SECTION: 4.1 Data Analysis Based on Survey of Farmersshodhganga.inflibnet.ac.in/bitstream/10603/4102/11/11_chapter 4.pdf · DATA PRESENTATION AND ANALYSIS INTRODUCTION This chapter

125

CHAPTER 4 DATA PRESENTATION AND ANALYSIS

INTRODUCTION

This chapter has been divided in to four parts. The researcher has made an attempt

to present the data in tabular form and analyze the same.

The first section deals with the analysis pertaining to the farmers and its

interpretations. The second section deals with the data related to commission agents, the

third section covers the data and tables related processing units and the fourth section

deals with the data related to hypothesis testing and its calculations.

SECTION: 4.1 Data Analysis Based on Survey of Farmers

Table No. 4.1: Classification of respondents on the basis of the size of farmers and

type of land holding

Land Type

Respondents Dry Irrigated

Dry &

Irrigated

Total

Proportion

of the total

agricultural

land

Small

Farmers (a)

56

(28%)

14

(7%)

18

(9%)

88

(44%) 17%

Medium

Farmers (b)

36

(18%)

4

(2%)

48

(24%)

90

(45%) 55%

Big

Farmers (c)

4

(2%)

0

(0%)

18

(9%)

22

(11%) 28%

Total 96

(48%)

18

(9%)

86

(43%)

200

(100%) 100%

Source: Survey Data

Figures in the parentheses indicate percentages

(a) 1 to 15 acres of agricultural land

(b) 16 to 50 acres of agricultural land

(c) Above 50 acres of agricultural land

126

56

36

4

14

4

0

18

48

18

0

10

20

30

40

50

60

R

e

s

p

o

n

d

e

n

t

s

Dry Irrigated Dry & Irrigated

Type of Land

Classification of respondents on the basis of the size of farmers and type of land holding

Small Farmers Medium Farmers Big Farmers

Graph No. 4.1

The above table presents the data related to the types of agricultural land and the

size farmers.

As per the data provided by table about 48% of the respondents possessed

completely dry land, 9% of the respondents had completely irrigated land and about 43%

of the respondents possessed both dry & irrigated land.

The respondents consisted of small, medium and big farmers. There were 44%

small farmers, (having 1 to 15 acres of land), 45% medium farmers (having 16 to 50

acres) and 11% big farmers (having 51 acres & above). The small and medium farmers

were more in number (89%) compared to big farmers (11%).

About 44% of the farmers were small farmers and the proportion of the total

agricultural land under small farmers was about 17%. The medium farmers who were

45% of the farmers had 55% of the total agricultural land and the big farmers who formed

11% of the total farmers possessed 28% of the total agricultural land. Compared to the

numbers of small farmers the land possessed by them was less whereas the big farmers

had more land compared to their numbers.

It was observed that about half of the respondents had complete dry agricultural

land and majority of them were small farmers. Majority of the medium and big farmers

had both dry & irrigated land. A small portion of farmers had completely irrigated land.

127

88

46

2 0

28

8

0

1612

0

10

20

30

40

50

60

70

80

90

R

e

s

p

o

n

d

e

n

t

s

1 to 10 11 to 20 21 & Above

Land in Acres

Classification of respondents on the basis of size and area under Tur Cultivation


Table No. 4.2 Classification of respondents on the basis of size and area under Tur Cultivation

Land (acres) Respondents

1 to 10 11 to 20 21 & Above Total

Small Farmers (a)

88 (44%)

0 (0%)

0 (0%)

88 (44%)

Medium Farmers (b)

46 (23%)

28 (14%)

16 (8%)

90 (45%)

Big Farmers (c)

2 (1%)

8 (4%)

12 (6%)

22 (11%)

Total 136

(68%) 36

(18%) 28

(14%) 200

(100%)

Proportion of land under Tur cultivation

12% 51% 37% 100%

Source: Survey Data





Graph No. 4.2

128

The above table depicts the area of agricultural land under Tur cultivation and the

size of farmers.

As indicated in the table about 68% of the farmers cultivated Tur in 1 to 10 acres

of land 18% farmers in 11 to 20 acres and 14% of the farmers utilized above 21 acres of

their agricultural land for Tur cultivation.

Small farmers possessed maximum of 15 acres of agricultural land and all the

small farmers cultivated Tur in 1 to 10 of acres of land. Half of the medium farmers

cultivated Tur in below 10 acres and half of them in above 10 acres of land. Most of the

big farmers cultivated Tur in above 10 acres of their land.

About 17% of the total area under Tur cultivation was under small farmers who

constituted 44% of the Tur producing farmers. 45% of the Tur producing farmers were

medium farmers and 55% of the area under Tur cultivation was under them. Big farmers

who cultivated Tur were about 11% and 28% of the land under Tur cultivation was being

utilized by them.

About 2/3 part of the farmers were cultivating Tur in 1 to 10 acres of land and 1/3

part of them were utilizing above 10 acres of land for Tur cultivation. As observed Tur

was cultivated in 42% part of the agricultural land and a major part of the agricultural

land was under the cultivation of Redgram.

The rains and overcast weather had dampened the spirits of the farmers as red

gram has been affected by pest. The crop is one of Gulbarga district’s major produce. On

the brighter side though, the advent of more powerful molecules to tackle the “heliothis”

pest has come as a blessing. The latest pesticides have helped control the pest menace.

129

6

86

04 2

15

02

6

62

0

18

0

10

20

30

40

50

60

70

80

90

R

e

s

p

o

n

d

e

n

t

s

Dry Irrigated Dry & Irrigated

Land Type

Cropping Pattern of Tur by the respondents

Rabi Kharif Summer Kharif & Rabi

Table No. 4.3 Cropping Pattern of Tur by the respondents

Land Type Season

Dry Irrigated Dry &

Irrigated

Total

Rabi 6

(3%) 2

(1%) 6

(3%) 14

(7%)

Kharif 86

(43%) 14

(7%) 62

(31%) 162

(81%)

Summer 0

(0%) 0

(0%) 0

(0%) 0

(0%)

Kharif & Rabi 4

(2%) 2

(1%) 18

(9%) 24

(12%)

Total 96

(48%) 18

(9%) 86

(43%) 200

(100%)

Source: Survey Data


Graph No. 4.3

130

The data presented in above table indicates the cropping pattern of Tur by the

farmers.

