .751
Technical Prospects .580
Extraction Method: Principal Component Analysis.
Source: Computed from primary data, a.1 components extracted.
The factor is arranged based on the Eigen value viz
F1 (Eigen value 1.851)
These three factors are described as this model has a strong statistical support and the Kaiser-Maya-
Olkin (KMO) test of sampling adequacy concurs that the sample taken to process the factor analysis is
statistically sufficient (KMO value = 0.8729).
Table 5 Descriptive Statistics and Rank Analysis
Variables N Mean Ranks SD SE Skewness Kurtosis
Prospects in Cultivation 540 48.67 A 5.87 0.25 0.45 0.05
Financial Prospects 540 14.54 C 2.80 0.12 0.96 0.28
Marketing Prospects 540 21.74 B 3.99 0.17 0.09 0.33
Technical Prospects 540 11.08 D 2.03 0.09 0.11 0.07
Source: Computed from Primary data
It is noted from the Table 5 shows that prospects in cultivation (48.67) scored higher mean value
than other groups. It indicates prospects in cultivation groups have high level of mean score than other
groups and Skewness value is 0.45 and Kurtosis value is 0.05. In the case of Prospects in cultivation rank
is A, Marketing Prospects rank is B, Financial Prospects rank is C and Technical Prospects rank is D.
SUGGESTIONS
1. To distribute various machineries like Hand operated Jackfruit cutter, Jackfruit cutter machine,
Electric Cabinet dryer, Sealing machine, Wet grinder are essential for processing the fruits into
various value added products of good demand.
2. More importance is needed to provide adequate and timeliness of credit availability to the
cultivators.
3. Enhancing awareness on improved production and postharvest handling techniques can be made
possible through training activities.
4. To enhance the poor infrastructure facilities like multipurpose cold storage facility, packaging and
transportation.
5. Educating the farmers about the improved cultivation and marketing practices through an
integrated extension network.
6. The horticulture department has to educate the farm cultivation and support to farm investment.
7. Development of farmer organizations to the help manage village-level investment and to enable
farmers to have a greater voice in national and local policy.
69Asia Pacific Journal of Research Vol: I Issue XVIII, October 2014
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8. The government has to encourage corporate sectors to install food processing factory at the major
production districts of Cuddalore, Kanyakumrai and Dindigul.
9. The horticulture department has to educate the farm cultivation and support to farm investment.
10. Organize linkages between entrepreneurs and financial institutions for financing Jackfruit
processing.
11. The government has to encourage corporate sectors to install food processing factory at the major
production districts of Cuddalore, Kanyakumrai and Dindigul.
12. To encourage the processing of value added products in commercial scale for their livelihood
enhancement.
13. There is a need to strengthen the market capacities of the cultivators, including their access to
good-quality roads and an efficient transport system, as well as to market information.
Strengthening market capacities of the cultivators can be achieved through more investments in
education and technical training.
CONCLUSION
The present study concluded that, the major problem in jackfruit cultivation is inefficient handling
and transportation; poor technologies for storage, processing, and packaging; involvement of too many
diverse actors; and poor infrastructure. Jackfruits are soft and delicate, are more prone to damage and
spoilage during handling and storage. Due to their high perishability, the postharvest management
required is also high. It starts right from harvesting, field handling, transportation to pack house, pre-
cooling and subsequent storage. Cold chains are essential component of horticultural postharvest
infrastructure. It ensures maintenance of freshness of produce for extended period of storage. The
cultivation requires linking operations more closely and systematically, modernizing marketing
infrastructure and technologies, capacity building of the cultivators, and strengthening the policy for better
marketing.
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