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Sambodhi ISSN: 2249-6661
(UGC Care Journal) Vol-43, No.-4, (N) October-December (2020)
Copyright ⓒ 2020Authors 140
INTEGRATED STRATEGIES TO MAKE THE FARMERS’ INTENTION POSITIVE
TOWARDS FARMING PROFESSION: AN MDS APPROACH
Dr. Saroj Kumar Sahoo
Asst. Professor, PG. Dept. of Business Administration, Sambalpur University, Odisha, India
Author is obliged to IMPRESS, ICSSR, New Delhi for the funding of this research work
Abstract
In the words of George Bernard Shaw “there is no love sincerer than the love of food”. But, till now citizens of underdeveloped
and most of the developing countries do not have the dare to show the above said love; and humanity of most of us assigned with
a great question mark. Both the small farmers and retail consumers are not getting justice from so many angles. Thus, problem
statement of this research is given as “can the marketability of regionally surplus agricultural products be improved by integrating
the issues of farmers, social communities and existing agricultural administrative system?” To study this problem, mixed research
design is adopted, where both the quantitative and qualitative studies are involved. For quantitative study, two separate samples
(farmer-cum-seller and consumers) are taken with the sample size of 324 & 322 respectively. For qualitative study, 20 agri-
officers are interviewed with some subjective questions. The broad outcomes of this study refers that 9 and 8 major factors of
satisfaction are extracted for the agri-products’ consumers and farmer-cum-sellers respectively. Factors of satisfaction put
significant positive impact on the ‘intention’ in both the cases. Finally, by Multi-Dimensional Scaling (MDS), it is concluded that
the consumers are satisfied, when really the small farmers of that region are satisfied.
Introduction
Mahatma Gandhi said that “there are people in the world so hungry, that God cannot appear to them except in the form of bread”.
This statement can be easily imagined by looking at the 94th rank of India in global hunger index in the year 2020, even below Sri-
lanka, Bangladesh, Nepal (India Fares Poorly in Hunger Index - The Hindu, n.d.). According to the World Bank report, three
quarter of Indian families depends on the rural income and these rural incomes is one way or another connected to agriculture and
vegetable production (“India: Issues and Priorities for Agriculture,” 2012). However, around 70% of population in India are rural
poor, who depend on the agriculture and horticulture for their livelihood. The wastage of food and vegetables can give more
insights into the necessity of doing research regarding marketing efficiency of agri-products. Within this explanation, a particular
data is worthy to mention here that 18% of India's fruit and vegetable production valued at Rs.13,300 crores is wasted annually
according to data compiled in a new report by Emerson Climate Technologies India, a business of the US-based manufacturing
and technology company Emerson (“India wastes Rs 44,000 cr of fruits, vegetables and grains annually | india | Hindustan
Times,” 2013). This is nothing but the inefficiency in the marketing and logistic problems that prevail in the agricultural and allied
sector. These are just few evidences that production of food materials are increasing and also encouraging with respect to other
similar countries. Still, the poorness and hungriness is really a matter of concern and giving a negative signal to the Indian
economy. In a way of solving the farmers problem, a particular research work refers that consumers' satisfaction influence
positively their orientation towards shopping the vegetables in regulated markets (Sahoo, Behera, & Sahoo, 2019). Further, it is
also found that the farmers’ satisfaction positively influence their intention to be in the farming profession. In this contextual
background, this research work aims at giving time-insulated solutions (sustainable in nature) with the integral effects to solve
multiple issues of farmers of a particular geographical area having surplus agri-products.
Research Problems
“Can the marketability of regionally surplus agricultural products be improved by integrating the issues of farmers, social
communities and existing agricultural administrative system?” Keeping in view this problem statement, the research objectives
and hypotheses are developed as follows.
Objectives of the study
On the basis of ‘research problem’ mentioned above, the following objectives are set for this research work. These objectives are
studied by various quantitative and qualitative techniques.
1. To explore the factors behind distraction (or satisfaction) of farmers from their main farming profession?
2. To analyse the impact of farmer-cum-sellers’ satisfaction on their intention to continue the farming profession and
continue to sale.
3. To analyse the impact of consumers’ satisfaction on their intention to purchase the agri-products from the farmers
directly.
4. To develop strategies to bring coordination among the local administration, agriculture officers, and farmers’ community
that give justice to the small farmers (farmer-cum-sellers).
5. To find out common collective solutions (integrated strategies) for multiple issues of surplus agriculture products of any
particular geographical areas, especially for India.
Sambodhi ISSN: 2249-6661
(UGC Care Journal) Vol-43, No.-4, (N) October-December (2020)
Copyright ⓒ 2020Authors 141
Hypotheses
To justify the ‘research problem’ and research objectives, the following hypotheses are developed and tested.
H1: Farmers’ satisfaction on farming and selling in the present market has significant positive impact on the farmers’ intention to
continue the farming profession and selling.
H2: consumers’ satisfaction-factors of purchasing the agri-product directly from farmers have significant positive influence
consumers’ intention to purchase directly from farmers.
Research Design & Methodology
The current study will follow mixed research design, where both the quantitative and qualitative analyses are adopted to provide
the integrated solutions as proposed in this research work. Under quantitative study, the causative research design is applied. Here,
the relationships of satisfying factors (after extracted) of farmer-cum-sellers, and satisfying factors of customer being the causes
separately, with the farmer-cum-sellers’ intention to continue the farming profession & to sell in the present market; and with the
consumers’ intention to purchase directly from farmers, are examined. Under qualitative study, relationships between the
opinions of agriculture officers, and local administrators are established by various logical arguments. The data is collected
through cluster sampling method. Here, three major geographical areas of Odisha are taken as three clusters of samples. The
sample size for the farmers’ data (sample-1) is 324; the sample size of the consumers’ data (sample-2) is 322. And for qualitative
data, 20 numbers of agriculture officers and local administrators are interviewed with an interview-schedule. So far as data
collection instruments are concerned two sets of structured questionnaires are prepared keeping in view the defined objectives and
hypotheses. Questionnaire-1 and questionnaire-2 followed five-point Likert scale. Questionnaire-1 is meant for farmers-cum-
sellers and questionnaire-2 is meant for the consumers of the agricultural product. These structured questionnaires (questionnaire-
1 and 2) are executed to the defined samples through face-to-face interaction.
Data analysis started by the scale reliability of two sets of data (farmers-cum-seller and consumers) are tested by the Cronbach’s
alpha (Cronbach, 1951). Two separate explorative factor analyses are applied to explore the factors of farmer-cum-sellers’
satisfaction, and to explore the factors of consumers’ satisfaction from two separate sets of data. These explored factors are taken
as independent variables for two separate multiple linear regression analyses with two dependent variables. These dependent
variables are farmer-cum-sellers’ intention to continue the farming profession & to sell in the near markets; and the consumers’
intention to purchase the agri-products directly from the farmers in the present market. These are done separately for two separate
sets of data. Finally, the multi-dimensional scaling (MDS) with Euclidean distance is executed to examine the proximate (by
mapping) of the factors of consumer satisfaction, factors of farmer-cum-sellers’ satisfaction, the farmers’ intention, and the
consumers’ intention. Through-out this research work the term ‘agri-product’ is used, which means both the agri-products and
vegetables. This study mainly focuses on the surplus agri-products of the small farmers (farmer-cum-sellers). The data analysis is
accomplished by SPSS 22.0 software. Citation and referencing is done through Mendeley desktop software with APA (American
Psychological Association) style.