As per the data, about 81% of the farmers cultivated Tur in kharif season, 7% in

Rabi season and 12% of the farmers cultivated Tur in both Rabi and kharif seasons.

Most of the farmers cultivated Tur in kharif season. About 43% complete dry land

farmers, 31% both dry & irrigated and 7% irrigated land farmers cultivated Tur in kharif

season.

It was observed that most of the farmers cultivated Tur in kharif season and

majority of the agricultural land was under Tur cultivation during kharif season.

In the rainfed and dry areas Redgram are sown with the onset of the monsoon.

Earlier sowing gives higher yield in India. Late sowing causes considerable reduction in

yield due to photoperiodicity and excessive soil moisture stress which coincides with the

reproductive growth (Chandra et.al 1983).

Table No. 4.4

Land under different varieties of Tur Variety of Tur

Area (Acre) Benur BSMR Gulelle Maruti

Kempu gulellu

Total

1 to 10 22

(11%) 6

(3%) 26

(13%) 62

(31%) 18

(9%) 136

(68%)

11 to 20 16

(8%) 0

(0%) 0

(0%) 18

(9%) 2

(1%) 36

(18%)

21 & Above 10

(5%) 0

(0%) 0

(0%) 18

(9%) 0

(0%) 28

(14%)

Total 48

(24%) 6

(3%) 26

(13%) 98

(49%) 20

(10%) 200

(100%) Proportion of land under

cultivation 28% 2% 5% 60% 5% 100%

Proportion of Total Yield 21% 2% 15% 49% 12% 100%

Source: Survey Data


131

22

6

26

62

18 16

0 0

18

2

10

0 0

18

0

0

10

20

30

40

50

60

70

R

e

s

p

o

n

d

e

n

t

s

1 to 10 11 to 20 21 & Above

Land in Acres

Land under different varieties of Tur

Benur BSMR Gulelle Maruti Kempu gulellu

Graph No. 4.4

The above table & Graph represents the data about the varieties of Redgram

cultivated by the respondents.

As per the data in above table the farmers cultivated five varieties of Redgram

viz. Maruti, Benur, BSMR, Gulellu and Kempu gulellu. The ‘Maruti’ variety of Redgram

was cultivated by 49% of the farmers and it covered 60% of the land under Tur

cultivation. The variety ‘Benur’ was cultivated by 24% of the farmers and covered 28%

of the land. The other varieties ‘BSMR’, ‘Gulellu’ and ‘Kempu Gulellu’ were cultivated

by 26% of the farmers in 12% of the agricultural land under Tur cultivation.

The proportion of the area of cultivation and the yield varied from variety to

variety of Tur. It was observed that gulellu and Kempu gulellu were high yielding

varieties followed by ‘Maruti’ and ‘Benur’.

132

6

0 0

44

14

4

36

64

42

10

24

6

10

0

5

10

15

20

25

30

35

40

45

R

e

s

p

o

n

d

e

n

t

s

1 to 10 11 to 20 21 to 30 31 to 40 41 to 50

Yield in Quintals

Yield on Tur cultivation

1 to 10 11 to 20 21 & Above

Table No. 4.5 Yield on Tur cultivation

Returns of Tur cultivation included the main product - Redgram and the by products -

Pod Husk and Stalk.

Yield (Quintals) Area

(Acre) 1 to 10 11 to 20 21 to 30 31 to 40 41 to 50

50 & Above

Total

1 to 10 6

(3%) 44

(22%) 36

(18%) 42

(21%) 4

(2%) 2

(1%) 136

(68%)

11 to 20 0

(0%) 14

(7%) 6

(3%) 10

(5%) 6

(3%) 0

(0%) 36

(18%)

21 & Above

0 (0%)

4 (2%)

4 (2%)

2 (1%)

10 (5%)

6 (3%)

28 (14%)

Total 6

(3%) 62

(31%) 48

(24%) 56

(26%) 20

(10%) 10

(5%) 200

(100%)

Source: Survey Data


Graph No. 4.5

133

8890

2217

55

28

3439

27

0

10

20

30

40

50

60

70

80

90

R

e

s

p

o

n

d

e

n

t

s

Respondents Proportion of Tur Cultivated

Land

Proportion of Total Yield

Different types of Yield

Tur yield obtained by the respondents


As per the data provided in above table 68% of the farmers cultivated Tur in 1 to

10 acres of land and these farmers were able to produce 1 to 50 qtls of Tur. 18% of the

farmers cultivated Tur in 11 to 20 acres of land and the yield of these farmers ranged

from 11 qtls to 50 qtls. 14% of the farmers who cultivated Tur in above 20 acres of land

under Tur cultivation. Majority of the farmers cultivated Tur in 1 acre to 10 acres of land

and were getting yields in the range of 11 qtls to 50 qtls.

Table No 4.6 Tur yield obtained by the respondents

Farmers Respondents Proportion of Tur Cultivated Land

Proportion of Total Yield

Small Farmers (a)

88 (44%) 17% 34%

Medium Farmers (b)

90 (45%) 55% 39%

Big Farmers (c)

22 (11%) 28% 27%

Total 200 (100%) 100% 100%

Source: Survey Data


(a) 1 to 15 acres of agricultural land (b) 16 to 50 acres of agricultural land (c) Above 50 acres of agricultural land

Graph No. 4.6

134

The data provided in above table indicates that small farmers cultivated Tur in

17% of the total area of land under Tur cultivation and produced 34% of the total yield.

