Mechanization of chickpea production in Andhra Pradesh: Meso and

1

Mechanization of chickpea production in Andhra

Pradesh: Meso and micro level analysis

Report Submitted to

International Crop Research Institute for the Semi-Arid Tropics

By

Shreya Bhardwaj

International Crops Research Institute for the Semi-Arid Tropics

Patancheru, 502 324

Telangana, India

[email protected]

July, 2014

mailto:[email protected]

2

July 11, 2014

To Whomsoever It May Concern

This is to certify that this project report on ―Mechanization of chickpea production in Andhra

Pradesh: Meso and micro level analysis‖ is a bonafide record of work done by Ms Shreya

Bhardwaj under my supervision and submitted to International Crop Research Institute for

the Semi – Arid Tropics, (ICRISAT), Patancheru, Telangana, India.

Dr. S. Nedumaran Dr. MCS Bantilan

Principal Scientist ResearchProgramme Director

Markets, Institutions and Policies Markets, Institutions and Policies

ICRISAT – Patancheru ICRISAT – Patancheru

Telangana – 502324 Telangana – 502324

India India

3

DECLARATION

I do hereby declare that the dissertation entitled ―Mechanization of chickpea production in

Andhra Pradesh: Meso and micro level analysis‖ is an original and independent record of

project work undertaken by me under the supervision of Dr. S. Nedumaran (Scientist) at

Markets, Institutions and Policies, International Crop Research Institute for Semi- Arid

Tropics (ICRISAT), Patencheru, Andhra Pradesh, India, during the period of my study as a

part of curriculum of Masters in Agri-business Economics at Gokhale Institute of Politics and

Economics, Pune.

HYDERABAD By

Date: 11th

July, 2014 Shreya Bhardwaj

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Acknowledgment

I express my deep gratitude to Dr. MCS Bantilan, Research Program Director (RP-Markets,

Institutions and Policies) for selecting me and thus providing me with the opportunity of

interning at ICRISAT, an institute of international repute and excellence.

I also express my thankfulness and indebtedness to Dr. S Nedumaran (Scientist) in RP-MIP

for always guiding me and sharing his knowledge with me without which the completion of

this project would not have been possible.

I would like to extend my heartfelt gratitude to Dr. D Moses Shyam (Special Project

Scientist- Economics) for taking time off his busy schedule to extend a helping hand as and

when required.

My special thanks to Ms Joytsna, Mrs C. Pamela and Mrs Deepti Rajan for their unending

support.

On this occasion I would take the opportunity to thank my parents who have always with me

through thick and thin and my friends for believing in me and thus being the pillar of support

throughout.

.

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Abstract

Global yields of chickpea are low, and have been relatively stagnant for much of the last two

decades. India has a dominating influence on these trends owing to its large share in the

global production of these crops. A number of biotic and abiotic factors limit realization of

yield potential. The sluggish growth in chickpea yields in India can be attributed to:

the shift in crop area from favourable to marginal environments

the slow uptake of improved varieties and other production technologies

Its cultivation on poor soils under erratic rainfall conditions.

The scope of raising chickpea production in Asia through area expansion alone is limited.

Therefore, the main challenges for research and development are to bridge the gap between

actual and attainable yield by enhancing farmers‘ access to quality inputs, improved

technologies and information; and improve the competitiveness of pulse crops through

domestic incentives related to production, marketing, processing, and prices in line with

cereals and competing crops.

In view of the significance of agricultural mechanization for chickpea production, this study

analysed various factors influencing agricultural mechanization in the chickpea producing

districts of Andhra Pradesh. Information needed for the study was obtained from the

ICRISAT database comprising of 269 households and 9700 plots across four major chickpea

producing districts (Anantapur, Kurnool, Kadapa and Prakasam) of Andhra Pradesh. The

regression analysis of determinants of expenditure on machineries revealed five significant

variables namely, expenditure on labour, total production value of chickpea, area under

irrigation, operated land and variety of chickpea grown. The R square of 0.86 and adjusted R

square of 0.86 indicate the high explanatory power of the model as a whole. The regression

model related to tractorization predicted the degree of draft animal used and variety of

chickpea grown as significantly influencing factors, with R square of 0.82 and adjusted R

square of 0.82.

Title : Mechanization of chickpea production in Andhra Pradesh: Meso and micro level analysis

Name : Shreya Bhardwaj

Institution : Gokhale Institute of Politics and Economics

Supervisor : Dr. S. Nedumaran

Date : 11.07.2014

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CONTENTS

CONTENTS Page no.

1. Introduction 7 – 8

2. Review of literature 8 -10

3. Methodology 10 – 11

4. Result and discussion 12 – 25

4.1 Growth of agricultural mechanization in different states (1966 – 2003) 12 – 19

4.2 Analysis of chickpea production in Andhra Pradesh 20 – 21

4.3 Analysis of labour force and bullocks used for chickpea cultivation in

Andhra Pradesh 22 – 24

4.4 Analysis of various machineries used in chickpea production in Andhra

Pradesh 24 – 25

5. Regression analysis of the household data 26 – 33

5.1 Determination of dependent and independent variables 26 – 28

5.2 Model specification 29 -33

5.3 Analysis of regression result 33

6. Conclusion 34 – 35

7. References 36 – 37

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List of Tables

TABLES Page No.