Respondents’ Profile
Out of 324 respondents of farmers-cum-sellers, 92% are men and 8% are female. Coming to the marital status, 12% are single and
88% are married. So far as age is concerned, bellow 30 years respondents are 6.9%; 31-40 years respondents are 25.6% ;
respondents of age group 41-50 years are 45.3%, respondents of 51-60 years are 20%; and above 60% respondents are of 2.8%.
Whereas, out of 322 respondents of consumer 14.9% respondents belong to the age group of 30 years or below, 33.3%
respondents are between age of 31-40; 31.3% respondents are between age of 41-50, 18% respondents are between the age of 51-
60 and 2.5% respondents are age above 60 years. 86.6 % of consumer-respondents are male where as 13.4% of the respondents
are female. So far as marital status is concerned 82.6 % married consumers and 17.4% respondents are unmarried consumers.
And, only 14.3% consumers are below matriculation, 33.5% respondents are +2 level, 39.1% respondents are +3 level and 13%
respondents are P.G level and above concerning to their educational qualification concern. The farmers with 5KM distance from
agricultural field, are 36.4%, with 6-10KM distance 33.6 %, with 11-15KM. distance are 20.4%, and farmers with more than
15KM. distance 9.6 % of the total respondents. So far as consumers’ distance is concerned consumers with less 5 KM distance are
70%, with 6-10 KM distance 25%, with 11-15 KM distance 4%, and consumers with above 15 KM distance are nearly 1%.
Literature Review
Based upon most of the relevant literatures, the objectives of this study are defined and accordingly the hypotheses are set.
Literatures related to recent past research articles, survey reports, and some theories & concepts are reviewed and presented below
systematically.
Marketable Surplus and its Implications
Various marketing challenges of surplus agri-products are commonly known that the intermediaries take the surplus productions
at a low rate and sell at a high rate to the consumer, where farmers sufferers a lot. Out of the total production of all crops, 87.51%
was marketed to various agencies, 9.22% retained by the households for their purpose while rest of the amount mainly lost during
harvesting and post-harvesting periods (Kumar et al., 2013). Again, they state that high storage loss has been arising due to the
bad conditions of storage in the village. At this juncture, Sahu et al., (2017) referred that farmers of large category marketed their
produce less than the marketable surplus because of their better retention capacity. Small farmers reported higher marketed
Sambodhi ISSN: 2249-6661
(UGC Care Journal) Vol-43, No.-4, (N) October-December (2020)
Copyright ⓒ 2020Authors 142
surplus i.e. 97.53% followed by medium 92.11% and large size farmers 86.72%. In this situation, the current study wanted to cite
some important studies that are giving basic knowledge regarding the problems for which both the farmers and consumers are not
getting justice. Some these major problems are wide price fluctuation, commission paid to commission agents, lack of marketing
information (Prakash, 2014); high transportation cost, high commission/market charges (Kumar et al., 2017) relating to marketing
of agri-products. Adding to the above results, Kiruthiga et al., (2015) state that in developing countries product quality, market
information, product quantity, functionary’s participation, lack of transportation facility and inadequate storage facility are other
major problems in agricultural marketing. In the similar line of understanding, Sahu & Srivastava (2018) has explored that poor
implementation of the government projects; lack of infrastructure, lack of coordination etc. has been a major issue in the storage
and marketing of surplus. Then the issue of fund or cash should be discussed. Some farmers retain extra produce in the hope that
they would get a higher price in the later period, but Kumar (2011) state that output, farm and family size, market channels are
important determinants of marketed surplus. Pramanik & Prakash (2010) explained that due to insufficiency of the marketing
channel and distribution member, the farmers’ share is low on the consumers’ contribution towards the surplus item that is
produced by the farmers. Adding to the above discussion, Joshi (2011) also explained that if, there will be less number of
intermediaries in the marketing of the surplus product, and then the farmers will be more beneficial because the vegetable growers
sell their products immediately after harvest owing to the perishability of the product, lack of cold storage facility, the poor
economic condition of the farmers and other factors. In this regard, Sharma (2016) point out that access to markets and price
information has a significant impact on marketed surplus. Additionally, they also stated that easy access to institutional credit and
proper storage at the farm household level would reduce forced distress sale. So, price determination process or price
determination capabilities become crucial for the future predictability, especially for the farmers’ prediction capacity regarding the
future of agri-products. This arguments is supported by a conceptual study that the price determination process define the
perception towards future productivity, where the consumption pattern moderate the effects of former on the latter (Sahoo, Sahoo,
& Satpathy, 2017). So, the findings of Sharma (2016) seems to be very relevant here that appropriate financial scopes will enable
the farmers to determine the price more accurately and hence the predictability of production becomes more stronger. Adenuga et
al., (2013) found that household size, spoilage at farm level, education of the household head and farming experience were the
significant determinants of marketable surplus in vegetable production. in the context, a relevant solution is given by Cadilhon,
Fearne, Moustier, & Poole, (2003) especially for the South Asia situations that state policy and the local authority can better plan
the infrastructure of the vegetable market due to the cultural and regional diversities (Cadilhon, Fearne, Moustier, & Poole, 2003).
This fact is taken by the authors of the current study as an important note as this study focuses on small regional farmers (farmer-
cum-sellers). Now, it becomes the obvious question that whether the marketing inefficiency makes these small farmers exploited?
Probably the answer to this question is addressed to some extent by two studies that vegetable producers are more inefficient
concerning marketing than production (Singbo et al., 2014) and this inefficiency among the producers is noticed due to less return
with regard to their surplus production due to non-availability of proper buyers (Vishandass et al., 2018). The above question
demands an integrated approach for solution. So, the relation of demographic variables of consumer and farmers with their
happiness or intention should be understood. In this context, Sahoo & Sahoo (2018) found that some of the demographic variables
of consumers interact with their positivity towards purchasing the agri-products in controlled market and mostly no demographic
variables of farmers interact with their happiness. Thus, it necessary to discuss some of the important marketing dimensions of
agri-products’ marketing to formulate integrated strategies.