Medium farmers used 55% of the land under Tur cultivation and produced 39% of the

total yield. Big farmers used 28% of the land under Tur cultivation produced 27% of the

total yield.

It was observed that the productivity of small farmers was highest followed by the

big and medium farmers.

Table No. 4.7 Tur sale price and Size of the farmers

Farmers Sales Price

(Rs.) Small Farmers

(a)

Medium Farmers

(b)

Big Farmers

(c)

Total

2800 - 2900 0

(0%) 2

(1%) 0

(0%) 2

(1%)

2901 - 3000 16

(8%) 14

(7%) 6

(3%) 36

(18%)

3001 -3100 50

(25%) 56

(28%) 10

(5%) 116

(58%)

3101 – 3200 20

(10%) 18

(9%) 2

(2%) 42

(21%)

3201 – 3300 0

(0%) 0

(0%) 0

(0%) 0

(0%)

3301 - 3400 0

(1%) 0

(0%) 2

(1%) 4

(2%)

Total 88

(44%) 90

(45%) 22

(11%) 200

(100%)

Source: Survey Data





135

0

16

50

20

0 0

2

14

56

18

0 00

16

10

20

2

0

10

20

30

40

50

60

R

e

s

p

o

n

d

e

n

t

s


Types of Farmers

Tur sale price and Size of the farmers

2800 - 2900 2901 - 3000 3001 -3100 3101 – 3200 3201 – 3300 3301 - 3400

Graph No. 4.7

In the markets sales prices of Tur were found to vary in the range of Rs 2900 per

quintal to Rs 3400 per quintal. Majority of the farmers were able to sell their produce at

the price of Rs 3100 per quintal a price very close to the average sales price of Rs 3108

per quintal. It was observed that there was not much price deviation in the markets.

Majority of the farmers sold Tur at a price which was almost equal to the overall average

sales price in the market. Most of the farmers were selling their produce in the range of

Rs +100 to -100 of the average sales price in the market.

In the liberalized trade environments there are several aspects with which the

farmer needs to be familiarized regularly to enable him to plan his production for best

returns. This is possible only through constant market research and making their findings

available to farmers in the form of an advisory service. The Farmers Advisory System

will help the farmers in adopting good marketing practices. It will help him in taking

decisions such as which commodities to produce; how much to produce; how much to

sell and at what price, etc.

136

6

56

110

0

18

10

0

20

40

60

80

100

120

R

e

s

p

o

n

d

e

n

t

s

For Sale Domestic use/seeds

Purpose

Storage pattern of Tur by the farmers

1 to 4 5 to 8 9 to 12

Table No. 4.8 Storage pattern of Tur by the farmers

Purpose Storage Period

(Months) For Sale Domestic use/seeds

Total

1 to 4 6

(3%) 0

(0%) 6

(3%)

5 to 8 56

(28%) 18

(9%) 74

(37%)

9 to 12 110

(55%) 10

(5%) 120

(60%)

Total 172

(86%) 28

(14%) 200

(100%)

Source: Survey Data Figures in the parentheses indicate percentages

Graph No. 4.8

The data in above table indicates that about 86% of the farmers sold the complete

quantity of yield and 14% of them retained a small portion of the yield for own use.

Farmers stored Tur for a maximum period of 12 months. Tur was stored by farmers either

for sales or for own use.

Table 4.9

137

715

447

1073 1162

2235

1073

894

626 715

0

500

1000

1500

2000

2500

R

e

s

p

o

n

d

e

n

t

s

Land

Preparation

FYM Seeds &

Sow ing

Fertilizer and

its

Application

PPC Weeding &

Hoeing

Harvesting Threshing Winnow ing /

Bagging

Field Operations

Cost of cultivation incurred by the farmers

Average Expenses (Rs/Acre)

Cost of cultivation incurred by the farmers

Field Operations Average Expenses

(Rs/Acre) Proportion

(%)

Land Preparation 715 8%

FYM 447 5%

Seeds & Sowing 1073 12%

Fertilizer and its Application 1162 13%

PPC 2235 25%

Weeding & Hoeing 1073 12%

Harvesting 894 10%

Threshing 626 7%

Winnowing / Bagging 715 8%

Total 8940 100%


Graph No. 4.9

138

The above table presents the data related to the costs of cultivation incurred by

farmers. The data indicates that the cost of cultivation consisted of the expenses on

different materials and field operations. The expenses on materials included purchasing

of seeds, manures, fertilizers and plant protection chemicals. The expenses on field

operations included land preparation, sowing, application of fertilizers and plant

protection chemicals, weeding, hoeing, harvesting, threshing, winnowing, and bagging.

The expenses on the plant protection chemicals (25%) constituted a major

component of the cost of cultivation. Other major expenses were fertilizers and

application (13%), weeding and hoeing (12%) seeds and sowing (12%). The expenses on

the operations of harvesting, threshing, winnowing and bagging combined together

formed 25% of the cost of cultivation.

Pigeonpea pod borer, Helicoverpa armigera Hubner is the most devastating pest

causing damage up to 90 to 100%. To curtain the menace farmers are mainly relying on

chemicals. Farmer take up 10 to 12 sprays in a period of two months. This unilateral

approach of pest management has caused several un-warranted repercussions.

The vermicompost is one of the integral parts of organic farming. 1 Kg worms

after three months these worms are multiplied by 5 folds and also produce 6 quintals of

compost per pit. In order to reduce pest menace organic farming is the need of the hour

Table No. 4.10 Marketing Costs incurred by the farmers

Particulars Average Expenses (Rs/Qtl) Proportion

Gunny Bags 40 18%

Loading/unloading + Transportation + Weighment

50 23%

Cleaning 30 14%

Commission Charges 90 41%

Personal Expenses 10 4%

Total 220 100%


139

40

50

30

90

10

0

10

20

30

40

50

60

70

80

90

R

e

s

p

o

n

d

e

n

t

s

Gunny Bags Loading/unloading +

Transportation +

Weighment

Cleaning Commission Charges Personal Expenses

Particulars

Marketing Costs incurred by the farmers

Average Expenses (Rs/Qtl)

Graph No. 4.10

Above table presents the data related to the marketing costs incurred by the Tur

producing farmers. The commission of the agents formed a major part (41%) of the

marketing cost followed by cleaning, gunny bags, transportation, loading/unloading and

personal expenses.

