PROBLEMS AND PROSPECTS OF JACKFRUIT CULTIVATORS - A STUDY WITH REFERENCE TO TAMILNADU

 
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

                                                                                                         63
Asia 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

                                                                                                           64
Asia 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.

                                                                                                         65
Asia 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.

                                                                                                         66
Asia 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.

                                                                                                            67
Asia 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.

                                                                                                         68
Asia 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.

                                                                                                          69
Asia Pacific Journal of Research                                      Vol: I Issue XVIII, October 2014
ISSN: 2320-5504, E-ISSN-2347-4793
   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.

REFERENCES
  1. Azad, A.K., Jones, J.G. and Haq, N. (2007), Assessing morphological and isozyme variation of
     jackfruit in Bangladesh, Agroforestry System, 71, pp. 109-125.
  2. Azam, F.M.S., Rahmatullah, M. and Ather-uz-Zaman (2009), Tissue culture of a year-round
     fruiting variety of Artocarpus heterophyllus, Bangladesh, Acta Horticulture, 806(1): 269-276.
  3. Bhatia, S., Siddappa, G.S. and Lal, Giridhari (1956), Product development from the fruits, Indian
     journal agriculture, 25: 408.
  4. Bose, T.K., (1985), Jackfruit. In B. K. Mitra (Ed.), Fruits of India: Tropical and subtropical naya
     prokas, Calcutta, India.
  5. Datta, S.C. and Biswas, S.C. (1972), Utilization of fruits for dietary purposes, Indian Farming, 3:
     pp.527-553.
  6. Guruprasad, T.R., (1981), Studies on systematic selection of jackfruit types, M.Sc.(Hort.) Thesis,
     University Agriculture Science, Bangalore, India.
  7. Jinsu Varghese and M. Haridas (2007), Prospects of Jackfruit Blend Yoghurt Whey, World
     Journal of Dairy & Food Sciences 2 (1), pp. 35-37, 2007.
  8. Konhar, T., Murmu, S. and Maharan, T. (1990), a study on the budding methods of propagation of
     jackfruit, Orissa journal of agricultural research 3(2), pp. 115-119.
  9. N.K. Halder, A.T.M. Farid and M. A. Siddiky (2008), Effect of Boron for Correcting the
     Deformed Shape and Size of Jackfruit, Journal of Agriculture & Rural Development, 6 (1&2), pp.
     37-42.

                                                                                                           70
Asia Pacific Journal of Research                                   Vol: I Issue XVIII, October 2014
ISSN: 2320-5504, E-ISSN-2347-4793
   10. Naik, K.C., (1949), south Indian fruits and their culture, P. Varadachery and Co. Madras,
       pp. 300-302.
   11. Prasad et al., (2009), Effects of high pressure treatment on the extraction yield, phenolic content
       and antioxidant activity of litchi fruit pericarp. International Journal of Food Science and
       Technology 44, pp. 960–966.
   12. Rashid, M.M., M.A. Kadhir and M.A. Hossain (1987), Bangladesher Fal (Fruits of Bangladesh),
       The Rashid publishing house, Joydebpur, Gazipur.
   13. Ribeiro SMR, de Queiroz JH, de Queiroz MELR, Campos FM, Santana HMP, (2007),
       Antioxidant in mango pulp, World J Agric Sci, 6(6), pp. 735–739.
   14. Samaddar HM. (1985), Jackfruit, Fruits of India: tropical and subtropical, Culcutta, India: Naya
       Prokash, pp. 638–649.
   15. Ullah, M.A. and Rahman, M.S. (2008), Study on the Performance of off Season Jackfruit
       Germplasm, Research Report on Horticultural Crop, pp. 243-244, Horticulture Research Center,
       BARI, Joydebpur.

                                                                                                       71
You can also read
Next slide ... Cancel