Effective promotion, logistic & distribution, and brand management
The agricultural products are often perished quickly due to climatic and time factors. But on the other hand, consumers prefer
fresh products. Many a time the place of production and the place of the presence of market and consumers are very fair and the
farmers face the problem of transportation. In this regard, Jones (1993) state that by establishing the long term storage for the
fruits and vegetable in the production site, and by providing the control to the farmer for selling the vegetable on their interest, the
efficiency of vegetable marketing will be achieved. Improper supply chain management, lack of cold chain infrastructure and food
processing units are leading to maximum inefficiencies and resulting to losses and wastage. And to create a better food chain, it is
required to provide freedom to farmers for their socio-economic activities and their own standard should be created (Moreira,
2017). The entire supply chain is laden with the issue of post-harvest losses and wastages due to long and fragmented chain,
dependency on intermediaries, poor road infrastructure, inefficient mandi system, inadequate cold chain infrastructure facilities,
high cost of packaging, poor quality of distribution, the weak link in the supply chain (Negi & Anand, 2015). In local food
systems, an integrated logistics network that embraced producers, customers (delivery points), collection centres and distribution
centres in the local food supply chain is very important, because the logistics services in such local systems are fragmented and
inefficient (Gebresenbet & Bosona,2012). Supporting to above statement, Srimanee & Routray (2012) stated that for the effective
delivery of the fresh fruits and vegetables, an effective distribution channel is needed. Concerning to the above discussion,
Munhuweyi et al. (2016) told that an effective transportation system, procurement system and retails outlet chains are necessary
for the effective selling of the agro product. So, this fact should be studied in Indian scenario as most of the time transportation
and procurement is objected by the small farmers. The above issues is addressed to some extent by Mann et al., (2011) that shorter
the marketing channel will reduce the marketing cost and end consumers can get the agri-products earlier. Again, for solving these
problems, result of one study should be cited here that cooperative channel also good option for the farmers where, the
cooperatives were not only purchase the vegetable, but also help farmers to sell the product and provide the guidance and
information (Zhang et al., 2017). By supporting this above result, Lu H. et al., (2008) state that, the ancient and traditional
marketing network of china (guanxi) help in improvement and performance of the modern marketing system and helps in the
better buyer-seller relationship, interpersonal trust and positive effect on small farmers marketing behaviour, which seems to be
Sambodhi ISSN: 2249-6661
(UGC Care Journal) Vol-43, No.-4, (N) October-December (2020)
Copyright ⓒ 2020Authors 143
very relevant in India scenario. Singh et al., (2018) majority of the products sold through wholesalers and a significant quantity
directly sold to the retailers because of higher price realization. In this regard (Oguoma et al., 2011) found that farmers encounter
high production costs in their efforts to boost production but hardly get fair pricing of their products from the middlemen as real
profit goes to the middlemen, who buy up the farm products at almost give away prices and sell at outrageous prices to the
consumers. It was observed by other authors that the marketing efficiency of vegetables and the result indicated a weakness in
marketing processes that farmers carried out on vegetable crops. Additionally, marketing processes were limited to packaging,
transportation omitting the important marketing steps such as cleaning, sorting, and grading according to colour, quality, and size
(Qays, Ali, & Shukur, 2018). The marketing cost incurred by commission agents is comparatively lower than those incurred by
wholesalers and retailers, it is due to non-performance of grading, packing and transportation functions by commission agents
(Barker et al., 2017; Martinez,2016). So, the marketing chain of the small farmers needs to transparent and efficient intermediary
reduction for reducing transaction costs. Price volatility in the marketplace leads to risk to the small farmers. So, there should be
open and transparent on intermediary activities such as back word activities like collection, cleaning, processing, grading, storing,
and transporting; and forward activities like contract acquisition, client service, merchandising, and sales for the positive outcome
of both the farmers, and intermediaries ( Zylberberg, 2013).
Sao far as promotions of agri-products are concerned, it can be said that like any other product, the vegetables also adequate
promotion and recognition by most of the studies. So, some of such important studies are cited here. For collective quality,
promotion can be a successful strategy for farmers (Marette, 2005). Social media is a very useful tool in agricultural marketing
which save time and cost of the farmers for getting information (Balkrishna & Deshmukh, 2017). Further, they state that 30-40
years age group farmers are using social media effectively. In the recent era, modern technology can be adhered in bringing the
marketing of the agricultural product in another height. In this regard, Sazzad (2014) found from his study that ICT (Information
and Communication Technology) can be used in the field of agriculture to promote the agricultural product and the effective
distribution channel. Kumar (2012) also supported the suggestions that promotions needed to enhance the awareness among the
consumers about the agricultural product produced in the specified area and ICT will also help to bring market force and the
payment against the product can be fair and transparent. Along with the promotion of the agricultural product, it is necessary to
brand those products with a special feature to attract customer with augmented benefits. In the context of USA also agricultural
marketing salespeople who understand their customers’ preference and behaviour became crucial for their success (Roucan-Kane
et al., 2010), which may hold well in Indian scenario also. Co-operatives and the other government organizations integration made
Chinese farmers successful in some cases (Huang & Liang, 2018), which can bring success in India also, if implemented
successfully as co-operative societies are accepted positively in India from ancient age. A study specifies to the rural farmers of
the Tanzania regarding the agricultural information need and found that for success in farming, the information regarding crop,
livestock husbandry, marketing information, funding option and value addition is very useful (Elly & Silayo, 2013). Further,
educational factors have the highest contribution in the challenges and problems of marketing of agricultural products followed by
other factors like marketing, managerial, economic and infrastructure factors (Arbabi, Mirdamadi, & Lashgarara, 2015).
Till now the agri-products are marketed as commodity, but it is argued that branding is the bare necessity for the surplus agri-
products. In this context it is found that branding of the agricultural product can be an effective tool for improving farm venture’s
profitability and sustainability (Salokhe, 2017). In recent times many producers brand their product by showing not only the core
benefits but also the augmented or the value-added benefits. These results of increased sale can bring the brand image into
context. Alike every other product agricultural products also need integrated branding to make the surplus marketed. In this
context, Vinola et al. (2015) found that alike milk, every other rural surplus can be branded to increase the visibility in the eyes of
the consumers. In this line of understanding, Dixit & Bajpai (2018) added that like any other MNC, the rural production should be
branded to induce and attract the consumers. For effective branding, it requires effective communication between the value chains.
And, require high-level relationship engagement, knowledge sharing and the establishment of formal mechanisms for
collaboration within the organization (Lewis et al., 2014). In this conjecture, it is suggested by Bhattarai et al. (2013) that
marketing efficiency of the small organic farmer can be achieved by liberalizing the certification of organic product, co-operative
formation by organic farmers and by developing the infrastructure of the market. Agriculture economy of china mainly depends
on the china agriculture integration, such as integration of co-operatives, funding agencies, central government and local
administration (Wim, Xueqin, & Lu, 2011), where it can be said that these factors are relevant in Indian scenario. The
development of the agriculture sector of India and China mostly depends on two aspects such as horizontal co-operation and
vertical integration (Huang, Vyas, & Liang, 2015). In the similar context, a study by Costa-Font et al. (2009) suggested that
reduce the number of intermediaries, improve the integration of agriculture channel and food processing units and, maintaining
more professionalism leads to the success of the marketing channel. Pay et al. (1996) found that the low observed level of
labelling and branding of agricultural and horticultural products appears to be justifiable where the important product attributes
have revealed intrinsic cues and where producers have a low degree of control over biological variability. The different platform
of the value chain development such as funding, planning need to integrated and should adopt the emerging challenges and
opportunities by co-operating one another (Devaux, Torero, Donovan, & Horton, 2018).
Analysis & Interpretation
According to research design and methodology of this study, data analysis is accomplished as follows and inferences are drawn
for the research objectives.
Sambodhi ISSN: 2249-6661
(UGC Care Journal) Vol-43, No.-4, (N) October-December (2020)
Copyright ⓒ 2020Authors 144
Scale reliability
By the scale reliability (of questionnaire-1 for sample-1) testing through Cronbach’s alpha (Cronbach, 1951) the following results
are obtained.
Table-1: Test of Reliability (farmers, sample-1) & (Consumers, sample-2)
Case Processing Summary
Scale Statistics
Reliability Statistics
N
%
Mean
Variance
Std. Deviation
No. of
Items
Cronbach's Alpha
No. of Items
Cases of
farmers
Valid
324
100.0
130.80
118.351
10.879
35
0.728
35
Excludeda
0
.0
Total
324
100.0
Cases of
consumers
Valid
322
100.0
129.12
186.615
13.661
37
0.808
37
Excludeda
0
0
Total
322
100.0
a. Listwise deletion based on all variables in the procedure.