Table No. 4.11 Constraints in the Cultivation of Tur

Constraints Respondents

Pest infestation 180 (90%)

Labour 110 (55%)

Weeding 76 (38%)

Hoeing 76 (38%)


140

Constraints in the Cultivation of Tur

Hoeing, 76

Weeding, 76

Labour, 110

Pest infestation, 180

Graph No. 4.11

Above table presents the data related to the constraints / problems faced by the

Tur producing farmers. Most of the farmers (90%) faced the problems of plant diseases,

pests and marketing, 55% of the farmers faced problems with laborers, 38% of the

farmers faced problems with field operations of weeding and hoeing.

It was observed that the major constraints faced by the farmers in the cultivation

of Tur were control of diseases and pests and laborers.

141

SECTION: 4.2 Data Analysis Based on Survey of Middleman

The market intermediaries acted between the producers and processors of

Redgram. They acted as purchasers for producers of Tur and sellers for processors of Tur.

In the activities of purchasing and selling middlemen earned profits as commission. The

commission agents, wholesale agents and co-operative societies etc, acted as market

intermediaries.

In the marketing of Redgram there were different market intermediaries having

different years of experience, different capacities of handling Redgram and employing

different number of laborers.

Table No. 4.12 Classification of Commission agents on the basis of the experience in to the business

Years No. of Respondents

(Commission Agents)

1 to 5 5 (10%)

6 to 10 21 (42%)

11 to 15 6 (12%)

16 to 20 9 (18%)

21 to 25 4 (8%)

26 to 30 3 (6%)

31 & Above 2 (4%)

Total 50 (100%)

Source: Survey Data


142

Classification of Commission agents on the basis of the experience in to the business

1 to 5

6 to 10

11 to 15

16 to 20

21 to 25

26 to 30

31 & Above

1 to 5

6 to 10

11 to 15

16 to 20

21 to 25

26 to 30

31 & Above

Graph No. 4.12

The concept of middleman who intervenes between the farmer and the ultimate

consumer, profiting in the act, is an old socialist augment, one which ultimately leads to

Government controlled monopolies.

Above table presents the data related to the business experience of respondents.

The data indicates that 5% of the market intermediaries were in to this business for more

than 30 years. The respondents who were in to this business since 21 to 30 years were

about 13% and 31% of the middlemen were in to this business since 11 to 20 years. The

respondents who were in to this business since 10 years constituted 51% of the

respondents

It was observed that the number of persons entering into the business of

marketing Tur as middleman was increasing.

143

Quantity of Tur handled by Middleman

20

14 16

1 – 400

401 – 600

Above 600

Table No. 4.13 Quantity of Tur handled by Middleman

Quantity handled (tons) Respondents

1 – 400 16 (32%)

401 – 600 20 (40%)

Above 600 14 (28%)

Total 50 (100%)

Source: Survey Data


Graph No. 4.13

The quantities of Tur handled by the middlemen are indicated in above table. As

per the data provided 32% of the middlemen handled up to 400 tonnes of Tur. 39% of the

middlemen handled between 401 to 600 tonnes and 29% of the middlemen handled more

than 600 tonnes of Tur in a year.

144

Employment of laborers by the middleman

0

5

10

15

20

25

30

1 2 3 4

Number of Labors

R

e

s

p

o

n

d

e

n

t

s

Casual labor Permanent

A major part of market intermediaries handled quantities of Tur in the range of

401 to 600 tonnes followed by the market intermediaries who handled up to 400 tonnes

and the middlemen handling above 600 tonnes of Tur.

Table No. 4.14 Employment of laborers by the middleman

Type of labor No. of Labors

Casual labor Permanent 1 Nil 27 (54%) 2 14 (28%) 18 (36%) 3 20 (40%) 4 (8%) 4 16 (32%) 1 (2%) Source: Survey Data


Graph No. 4.14

Above table gives the details of the laborers employed by market intermediaries.

The data shows the numbers and type of employment of the laborers.

Majority of the market intermediaries (53%) employed one labor on permanent

basis. Where as 41% of the middlemen employed three laborers on casual basis. No

145

Facilities provided to farmers by the middleman

0

10

20

30

40

50

60

Storage Transport Loading / Un

loading

Gunny Bags Credit Inputs to farmers

Facilities

Res

po

nd

en

ts

middlemen employed single casual labor. It was observed that most of the middlemen

employed 1 to 2 permanent laborers and 2 to 3 casual laborers. On an average the

middlemen employed 5 laborers out of which 2 were permanent and 3 were casual.

Table No. 4.15 Facilities provided to farmers by the middleman

Facilities Respondents Storage 50 (100%) Transport Nil Loading / Un loading Nil Gunny Bags 50 (100%) Credit 50 (100%) Inputs to farmers Nil

Source: Survey Data


Graph No. 4.15

The role of middlemen in providing agricultural credit and marketing facilities

and he act as both suppliers of inputs and as purchasers of farm outputs, and are either

credit advancing commercial middlemen or simply commissioning agents. Competition

between them does not affect the commission payable by the farmer. Middlemen play an

146

Knowledge of quality, grades and standards of different pulses among the intermediary

0

5

10

15

20

25

30

35

Little Average Perfect

Particulars

Resp

on

de

nts

important marketing role because of the small size of agricultural units and consequently

of the marketable surplus produced by individual farmers. Their elimination as credit

sources depends on the ready availability of institutional credit at the right time and

without extensive formalities. Their role in marketing is a more intractable problem.

The above table reveals the data related to the facilities provided by middlemen.

As per the data all middlemen provided the gunny bags and storage facilities commonly

to all farmers. None of the middlemen provided transport, loading/unloading and input

facilities (fertilizers, good quality seeds, pesticides etc) to farmers.

Table No. 4.16 Knowledge of quality, grades and standards of different pulses among the

intermediary

Particulars Respondents

Little 5 (10%)

Average 15 (30%)

Perfect 30 (60%)

Source: Survey Data


Graph No. 4.16

147

Traditionally, farmers are producers of food and other agricultural products by

utilizing natural resources, labor, skills and knowledge plus their investment, either from

their own saving or financial loans. But some farmers are poorer than others. Usually

they are landless farmers who need to rent other people’s land or become daily-waged

agricultural workers. Farm products are usually directed to local middlemen or brokers,

who sometimes provide farmers loans and factors of productions with tied conditions,

before distributing to larger national suppliers. These are the people who know the

market condition, grades etc. These people will then sell the products to the markets.

Under this market-oriented structure, most farmers are price takers. Their bargaining

power is low except in rare circumstances such as in very early or late season or during

period where supply is shortage.

As per the data provided in the above table all agents were found to possess the

knowledge of grades. 10% of the agents had little knowledge of the grades of Tur, 40%

had average and 60% had perfect knowledge.

Table No. 4.17 Knowledge of arrival / prices of different pulses prevailing in the different markets

and the media through which the information available

Media

Market Personal

Visit Neighbour

News Paper /

Radio / TV Telephone

Total

Local market 30

(60%)

10

(20%) Nil

10

(20%)

50

(100%)

Other market 3

(6%)

5

(10%)

10

(20%)

32

(65%)

50

(100%)


148

Knowledge of arrival / prices of different pulses prevailing in the different markets and the

media through which the information available

0

5

10

15

20

25

30

35

Personal Visit Neighbour News Paper / Radio / TV Telephone

Different types of Markets

Res

po

nd

en

ts

Local market Other market

Graph No. 4.17

Above table provides the data related to the methods followed by middleman to

collect information about the conditions of the local and other markets.

As per the data the information about the local market was gathered by personal

visit by 60% of the middleman and through neighbor by 20% and 20% respondents use

telephone to collect the information.

To collect the information of other markets 5% respondents used personal visit,

10% used neighbor, 20% used newspaper/radio/TV and 65% of the respondents used

telephone medium.

149

Policies followed by middleman in setting the purchase price

0

5

10

15

20

25

30

35

40

Price leadership Agreement among buyers Trade co-ordination Tender System

Policies Adopted

Re

sp

on

den

ts

Table No. 4.18 Policies followed by middleman in setting the purchase price

Policy adopted Respondents

Price leadership 3 (6%)

Agreement among buyers 10 (20%)

Trade co-ordination Nil

Tender System 37 (74%)

Total 50 (100%)

Source: Survey Data


Graph No. 4.18

Above table reveals the data about the policies followed by agents in fixing the

purchase price of Redgram. It is observed that majority (75%) of the respondents

followed the tender system for fixing the purchase price. 20% agents had agreement

among them for fixing the purchase price and 5% of the agents followed the price

leadership policy for fixing the purchase price of Redgram.

150

Statement showing the year of establishment and the installed capacity of the processing

units

5

12

10 0 0 0

5

21 1

2

0

2

0

21

26

17

5

1

34

1

0

5

10

15

20

25

30

< 5 yrs 5 – 10 Yrs 10 – 15 Yrs > 15 Yrs

Period of Establishment

Re

sp

on

den

ts

50 - 60 61 - 70 71 - 80 81 - 90 91 - 100 100 & Above

SECTION: 4.3 Data Analysis Based on Survey of Processing Units

The respondents included the processing units with different capacities, located at

different places, established in different periods.

Table No.4.19 Statement showing the year of establishment and the installed capacity of the

processing units

Period of Establishment Installed Capacity (tons)

< 5 yrs 5 – 10 Yrs 10 – 15 Yrs > 15 Yrs Total

50 - 60 5 1 2 1 9

61 - 70 0 0 0 0 0

71 - 80 5 2 1 1 9

81 - 90 2 0 2 0 4

91 - 100 21 26 17 5 69

100 & Above 1 3 4 1 9

Total 34 32 26 8 100


Graph No. 4.19

151

Laborers employed in processing units

Men

71%

Women

29%

Above table presents the data related to the establishment and installed capacity of

processing units.

As per the data the respondents consisted of the processing units which were

operating / working since more than 15 years. 8% of the processing units were working

since more than 15 years, 26% were 10- 15 years old, 32% were working since 5-10

years and 34% of the processing units were working since 5 years.

The installed capacity of 18% of the processing units was between 50 and 80

quintals per day. 73% of the processing units had the installed capacity of 81 to 100

quintals per day and 9% of the processing units had the capacity of processing above 100

quintals of Tur per day.

It was observed that the number of newly installed processing units had increased

steadily.

Table No.4.20 Laborers employed in processing units

Labor category Percentage

Men 71%

Women 29%

Total 100%

Source: Survey Data Total number of respondents = 100

Graph No. 4.20

152

Above table shows the data related to laborers employed in processing units. The

data shows that the laborers employed by processing units included both men and

women. 71% of the laborers were men and 29% of the laborers were women. On an

average a processing unit employed 11 number of laborers of which 8 were men and 3

were women.