The scale is reliable by 73% for the questionnaire-1 (for farmers) as the Cronbach’s alpha is 0.728 with 35 items excluding
demographic variables. The scale reliability is nearly 81% for questionnaire-2 (for consumers) as the Cronbach’s alpha is 0.808
with 37 items excluding demographic variables as referred from the table –1. The value of Cronbach’s alpha between 0.7 to 0.8 is
‘acceptable’ and value between 0.8 to 0.9 is ‘good’ (George & Mallery, 2006).
Major factors of satisfaction of farmer-cum-sellers by explorative factor analysis
To explore major factors that are behind the satisfaction of farmers-cum-sellers relating to agri-product marketing, the explorative
factor analysis is adopted by taking 30 variables (items of the questionnaire-1) of the sample-1.
Table-2 KMO and Bartlett's Test (samle-1)
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.757
Bartlett's Test of Sphericity
Approx. Chi-Square
2221.460
df
435
Sig.
.000
KMO measure of sample adequacy is 0.757, which is significant (0.000). So, explorative factor analysis is applicable here as
referred in the table-2. The significant (0.000) chi-square statistics (2221.460) of Bartlett’s test of sphericity justify that the
correlation matrix is not an identity matrix. This shows that the sample is very appropriate for factor analysis.
By principal component method and varimax rotation with 10 iterations, 8 factors are extracted from 30 variables (30 items of the
questionnaire) relating to farmer-cum-sellers’ satisfaction. The numbers of variable more than 1 initial eigenvalues are considered
as factors. These 8 factors explain near about 55% variance as cumulative percentage of rotated square loading is 55.159. These 8
factors are named as ease of marketing, matching to customers’ preference by farmers, farmers’ expediency, market predictability,
market risk, feel good, adequate selling alternative, and consumer relationship by farmers
Impact of farmers’ satisfying factors on the farmers’ intention to continue the farming profession
By pursuing the following hypothesis with multiple linear regression, the effect of eight factors (extracted by EFA) of the
satisfaction of farmers on the ‘intention of farmers to continue farming is checked’.
H1: Farmers’ satisfaction on farming and selling in the present market has significant positive impact on the farmers’ intention to
continue the farming profession and selling.
There are five dependent variables originally considered (5 item in the questionnaire), but for multiple-linear regression, there 5
variables transformed to one variable, named as “farmers’ intention to continue the farming the farming profession”. The above
said five variables are ‘agri-products are main source of income’, struggling with surplus agri-product’, ‘optimistic regarding
future profit margin from agri-product’, ‘happiness with present profession’, and ‘continuance of production and selling of present
agri-products in the present market’. By taking the mean of these variables (after transformation, a single dependent variable is
created), multiple linear regression is put with 8 factors (as independent variables) of “farmers’ satisfaction on dependent variable,
intention of producing and selling the agri-products in the present market” in order to test the above said hypothesis.
Table-3: Model Summaryb & model fitting (for multiple linear regression of Sample-1)
Model Summaryb
ANOVA
Model
R
R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
Durbin-
Watson
F
Sig.
F Change
df1
df2
Sig. F
Change
1
.514a
.264
.246
.38932
14.156
8
315
.000
1.516
14.156
.000
a. Predictors: (Constant), Customer relationship by farmers, Adequate selling alternatives for farmers, Feel good factor of farmers, Market risk realized by
farmers, Market predictability for farmers, Farmers' expediency, Matching to customers' expectation, Ease of marketing for farmers
b. Dependent Variable: Farmers intention to continue farming profession & selling directly
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The farmers’ satisfaction-factors put significant positive impact on the dependent variable, “farmers’ intention to continue the
farming profession and sale directly to the consumers” by 25% as the adjusted R2 value (0.246) is significant (P=0.000). Again,
the adjusted R2 is close to R-square that justifies the generalizability of the result. The variables are truly independent because the
variables have no auto co-relation symptoms avelebeble. It is justifying from the Durbin Watson statics (it should very from 1.5 -
2.5). In current research it is 1.516, which is justify by the above argument as referred from the table-3.
Observing the impact of individual factors on the above said dependent variable, it is found that “market risk realized by farmers”,
“ease of marketing for farmers”, “market predictability for farmers”, “matching to customers' expectation” are showing heist to
lowest impacts (34%, 28%, 18%, and 14% respectively). Surprisingly the factor, “adequate selling alternatives for farmers” is
influencing negatively the “farmers' intention to continue farming profession and selling” having significant (p=0.042) beta value
as -0.099. Negligible heteroscedasticity are seen. So, there is no doubt on the significance of the beta values.
Major factors consumers’ satisfaction towards agri-product shopping
To explore major factors of consumers’ satisfaction towards agri-product shopping, the explorative factor analysis is adopted by
taking 33 variables (items of the questionnaire-2) of the sample-2.
Table-4: KMO and Bartlett's Test (EFA, samjple-2, consumers)
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.800
Bartlett's Test of Sphericity
Approx. Chi-Square
2441.701
df
528
Sig.
.000
KMO measure of sample adequacy is 0.800, which is significant (0.000). So, explorative factor analysis can be satisfactorily
proceed with the sample-2 as this sample is 80% adequate to perform explorative factor analysis, referred from the table-4. The
significant (0.000) statistics (0.2441.701) of Bartlett’s test of sphericity justify that the correlation matrix is not an identity matrix.
This proves that the sample-2 is suitable for explorative factor analysis. By principal component method and varimax rotation
with 10 iterations, 9 factors are extracted having more than 1 initial eigenvalues as factors. These 9 factors explain near about 55%
of the total variance as cumulative percentage of rotated square loading is 55.159.
By using principal component analysis and by varimax rotation with Kaiser normalization, 9 factors are extracted as described in
the rotated component matrix, which are named as customers’ intellectuality, purchase pragmatism, purchase affluence, rational
marketing for consumers, purchase convenience, market consistency for consumers, consumers’ loyalty, matching customers’
expectations, and customer-relationship.
Impact of consumers’ satisfying factors on consumers’ purchase intention directly from farmers
The impact of nine factors (extracted by EFA) of consumers’ satisfaction on the consumers’ intention to buy the agri-products
directly from farmers, is tested by the following hypothesis with multiple linear regression.
H2: consumers’ satisfying factors by purchasing the Agri-product have significant positive influence consumers’ intention to
purchase directly from farmers
Originally there are four dependent variables (4 items in the questionnaire) like ‘purchase from farmers directly rather than from
the pure merchant’, ‘compare the quality of agri-product from different seller before purchase’, ‘continue to purchase the agri-
products from the present market’, and ‘perception of consumer towards happiness of farmer-cum-sellers’. But, here the mean of
these variables (by transformation) are taken as one dependent variable called as “purchase intention of consumers to purchase
directly from farmers’.
Table-5: Model Summaryb and model fitting (for multiple linear regression of Sample-2)
Model Summaryb
ANOVA
Model
R
R Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
Durbin-
Watson
F
Sig.