Table No.4.21 Procurement cost of Redgram

Procurement Costs of Redgram

Price of Redgram

Transportation Loading/

Unloading Gunny Bags

Commission

Cost (Rs) 3207 221 74 55 129

Proportion 87.0% 6.0% 2% 1.5% 3.5%


Graph No. 4.21

Procurement cost of Redgram

3207

22174 55

129

0

500

1000

1500

2000

2500

3000

3500

Cost / Rs

Re

sp

on

de

nts

153

Procurement problems regarding availability of Tur

99

10

20

40

60

80

100

120

Regular Irregular

Availability of Redgram

Re

sp

on

den

ts

Above table presents the data about the different costs of procurement incurred by

the processing units. According to the data the total cost of procurement incurred by the

processing units included the cost of Redgram, transportation, loading / unloading, gunny

bags and commission. The cost of Redgram constituted 87% of the total procurement cost

followed by transportation cost (6%) commission (3%), loading / unloading (2.5%) and

gunny bags (1.5%).

Table No.4.22 Procurement problems regarding availability of Tur

Availability of Redgram Respondents

Regular 99

Irregular 1

Total 100


Graph No. 4.22

154

Availability of transportation facilities

74

15

9

2

0

10

20

30

40

50

60

70

80

Adequate Inadequate Costly Moderate

Transportation Facility

Re

sp

ond

en

ts

Pulses suffer heavy losses due to stored grain pests. The quality of seeds stored in

the traditional storage structures also deteriorates. Further, there are no small processing

units to convert pulse grains into Dal and other byproducts. This compels the growers to

dispose of their produce immediately after harvest at low price.

Above table reveals the data about availability of Redgram in markets for

procurement by processing units. According to the data 99% of the processing units

found regular availability of Tur in the markets. Most of the respondents were getting the

required quantity of Tur regularly in the markets. However, only 1% of the processing

units found difficulty in the procurement of Tur.

Table No.4.23 Availability of transportation facilities

Transportation Facility Respondents

Adequate 74

Inadequate 15

Costly 9

Moderate 2

Total 100 Source: Survey Data

Total number of respondents = 100

Graph No. 4.23

155

Availability of market facilities

97

3

Near Far away

From the above table it is found that transportation facilities were adequately

available to the 74% of the respondents and 15% of the respondents were finding

difficulties in getting the adequate transportation facilities. 9% of the respondents felt that

transportation facilities were costly and 2 % of the respondents felt they were moderate.

It was observed that most of the processing units were getting adequate

transportation facilities.

Table No.4.24 Availability of market facilities

Distance from the market Respondents

Near 97

Far away 03

Total 100


Graph No. 4.24

156

Procurement problems with respect to the market fees charges and the

pricing

59

1

36

4

0

10

20

30

40

50

60

70

Fair High

Market Fees / Charges

Re

sp

on

den

ts

High Reasonable

Above table gives the data about the distances between the processing units and

markets in procurement process of Tur. As per the data 97% of the processing units found

the markets at the places near to them and 3% of them found the market places at faraway

distances.

Table No.4.25 Procurement problems with respect to the market fees charges and the pricing

Price condition Market Fees/

Charges Fair High

Total

High 59 1 60

Reasonable 36 4 40

Total 95 5 100


Graph No. 4.25

157

Statement showing about the main product, by-product and wastage

80%

15%

5%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Main product By product – Chunni, Bhusa Wastage

Particulars

Re

sp

on

de

nts

The data about prices of Redgram in the markets and market fees/charges are

presented in the above table. According to the data 95% of the processing units were able

to procure Redgram at fair prices. Whereas, 5% of them found the prices of Redgram

were high. 60% of the respondents found that the market fees/charges were high and 40%

of them found the market fees/charges as reasonable.

Table No.4.26 Statement showing about the main product, by-product and wastage

Particulars Proportion

Main product 80%

By product – Chunni, Bhusa 15%

Wastage 5%

Source: Survey Data

Total number of respondents = 100

Graph No. 4.26

158

Problems regarding the availability of space and gunny bags

99

01 00

20

40

60

80

100

120

Timely Untimely

Availability of Gunny Bags

Res

po

nd

en

ts

Adequate Inadequate

In many countries of the world grain legumes are initially processed by removing

the hull and splitting into dicotyledonous components. Dehulling pigeonpea is a primary

process that converts the whole seed into dhal. Dehulling operation is usually performed

in two steps, the first involves loosening the husk from the cotyledons and the second

removing the husk from cotyledons and splitting them using a roller machine or stone

chakki.

Data about the final products of the processing of Redgram are presented in the

above table. As per the data provided the final products consisted of 80% Dal and 18%

chunni and bhusa. About 2% of Redgram was lost in the processing operations as

wastages.

Table No.4.27 Problems regarding the availability of space and gunny bags

Availability of Gunny Bags Availability of Space

Timely Untimely Total

Adequate 99 0 99

Inadequate 1 0 1

Total 100 0 100


Graph No. 4.27

159

Above table reveals the data regarding the availability of space and gunny bags

for storage. From the data it was found that 99 % of the respondents were having

adequate space for storage of goods and only 1 % of respondents faced the problem of

storage space. All respondents were able to get required numbers of gunny bags on time.

Table No.4.28 Statement showing the availability of power and fuel

Availability Power Supply Availability of Fuel

Continuous Intermittently breakdown Total

Sufficient 81 12 1 94

Insufficient 2 4 0 6

Total 83 16 1 100


Graph No. 4.28

Statement showing the availability of power and fuel

81

12

124

00

10

20

30

40

50

60

70

80

90

Continuous Intermittently breakdown

Availability of Power Supply

Res

po

nd

en

ts

Sufficient Insufficient

160

Availability of Labor and Mode of Payment

Sufficient, 37

Sufficient, 45

Insufficient, 2Insufficient, 0

Sufficient, 8

Insufficient, 8

0

5

10

15

20

25

30

35

40

45

50

Daily Weekly Monthly

Mode of Payment

Re

sp

on

den

ts

Sufficient Insufficient

Above table presents the data about availability of power and fuel to processing

units. According to the data power was continuously available to 83% of the dal mills,

intermittently to 16% of them and 1% of the processing units were facing the problem of

power breakdown. 94% of the processing units were able to get adequate fuel and 6% of

them were facing the shortage of fuel.