F Change
df1
df2
Sig. F
Change
1
.503a
.253
.232
.40382
11.751
9
312
.000
1.794
11.751
.000
a. Predictors: (Constant), Customer-Relationship, Matching customers’ expectations, Consumers’ Loyalty for consumers, Market consistency for consumers,
Purchase Convenience , Rational marketing for consumers, Purchase affluence of consumers, Purchase pragmatism of consumers, Customers’ intellectuality
b. Dependent Variable: Consumers’ intention to purchase the agri-products from farmers
The satisfaction-factors of agri-products’ consumers have significant positive (nearly 23%) impact on consumers’ purchase
intention directly from farmers, which is proven from adjusted R2 (0.232) is significant (P=0.000). Further, the adjusted R2 is close
to R-square (0.253) that justifies the generalizability of the result. The variables are truly independent because the variables have
no auto co-relation symptoms visible in this analysis having value of Durbin Watson statistics as 1.794 (table-5), which should
very from 1.5 - 2.5.
Out of nine satisfying factors of consumers, five factors have significant positive impact on the consumers’ intention to purchase
the agri-products directly from farmers. From highest impact to lowest impact, these factors are “rational marketing for
consumers”, “matching customers’ expectations”, “customers’ intellectuality”, “purchase pragmatism of consumers”, and
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“customer-relationship” carry the significant (p=0.000, 0.000, 0.000, 0.000, 0.001) beta values as 0.262, 0.230, 0.215, 0.208 and
0.163 respectively. Rest of the four individual factors is not showing any significant impacts on the above said dependent variable.
Mapping of consumers’ satisfaction with farmer-cum-sellers’ satisfaction
Nine factors that signifies the consumers’’ satisfaction and eight factors that signifies the farmer-cum-sellers’ satisfaction are
plotted in theme based map by a statistical procedure called as multi dimensional scaling (MDS). This is done to understand how
close the satisfying factors of consumers are with each other and with the satisfying factors of farmer-cum-sellers also. This will
enable the strategists to formulate the integrated strategies.
Table-6: Case Processing Summarya
Valid cases
Missing cases
Total cases
N
Percent
N
Percent
N
Percent
322
100.0%
0
0.0%
322
100.0%
a. Euclidean Distance used
The table-6 reflects that there is no missing cases and the entire sample size (322) of both the samples are taken for calculation of
Euclidean distance, the key statistics to perform multi dimensional scaling (MDS).
Table-7: Model comparison for dimensional appropriateness
Iteration history for 2 dimensional solution (in squared
distances)
Iteration history for 3 dimensional solution (in squared distances)
Iteration
S - stress
Improvement
Iteration
S – stress
Improvement
1
0.33585
1
0.26282
2
0.26524
0.07061
2
0.19949
0.06333
3
0.25917
0.00607
3
0.19445
0.00504
4
0.25858
0.00059
4
0.19373
0.00072
Iteration stopped because S – stress improvement is less than 0.001000
Young’s S – stress formula 1 is used
By the model comparison (table-7), it can be inferred that the 3- dimensional solution is more preferred than the 2-dimensional
solution. Because both the models ends with 4th iteration and after 4th iteration the 3-dimensional solution is showing less S-stress
value (0.19373) in comparison to S-stress value (0.25858) of 2-dimensional solution.
Table-8: Stress and squired correlation (RSQ) in distances
For Matrix
2-Dimensional solution
Stress
0.26122
RSQ
0.79679
3-Dimensional
solution
Stress
0.18486
RSQ
0.85212
The values of RSQ also strengthen the preference that 3-dimensional solution is more preferred than the 2-dimensional solutions.
Because the stress (0.18486) of 3-dimensional solution is less than the stress (0.26122) of 2-dimensional solution, where lesser is
the stress more is the model fitting. Further, the values of RSQ (R2) is more (0.85212) for the 3-dimensional solution than the 2-
dimensional solution (0.79679) as reflected from the table-8, where more is the value more is the variance of original dissimilarity
matrix, accounted for. Thus, 3-dimensional solution is more preferred to proceed with multi dimensional scaling (MDS) along
these data sets.
Figure. 1
The Euclidean distance increase almost steadily along the disparities as reflected from the figure-1, which referred that
dissimilarity is appropriately captured by the Euclidean distance-statistics. Again, it clear that the MDS procedure is appropriate to
proceed with.
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In the derived stimulus configuration with Euclidean distance (figure-2), nine satisfying factors of consumers (CFs) and eight
satisfying factors (FFs) of farmers-cum-sellers are plotted in the 3-dimensional MDS procedure. It is observed that 7 satisfying
factors of farmers-cum-sellers out of 8 are appearing one side and one (FF8) is appearing on another side, along the satisfying
factors of consumers. On this same side (former) 4 satisfying factors (CF1, CF3, CF6, CF7) of consumers are appearing along the
above said 7 factors. This means satisfactions of farmer-cum-sellers are going side-by-side with the consumers’ satisfying factors.
Inferring the chumminess of the factors plotted in figure-2, it is seen that 4 satisfying factors (CF2, CF4, CF8, CF9) of consumers
are closer to one (FF8) satisfying factors of farmer-cum-sellers, which means matching customers’ expectation (CF8) and
matching customers’ expectation by farmers (FF8) are close to each other and also close to purchase pragmatism (CF2), rational
marketing (CF4), and customer-relationship (CF9). It can be inferred that illogical and irrational activities of any one in the agri-
product market can never build healthy customer-relationship and can never match the customers’ expectations.
In consistence with the above inference, it can be interpreted that farmers’ intention to continue the farming profession and
consumers’ intention to purchase directly from the farmers are going side-by-side the rationality, logicality, and customer-
relationship. This is a very important outcome, especially in Indian context that apart from value for money, both the consumers
and farmers emphasizes the relationship and rationality to continue the transactions. It is witnessed from the figure-3 that FF1,
FF4, and FF5 are close to each other, which means ease of marketing (FF1), market predictability (FF4), and market risk are close
to each other; and not far away from “farmers’ intention to continue the farming profession”, which give inference that
conformability (both psychologically and physically) can strengthen the farmers’ intention to continue the farming profession.
Additionally, it is observed that FF2, FF3, & FF7 are close to each other; and close to CF6. This means, matching customers’
expectation by farmers (FF2), farmers’ expediency (FF3), and adequate selling alternatives (FF7) are close to each other and also
close to market consistency (CF6), which gives inference that a sense of stability can make the consumers and farmers-cum-
sellers close to each other, which may act positively to attract the consumers to purchase the agri-products directly from the
farmers that ultimately can encourage the farmers to continue the farming. Lastly, it is observed that CF1, CF3, and FF6 are close
to each other. This means customer-intellectuality (CF1), purchase affluence (CF3), and farmers’ feel good factor (FF6) are close
to each other, making a sense that consumers’ prosperity can go along the farmers’ happiness only.
Summary Findings
Farmer-cum-sellers’ satisfaction can be explained by the major factors like ease of marketing, matching to customers’ preference
by farmers, farmers’ expediency, market predictability, market risk, feel good, adequate selling alternative, and consumer
relationship by farmers. Satisfaction of agri-products’ consumers can be explained by customers’ intellectuality, purchase
pragmatism, purchase affluence, rational marketing for consumers, purchase convenience, market consistency for consumers,
consumers’ loyalty, matching customers’ expectations, and customer-relationship.
One of the data analyses refers that eight major factors of farmers’ satisfaction put significant positive impact (25%) on farmers’
intention to continue the farming profession and sale by him/her. Another analysis shows that nine major factors of consumers’
satisfaction have significant positive (nearly 23%) impact on consumers’ intention to purchase the agri-products directly from
farmers. So, it can be concluded that satisfaction in both cases has potential to propel both types of ‘intentions’ even more, if can
be strategized on the basis of the extracted factors. Now it is a matter to understand the individual factors that can be critically
examined for strategy formulation. The following findings will say more regarding these aspects.
Looking at the individual impacts of the factors of farmers’ satisfaction, it is concluded that “market risk realized by farmers”,
“ease of marketing for farmers”, “market predictability for farmers” and “matching to customers' expectation by farmers” put
impact significantly and positively on “farmers' intention to continue farming profession and selling”. But, astonishingly the
factor, “adequate selling alternatives for farmers” is influencing negatively farmers’ above-said intention, which refers that the
small farmers do not require much alternatives, rather they want some surety in their profession as risk realization, ease of
marketing, and market predictability are the major positive influencers.
So far as the individual impacts of factors of consumers’ satisfaction is concerned, it is found that “rational marketing for
consumers”, “matching customers’ expectations”, “customers’ intellectuality”, “purchase pragmatism of consumers”, and
Figure- 2: Derived Stimulus configuration with Euclidean
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“customer-relationship” put significant positive impact from highest to lowest, on the consumers’ intention to purchase the agri-
products directly from the farmers. No negatively influenced factors and centrally held theme of the positively influencing factors
is rationalization, which means consumers must know the truth behind any adversity to their happy shopping of agri-products.
In one hand customers’ intellectuality, purchase pragmatism, expectation of rational market are seen to be predominant; in another
hand, the predominant factors for farmer-cum-sellers are ease of marketing, risk realization, market predictability. Logically, the
meaning can be derived that farmers’ level of understanding, knowledge, and thought process to market the agri-products are
lagging behind the customers in today’s society. So, the public planners should make studies separately for separate regions with
separate criteria to know the respective causes in isolation.
The factors of satisfaction which are not putting any significant impact on the intention of farmers to remain in their same
profession but successfully extracted, are farmers' expediency, feel-good factor of farmers, and customer-relationship by farmers.
In the same manner, the satisfaction-factors of consumers, which have no significant impact on the intention of consumers to
purchase the products directly from the farmer-cum-sellers, but explored successfully, are purchase affluence of consumers,
purchase convenience, market consistency for consumers, and consumers’ loyalty. That means, although these factors are not
presently influencing the ‘intention’, these factors have the potential for any other positive angles of farmers and consumers. So, it
can be studied by the future researchers. Both for consumers & farmers, one factor called matching customers’ satisfaction, is the
significant positive influencer of the ‘intention’. It is obvious that consumers always desire that their expectations need t o be
fulfilled, but interestingly farmer-cum-sellers also want the consumers’ expectations to be filled. This particular finding implies
that whenever there will be scope for the farmer-cum-sellers to serve the consumers’ expectations, they will do it happily.
Consumers’ satisfaction-factors are appearing side-by-side of satisfaction-factors of farmer-cum-sellers, which means the general
understanding that consumers’ happiness automatically builds the sellers’ prosperity, proven to be true here. But the reverse is not
true in this research that farmer-cum-sellers’ satisfaction can make the consumers satisfied. This result is derived from the MDS
(Multi-Dimensional Scaling) technique, where more number of consumers’ satisfying factors are in a close proximity of all most
all satisfying-factors of farmers-cum-sellers’, but only one factor of farmers’ satisfaction is in close proximity of consumers’
satisfying factors. Factor like ‘matching the customers’ expectations’ can be said to be the builder of healthy customer-
relationship can be developed by eradicating illogical and irrational activities in the agri-products’ market. Thus, government
agencies should facilitate the above said relationship through various programs, match to the respective regional culture.
This research work given an important outcomes that even for the agri-products; the consumers are emphasizing the ‘relationship’,
but the farmers-cum-sellers are emphasizing the ‘rationality’ to continue the transactions, which seems to be very much crucial in
the Indian context. It is obvious that the transactions of agri-products and vegetables do require rationality because of its
perishable nature. Side by side ‘relationship’ also finds an important place in mind of the consumer while purchasing the agr i-
products directly from farmers. This fact of relationship is also acknowledged by the farmers as one of the important extracted
factors of farmers’ satisfaction refers to ‘customer-relationship’. However, the relationship takes less priority than rationality (also
seen from outcome-5). That means, the farmers need to be facilitated for maintaining the relationships, so that they will be
enabled to understand the expectations of customers and try to fulfil. Both psychologically and physically, comfort-ness of
farmers can strengthen their intention to continue the farming profession and selling by themselves. So, the strategists and public
planners should identify the exact causes related to above said factors to make the farmers comfort, which can be done if the some
authorities of the planners’ team will spend some time with those regional farmers exactly along their life style.
Realization of stability can make the consumers and farmers-cum-sellers close to each other, which may play an important role in
making the consumers, purchase the agri-products directly from the farmers that ultimately can encourage the farmers to continue
the farming.
In one way ‘stability’ and in another way, ‘comfort-ness’, can enable the farmers-cum-sellers to stick to their farming profession
as the ‘comfort-ability’ will be strengthened by the ‘stable future of the life’. Also, stable future of life will encourage next-
generation farmers’ family. It is found in the above point that consumer also likes to see a stable market (in terms of price ,
availability of certain agri-product throughout, presence of certain farmers throughout, etc.), which means if the farmer-cum-
sellers will know what exactly consumers want to be available or to be offered with or to be given with continuously, then farmers
will be encouraged to serve these aspects to the consumers in a steady manner. This outcome ultimately answers one of the
important questions this research that how the farmers can carry a positive intention to continue the farming profession? Factual
enjoyment of consumers and their prosperity for a comparative long time period can take the farmers’ happiness to the expected
level, so far as agri-products marketing are concerned. This research-result, in the Indian context, is very much realistic in nature
as agri-products are the base of real enjoyment of life, not the so-called enjoyment of modern society.
As per the methodology of this research work, the quantitative results should be substantiated by the qualitative results or data. In
this regard some of the common opinions (qualitative data) of the agriculture officers and local administrations are considered
here. These common opinions/suggestions are problems related to middlemen, lack of storage facilities, lack of knowledge about
the price & market, credit facilities to the farmers, agricultural inputs subsidy, adequate marketing infrastructure & markets yards,
implementation of appropriate support price or procurement price, awareness and training for farmers regarding harvesting & post
harvesting management, it-based training platform, special village market or bazzar , and establishment of fair-price shop by
which farmers can directly sale to the end-users. The regulated market committees should work in the above directions.
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Limitations and future research scopes
Apart from the farmer-cum-sellers and agri-products’ consumers, some other respondents like PDS (Public Distribution Officer)
officer, rice mill owners and owners of other food processing units, agri-products’ retailers, distributors, and agents could not be
considered for the current research work because of pandemic situation. If these types of respondents with more clusters can be
interviewed by the future researchers, then outcomes of the research can be broaden. For this same reason, some other clusters of
sample, where agri-products are surplus could not be could not be considered for data collection, although the sample size has not
been hampered so much. The pandemic situation and the depressed psychological conditions of villages (small farmers) made the
data collection too much difficult. Thus, a comparative larger sample size could not be obtained, which would have given more
statistical significance. Future researchers can address this issue in future normal situation, by which the academic contributions of
this research, if replicated, can be strengthened.