It is observed that majority of the processing units were getting continuous power

supply and adequate quantity of fuel.

Table No.4.29 Availability of Labor and Mode of Payment

Mode of Payment Availability of Labor Daily Weekly Monthly

Total

Sufficient 37 8 45 90

Insufficient 2 8 0 10

Total 39 16 45 100


Graph No. 4.29

161

Agro-processing is now regarded as the sunrise sector of the Indian economy in

view of its large potential for growth and likely socio economic impact specifically on

employment and income generation. Some estimates suggest that in developed countries,

up to 14 per cent of the total work force is engaged in agro-processing sector directly or

indirectly. However, in India, only about 3 per cent of the work force finds employment

in this sector revealing its underdeveloped state and vast untapped potential for

employment

Above table presents the data about the availability and mode of payment of the

laborers in processing units.

The data shows that 90% of the processing units were able to get the required

numbers of workers and 10% of them were facing problems in finding the required

numbers of workers. Majority of the processing units (45%) who were easily getting the

required numbers of workers made payments on monthly basis and 37% were paying on

daily basis.

The processing units which were finding difficulties in getting the required

numbers of workers were making payments on daily and weekly basis.

Table No.4.30 Availability of finance and source

Sources of

Finance

Procurement

of finance

Banks Other

Total

Easy 35 56 91

Difficult 6 3 9

Total 41 59 100


162

Graph No. 4.30

Availability of finance and source

Easy, 35

Easy, 56

Difficult, 6 Difficult, 3

0

10

20

30

40

50

60

Banks Other

Source of Finance

Res

po

nd

en

ts

Easy Difficult

Above table presents the data about the problems faced by the processing units in

arranging the finance. As per the data the dal millers were getting finance from banks and

other sources. 41% of the dal millers were financed by local banks and 59% of dal millers

were financed from other sources such as private financing agency, money lenders etc.

About 91% processing units were able to get the required finance easily.

Table No.4.31 Statements showing availability of customers and transportation Facilities

Availability of Customer Transportation Facilities

Good Poor Total

Adequate 97 0 97

Inadequate 3 0 3

Total 100 0 100


163

Statements showing availability of customers and transportation Facilities

Adequate, 97

Adequate, 0 Inadequate, 0Inadequate, 30

20

40

60

80

100

120

Good Poor

Source of Finance

Re

sp

on

de

nts

Adequate Inadequate

Graph No. 4.31

The data about problems faced by processing units in marketing of dal are

presented in the above table.

According to the data all processing units were finding good number of customers

in the market. 97% of the respondents were getting adequate transportation facilities for

marketing of dal and 3% of them were finding difficulties in getting adequate

transportation facilities. It is observed that all processing units were finding good number

of customers and most of them were getting adequate transportation facilities.

164

Table No.4.32 Price spread, Marketing margin and Farmer's share for Tur (Rs./quintal)

A. Farmer Existing

channel

Proposed

channel

a. Gross Return 3108 3108

b. Expenses incurred by farmer

ii. Commission of the agent and market charges 90 0

iii. Marketing cost 130 130

c. Net price received by farmer 2888 2978

B. Commission agent

a. Purchase price 3108 0

b. Marketing cost 90 0

c. Market margin by the agent 128 0

C. Dall mill

a. Purchase price 3326 3108

b. Commission of the agent 128 0

c. Marketing cost 222 222

d. Expenses incurred by the dal mill 371 371

D. Final price 4047 3701

Source: Survey Data

As apparent from the above table, the farmers who sold their produce through this

channel realized with a net price of Rs.3108 per quintal. The farmers marketed their

produce through commission agents and Dall mills, who reaped away large amount of

producer’s margin.

Commission agents as marketing intermediary was found to be involved in this

channel. It was found that commission agents also reaped benefit with only a small effort.

Dall mills who are the processing units were also found to be one of the

intermediaries in this channel, which was reaped comparatively higher benefit than the

commission agents.

165

Table No.4.33 Marketing Efficiency of Tur

Particular Existing channel Proposed channel

Net farmer share (Rs./quintal) 2888 2978

Total marketing cost and margins 1159 723

Final price 4047 3701

Marketing Efficiency Index 2.49 4.11

Source: Survey Data

Shepherd’s Equation:

MEI = (V/ I ) – 1

Where

MEI = Marketing Efficiency Index

V = Value of the goods sold (Final Price)

I = Total Marketing Cost and margins

Marketing Efficiency Index (MEI) estimated in marketing of Tur is presented in

the above table. It is revealed form the table that the marketing efficiency of the existing

channel was 2.49. An alternative to the existing channel was proposed and the marketing

efficiency was found to be 4.11 and the marketing margins would be less than to the

existing channel which would result in better efficiency of marketing of Tur compared to

the existing channel.

166

SECTION: 4.4 Testing of Hypothesis

Hypothesis 1: The production of Tur is positively related to the area under crop.

The result of correlation analysis depicts that the production of Tur in quintal is

significantly correlated with the area under the crop (r = 0.581). The result of correlation

analysis provided support for the hypothesis.

Table No 4.34: Showing the result of simple regression analysis

R R Square Adjusted R

Square

Std. Error of

the Estimate

.581 .338 .330 13.067

Predictors: (Constant), Area Crop

Table No 4.35: Showing analysis of variance

Sum of Squares

df Mean

Square F Sig.

Regression 7399.525 1 7399.525 43.338 .000

Residual 14512.912 85 170.740

Total 21912.437 86

Predictors: (Constant), Area Crop, Dependent Variable: Production

Table No 4.36: Showing the analysis of coefficients

Unstandardized Coefficients

Standardized Coefficients

t Sig.

B

Std. Error

Beta

(Constant) 7.726 1.995 3.872 .000

Area Crop 0.823 0.125 0.581 6.583 .000

Dependent Variable: Production

167

The result of Entered regression method depicts that the production of Tur (p <

0.05) was found to be statistically significantly related with the area under crop. The null

hypothesis that the linear relationship does not exist was rejected at the 0.05 significant

level since the significant probability of the F-test was 0.000.