Originality/Contribution
Rarely farmers’ perspectives are integrated with the consumers’ perspectives and with agri-officers’ experiences to give justice to
the small farmers (farmer-cum-sellers), which the current research has addressed successfully by proposing the common strategies
for diversified issues. Further, the roles of demographic variables and market distance in formulating the above said strategies, is
another contribution or originality of this study. One crucial contribution of this study to the academic world can be the
application of MDS (Multi-Dimensional Scaling) procedure, where the “satisfaction-factors” of small farmers and consumers of
agri-products are mapped with the “intention of farmers to continue the farming profession” and “consumers’ intention to
purchase the agri-products directly from farmers” to strengthen the empirical results by the qualitative analysis.
References
1. Adenuga, A. H., Fakayode, S. B., & Adewole, R. A. (2013). Marketing Efficiency and Determinants of Marketable Surplus in Vegetable Production in
Kwara State, Nigeria. In 4th International Conference of the African Association of Agricultural Economists (pp. 1–15).
2. Arbabi, F., Mirdamadi, M., & Lashgarara, F. (2015). Marketing Challenges of Agricultural Products from the Perspective of Rural Cooperatives in
Qom Province. International Journal of Review in Life Sciences, 5(2), 59–62.
3. Balkrishna, Bite, B., & Deshmukh, Anand, A. (2017). A Study on Role of Social Media in Agriculture Marketing and its Scope. Global Journal of
Management and Business Research:E Marketing, 17(1), 283–287. https://doi.org/10.18782/2320-7051.7722
4. Barker, N., Kumar, D., & Singh, N. (2017). An Economic Analysis of Brinjal in Allahabad District of Uttar-pradesh State. International Journal of
Recent Scientific Research, 8(3), 15925–15929. https://doi.org/10.24327/IJRSR
5. Bhattarai, S., Lyne, M. C., & Martin, S. K. (2013). Assessing the performance of a supply chain for organic vegetables from a smallholder perspective.
Journal of Agribusiness in Developing and Emerging Economies, 3(2), 101–118. https://doi.org/10.1108/JADEE-12-2012-0031
6. Cadilhon, J. J., Fearne, A. P., Moustier, P., & Poole, N. D. (2003). Modelling vegetable marketing systems in South East Asia: Phenomenological
insights from Vietnam. Supply Chain Management : An International Jurnal, 8(5), 427–441. https://doi.org/10.1108/13598540310500268
7. Costa-Font, M., Serra, T., Gil, M., & Gras, A. (2009). Explaining low farm-gate prices in the Catalan wine sector. International Journal of Wine
Business Research, 21(2), 169–184. https://doi.org/10.1108/17511060910968006
8. Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 197–334.
9. Devaux, A., Torero, M., Donovan, J., & Horton, D. (2018). Agricultural innovation and inclusive value-chain development: a review. Journal of
Agribusiness in Developing and Emerging Economies, 8(1), 99–123. https://doi.org/10.1108/JADEE-06-2017-0065
10. Dixit, P. K., & Bajpai, S. (2018). New Destination For Multinational Corporations in India - Rural market. Swaranjali Publication, 233–243
11. Elly, T., & Silayo, E. E. (2013). Agricultural information needs and sources of the rural farmers in Tanzania A case of Iringa rural district. Library
Review, 62(8/2), 547–566.
12. Gebresenbet, G., & Bosona, T. (2012). Logistics and Supply Chains in Agriculture and Food. In Pathways to Supply Chain Excellence (pp. 126–146).
https://doi.org/10.5772/25907
13. George, D., & Mallery, P. (2006). SPSS for Windows step by step: A simple guide and reference. 13.0 update (6th ed.). Pearson Education, Inc.
14. Huang, Z., & Liang, Q. (2018). Agricultural organizations and the role of farmer cooperatives in China since 1978: Past and future. China Agricultural
Economic Review, 10(1), 48–64. https://doi.org/10.1108/CAER-10-2017-0189
15. Huang, Z., Vyas, V., & Liang, Q. (2015). Farmer organizations in China and India. China Agricultral Economic Review, 7(3), 601–615.
16. India wastes Rs 44,000 cr of fruits, vegetables and grains annually | India | Hindustan Times. (n.d). Retrieved December 5, 2017, from
http://www.hindustantimes.com/india/india-wastes-rs-44-000-cr-of-fruits-vegetables-and-grains-annually/story-eISAhdFucY9INjVFjkOx7O.html
17. India: Issues and Priorities for Agriculture. (2012). Retrieved from http://www.worldbank.org/en/news/feature/2012/05/17/india-agriculture-issues-
priorities
18. Jones, S. (1993). The Future for Fruit and Vegetable Distribution in Russia. British Food Journal, 95(7), 21–23.
https://doi.org/10.1108/EUM0000000002373
19. Joshi, G. (2011). An Analysis of Marketed Surplus and Price Spread of Brinjal in Western Uttar Pradesh. ASIAN JOURNAL OF MANAGEMENT
RESEARCH, 2(1), 484–490. https://doi.org/https://doi.org/10.1108/13598540710724365
20. Kiruthiga, M. K., Karthi, R., & Daisy, M. B. A. (2015). Agricultural Marketing – An Overview. International Journal of Scientific and Research
Publications, 5(4), 4–5.
21. Kline, C. S., Joyner, L. E., Kirchoff, J. F., Crawford, A., Jilcott Pitts, S., Wall-Bassett, E., … Dunning, R. (2016). Gaps and barriers along the North
Carolina agri-food value chain. British Food Journal, 118(2), 301–317. https://doi.org/10.1108/BFJ-06-2015-0223
22. Kumar, M. N., B.B, G., Mohan, D. A., H.R, S., Rajesh, K., & G.N, G. (2017). A study of marketing of green chilli in kolhapur district of maharashtra.
International Journal of Agriculture Sciences, 9(9), 43958–43961.
23. Kumar, M. P. (2012). a Study on Problems of Marketing Vegetables. IJRDMS, 6(1), 241–251.
24. Kumar, P. (2011). Structure of Markets and Prices in Agriculture: Empirical Findings from Punjab and Bihar. Margin-The Journal of Applied
Economic Research, 5(2), 183–220. https://doi.org/10.1177/097380101100500202
25. Kumar, P., Kannan, E., Chaudhary, R., & Vishnu, K. (2013). Assessment of Marketed and Marketable Surplus of Foodgrain Crops in Karnataka.
https://doi.org/10.13140/RG.2.1.3005.7840
26. Lewis, G., Crispin, S., Bonney, L., Woods, M., Fei, J., Ayala, S., & Miles, M. (2014). Branding as innovation within agribusiness value chains. Journal
of Research in Marketing and Entrepreneurship, 16(2), 146–162. https://doi.org/10.1108/JRME-03-2014-0005
27. Lu, H., Trienekens, J. H., Omta, S. W. F. (Onno), & Feng, S. (2008). The value of guanxi for small vegetable farmers in China. British Food Journal,
110(4/5), 1–5.
28. Mann, S., Breukers, A., Schweiger, J., & Mack, G. (2011). Greenhouse vegetable production in The Netherlands and Switzerland: A grounded look at
sector competitiveness. Competitiveness Review, 21(4), 339–351. https://doi.org/10.1108/10595421111152147
Sambodhi ISSN: 2249-6661
(UGC Care Journal) Vol-43, No.-4, (N) October-December (2020)
Copyright ⓒ 2020Authors 150
29. Marette, S. (2005). The Collective-Quality Promotion in the Agribusiness Sector: An Overview. Retrieved from
http://lib.dr.iastate.edu/card_workingpapers%0Ahttp://lib.dr.iastate.edu/card_workingpapers/431
30. Martinez, S. W. (2016). Hedonic analysis of US fresh produce prices at direct-to-consumer sales outlets versus competing retailers. British Food
Journal, 118(7), 1665–1681. https://doi.org/10.1108/BFJ-05-2015-0183
31. Moreira, M. B. (2017). Farmer’s Freedom in the Productive World Order: Standard Takers, Contesters and Negotiators, or Dissenters? Transforming
the Rural, 24(July), 155–176.
32. Munhuweyi, K., Opara, U. L., & Sigge, G. (2016). Postharvest losses of cabbages from retail to consumer and the socio-economic and environmental
impacts. British Food Journal, 118(2), 286–300. https://doi.org/10.1108/BFJ-08-2014-0280
33. Negi, S., & Anand, N. (2015). Issues and Challenges in the Supply Chain of Fruits & Vegetables Sector in India: A Review. International Journal
of Managing Value and Supply Chains, 6(2), 47–62. https://doi.org/10.5121/ijmvsc.2015.6205
34. Oguoma, O. N., Nkwocha, V. I., & Ibeawuchi, I. I. (2010). Implications of middlemen in the supply chain of agricultural products. Journal of
Agriculture and Social Research (JASR), 10(2), 77–83. https://doi.org/10.4314/jasr.v10i2.67575
35. Pay, C., White, M. R., & Zwart, A. C. (1996). The Role and Importance of Branding in Agricultural Marketing. In New Zealand Marketing Educators ’
conference (p. 13). Retrieved from https://researcharchive.lincoln.ac.nz/handle/10182/976
36. Prakash, K. C. (2014). An analysis of supply chain of tomato from farm to retail outlets for spencers retail outlets in Bangalore city. International
Journal of Commerce and Business Management, 7(2), 243–250. https://doi.org/10.15740/has/ijcbm/7.2/243-250
37. Pramanik, R., & Prakash, G. (2010). Marketable Surplus and Marketing Efficiency of Vegetables in Indore District : A Micro-Level Study. The IUP
Journal of Agricultural Economics, VII(3), 84–94
38. Qays, T., Ali, N., & Shukur, A. S. (2018). Economic Study to Measure the Efficiency and Marketing Margins of the Main Vegetable Crops Baghdad
Province for the Agricultural Season 2016. IOSR Journal of Agriculture and Veterinary Science, 11(7), 11–15. https://doi.org/10.9790/2380-
1107011115
39. Roucan-Kane, M., Alexander, C., Boehlje, M. D., Downey, S. W., & Gray, A. W. (2010). Agricultural financial market segments. Agricultural Finance
Review, 70(2), 231–244. https://doi.org/10.1108/00021461011065265
40. Sahoo, S. K., Behera, Y. D. P., & Sahoo, T. R. (2019). A study on the efficiency of vegetable marketing : an integrated approach with consumers ’ and
farmer-cum- sellers ’ perspective. J-GIBS, 11(1), 77–88.
41. Sahoo, T. R., & Sahoo, S. K. (2018). EFFICIENCY OF VEGETABLE MARKETING : A STUDY WITH ROLE OF RMC. (1st, Ed.). Delhi: Agritech
Publishing.
42. Sahoo, T. R., Sahoo, S. K., & Satpathy, B. (2017). Subjective Consideration of Agricultural Future Productivity: The Study by a Proposed Model.
Asian Journal of Economics, Business and Accounting, 3(3), 1–9.
43. Sahu, M., Srivastava, S., Jaulkar, A., & Raghuvanshi, J. (2017). Marketable and Marketed Surpluses of Mustard in Morar Block of Gwalior District of
Madhya Pradesh. International Journal of Agricultural Science and Research (IJASR), 7(2), 55–58.
44. Sahu, R. K., & Shrivastava, R. (2018). Agriculture rural market opportunity and chhallenges in india. IJARIIT, 393–398.
45. Salokhe, S. (2017). Branding of Agricultural Commodities / Products for Adding Value. In International Conference on Recent Innovations in
Engineering, Applied sciences and Management (pp. 48–58).
46. Sazzad, P. (2014). Food supply chain management in Indian Agriculture: Issues, opportunities and further research. African Journal of Business
Management, 8(14), 572–581. https://doi.org/10.5897/AJBM2013.7292
47. Sharma, V. P. (2016). Marketable and Marketed Surplus of Rice and Wheat in India: Distribution and Determinants. Indian Journal of Agricultural
Economics, 71(2), 137–159.
48. Sharma, V. P. (2016). Marketable and Marketed Surplus of Rice and Wheat in India: Distribution and Determinants. Indian Journal of Agricultural
Economics, 71(2), 137–159.
49. Singbo, A. G., Oude Lansink, A., & Emvalomatis, G. (2014). Estimating farmers’ productive and marketing inefficiency: an application to vegetable
producers in Benin. Journal of Productivity Analysis, 42(2), 157–169. https://doi.org/10.1007/s11123-014-0391-1
50. Singh, D. K., Pandey, N. K., & Kharumnuid, P. (2018). An Economic Analysis of Marketing of Potato from Shimla Hills of Himachal Pradesh.
Economic Affairs, 63(2), 419–424. https://doi.org/10.30954/0424-2513.2.2018.18
51. Srimanee, Y., & Routray, J. K. (2012). The fruit and vegetable marketing chains in Thailand: policy impacts and implications. International Journal of
Retail & Distribution Management, 40(9), 656–675. https://doi.org/10.1108/09590551211255956
52. Vinola, R., Swaminathan, R., & Jeevithan, E. (2015). A Detailed Study on Impact of Milk Marketing : Modern Constrictions and Prospects of Milk
Productions Status in India. Impact of Milk Marketing in India Milk, 4(3), 1–5.
53. Vishandass, A. K., Lal, R. C., Singh, J., & Sharma, A. (2018). Inefficiency in Cultivation of Maize on Different Sized Farm in Uttar Pradesh, India.
Int.J.Curr.Microbiol.App.Sci, 7(5), 635–647. https://doi.org/10.20546/ijcmas.2018.705.078
54. Wim, S. M., Xueqin, H., & Lu, Z. Q. (2011). China Agricultural Economic R eview Article information : 1978-2008. China Agricultral Economic
Review, 7(3), 448–466.
55. Zhang, B., Fu, Z., Wang, J., Tang, X., Zhao, Y., & Zhang, L. (2017). Effect of householder characteristics, production, sales and safety awareness on
farmers’ choice of vegetable marketing channels in Beijing, China. British Food Journal, 119(6), 1216–1231. https://doi.org/10.1108/BFJ-08-2016-
0378
56. Zylberberg, E. (2013). Bloom or bust? A global value chain approach to smallholder flower production in Kenya. Journal of Agribusiness in
Developing and Emerging Economies, 3(1), 4–26. https://doi.org/10.1108/20440831311321638