The explanatory power of independent variable to the dependent variable can be

estimated because the coefficient of determinant (R2) was at least 33.8%.

The coefficient of regression equation is statistically significant at the 0.05 level.

So, it can be interpreted that the result of Entered regression analysis method provided

support for the above hypothesis. There is a positive association between area under the

crop and the production of Tur. It shows that the production of Tur can increase of 0.823

quintal per acre of the area under the crop.

Hypothesis 2: The quantity of sale of Tur is positively related to the production of the

product. The result of correlation analysis depicts that the quantity of sale of Tur in

quintal is significantly correlated with the production of the product (r = 0.999). The

result of correlation analysis provided support for the hypothesis.

Table 4.37: Showing the result of simple regression analysis

R R Square Adjusted R Square

Std. Error of the Estimate

.999 .998 .998 .767

Predictors: (Constant), Production

Table No 4.38: Showing analysis of variance

Sum of Squares

Df Mean

Square F Sig.

Regression 21252.063 1 21252.063 36124.425 .000

Residual 50.006 85 .588

Total 21302.069 86

Predictors: (Constant), Production, Dependent Variable: Quantity sold

168

Table 4.39: Showing the analysis of coefficients

Unstandardized

Coefficients Standardized Coefficients

t Sig.

B Std. Error Beta

(Constant) 0.075 0.121 0.624 0.534

Production 0.985 0.005 0.999 190.064 0.000

Dependent Variable: Quantity sold

The result of Entered regression method depicts that the quantity of sale of Tur (p

< 0.05) was found to be statistically significantly related with the production of the

product. The null hypothesis that the linear relationship does not exist was rejected at the

0.05 significant level since the significant probability of the F-test was 0.000.



The coefficient of regression equation is statistically significant at the 0.05 level.

So, it can be interpreted that the result of Entered regression analysis method provided

support for the above hypothesis. There is a positive association between the quantity of

sale of Tur and the production of Tur. It shows that the quantity of sale of Tur can

increase of 0.985 quintal for the production of the crop.

Hypothesis 3: The sale of Tur is positively related to the price per quintal.

The result of correlation analysis depicts that the sale of Tur in quintal is

significantly correlated with the price per quintal (r = 0.092). The result of correlation

analysis provided support for the hypothesis.

Table 4.40: Showing the result of simple regression analysis

R R

Square

Adjusted R

Square

Std. Error of the

Estimate

0.092 0.008 -0.003 15.764

Predictors: (Constant), Price/Qtl

169

Table 4.41: Showing result of the analysis of variance

Sum of Squares df

Mean Square F Sig.

Regression 179.765 1 179.765 .723 0.397

Residual 21122.304 85 248.498

Total 21302.069 86

Predictors: (Constant), Price/Qtl, Dependent Variable: Quantity sold

Table 4.42: Showing result of the analysis of coefficients

Unstandardized Coefficients

Standardized

Coefficients t Sig.

B Std.

Error Beta

(Constant) -38.574 65.240 -0.591 0.556

Price/Qtl 0.018 0.021 0.092 0.851 0.397

Dependent Variable: Quantity sold

The result of Entered regression method depicts that the sale of Tur (p < 0.05) was

found to be statistically not significant related with the price per quintal. The null

hypothesis that the linear relationship does not exist was accepted at the 0.05 significant

level since the significant probability of the F-test was 0.397.



The coefficient of regression equation is not statistically significant at the 0.05

level. So, it can be interpreted that the result of Entered regression analysis method did

not provide support for the above hypothesis. There is not a positive association between

the sale of Tur and the price per quintal.

170

Hypothesis 4: Variety of Tur production differs significantly due to the area under crop.

Table No 4.43: Output in a one-way ANOVA (Single Factor)

Sum of Squares

df Mean

Square F Sig.

Between Groups 202.174 2 101.087 54.735 .000

Within Groups 363.826 197 1.847

Total 566.000 199

The ANOVA test revealed the significant influence of area under crop in the

variety of Tur at the 5% level. It clearly states that the null hypothesis is rejected as F-

statistics of 54.735 on (2, 197) degrees of freedom for which p-value is 0.000. Hence, it

can be stated that the variety of Tur depends on the area under the crop.

Hypothesis 5: Proportion of yield differs significantly due to the area under the crop.

Table 4.44: Output in a one-way ANOVA (Single Factor)

Sum of Squares

df Mean

Square F Sig.

Between Groups 239.651 2 119.825 115.293 .000

Within Groups 204.744 197 1.039

Total 444.395 199

The ANOVA test revealed the significant influence of area under crop in

the proportion of yield of Tur at the 5% level. It clearly states that the null hypothesis is

rejected as F-statistics of 115.293 on (2, 197) degrees of freedom for which p-value is

0.000. Hence, it can be stated that the proportion of yield differs significantly due to the

area under the crop.

171

Hypothesis 6: Proportion of yield differs significantly due to the different categories of farmers.

Table No 4.45: Output in a one-way ANOVA (Single Factor)

Sum of Squares

df Mean

Square F Sig.

Between Groups 64.621 2 32.310 26.778 .000

Within Groups 237.699 197 1.207

Total 302.320 199

The ANOVA test revealed the significant influence of categories of the farmers

on the proportion of yield of Tur at the 5% level. It clearly states that the null hypothesis

is rejected as F-statistics of 26.778 on (2, 197) degrees of freedom for which p-value is

0.000. Hence, it can be stated that the proportion of yield of Tur depends on the

categories of farmers.

SECTION: 4.1 Data Analysis Based on Survey of Farmersshodhganga.inflibnet.ac.in/bitstream/10603/4102/11/11_chapter 4.pdf · DATA PRESENTATION AND ANALYSIS INTRODUCTION This chapter

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