Table 1.1 Tractors used in various states of India 13

Table 1.2 Mechanization in Andhra Pradesh 13

Table 1.3 Use of various machineries per 1000 hectares in 1992 14

Table 1.4 Various machineries used in Himachal Pradesh 15

Table 1.5 Various machineries used in Gujarat and Punjab 16

Table 1.6 Use of machineries per 1000 hectares in 2003 17



Table 1.9 Mechanization in Andhra Pradesh 19

Table 2.1 Production of various varieties of chickpea in Andhra Pradesh 20

Table 2.2 Production of chickpea in different districts of Andhra Pradesh 20

Table 2.3 Production of different varieties of chickpea in different districts of

Andhra Pradesh 21

Table 2.4 Percentage share of various varieties of chickpea in different districts of

Andhra Pradesh 21

Table 3.1 Labour used for various operations of chickpea cultivation in Andhra

Pradesh 23

Table 3.2 Labour used for chickpea cultivation in different districts of Andhra

Pradesh 23

Table 3.3 Bullock used for chickpea production in different districts of Andhra

Pradesh 24

Table 4.1 Uses of tractors in different districts of Andhra Pradesh 24

Table 4.2 Machinery used in chickpea production in various districts of Andhra

Pradesh 25

List of Graphs

GRAPHS

Page

No.

Graph 1.1 Machinery used in Andhra Pradesh 13

Graph 1.2 Total mechanization in different states in 1992 14

Graph 1.3 Total mechanization in different states in 1987 and 2003 18

Graph 2.1 Production of different varieties of chickpea in Andhra Pradesh 21

Graph 3.1 Labour used for various operations of chickpea cultivation in Andhra

Pradesh 22

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1.Introduction

Chickpea is an important leguminous food grain. The area covered under chickpea cultivation

is around 10 million hectares and the annual production average to 7.5 million tonnes. India

is the largest producer of chickpea with about 63% of the total area under chickpea

production lying in India (Rao et al. 2010). Chickpea is a highly nutritious grain legume crop.

It is an important source of energy, proteins, minerals, fibres and vitamins. Mature chickpea

grains contain 12% to 31% proteins — higher than any other pulse crop; 60% to 65%

carbohydrates and 6% fat (Geervani et al. 1991). Chickpea is also a good source of soluble

and insoluble fibres, vitamins (B vitamins), potassium and phosphorus, hence is increasingly

advocated in health-conscious diets. Chickpea is one of the cheapest sources of protein (Joshi

et al. 2002; Byerlee and White 2000); hence it can play an important role in overcoming

problems related to nutritional insecurity of the poor in developing countries where it is

grown and consumed. On the basis of grain size, colour and taste, chickpea is classified into

desi chickpea and kabuli chickpea. Grains of desi chickpea are small in size, are light to dark

brown in colour, and have a thick seed coat. Grains of kabuli chickpea are bigger in size,

have a cream colour and thin seed coat. The desi type however, is more prominent – it

accounts for close to 80% of global chickpea production, and kabuli type chickpea comprises

the rest (Agbola et al. 2002). Desi chickpea can withstand cooler temperatures and matures

quicker than kabuli chickpea.

Chickpea is a crop of temperate regions generally cultivated on sandy loam soils under low

rainfall conditions. Globally, chickpea is grown on 11 million ha. However, most of the

chickpea area is concentrated in South Asia, which accounts for more than three-fourths of

the world chickpea area. With advances in plant breeding, despite being a crop of the

temperate region, chickpea cultivation is gradually spreading to sub-tropical and tropical

regions of Africa, North America and Oceania. In Africa, it is grown mainly to utilize fallow

lands. Africa‘s share in global chickpea area has gradually increased to 4.6% in 2005-07 from

3.8% in 1981-83. While kabuli is mainly grown in temperate climate of West Asia, the desi

variety is generally grown in warmer climates of South Asia and East Africa.

In North America and Oceania, chickpea is a new crop. Introduced there in the 1980s, its area

has expanded considerably. In 2005-07, North America and Oceania accounted for 1.6% and

2% of the world chickpea area, respectively (Rao et al.2010). This rapid expansion of

chickpea to non- traditional areas could be attributed to the huge export opportunities to the

Indian subcontinent where chickpea is widely consumed, and the demand for pulses,

including chickpea, far exceeds its domestic production. Also, increasing globalization of the

agri-food markets under the World Trade Organization created more export opportunities for

North America and Oceania. Asia accounts for 89% of the global chickpea area, i.e. 9.9

million ha, and 80% of it is cultivated in South Asia (Rao et al.2010). West Asia accounts for

almost the rest of the chickpea area. India is the largest producer of chickpea in the world. It

accounts for two thirds of the global and 72% of Asia‘s chickpea area. Pakistan and Iran are

other important chickpea growing countries in the region. In 2005-07, these two countries

accounted for about 11% and 7% of Asia‘s chickpea area, respectively (Rao et al.2010). In

India, chickpea is largely grown under rain fed conditions; only one-third of the area is

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irrigated (GOI, various years). Its cultivation is largely confined to the semi-arid tropics,

which occupy 61% of the country‘s total chickpea area. Semi-arid temperate regions account

for about a quarter of the total chickpea area in India.

Apart from these the main constraints in chickpea production are wilt, dry root rot diseases

and drought. However to overcome these shortcomings two new varieties were introduced:

ICCC-37 and ICCV-2. These varieties were resistant to wilt and tolerant to dry root rot

disease. The ICCV-2 has the ability to escape terminal drought due to early maturity and it

fetches higher price. From 1980-2007 global chickpea yield increased at an annual rate of

0.9% from 649 Kg per ha to 803 Kg per ha while cereals and oilseeds grew at 1.2% and 4.1%

per annum respectively (Rao et al 2010). The reason for the sluggish growth were shift in

crop area from favourable to marginal environment, the slow uptake of improved varieties

and other production technologies and cultivation mainly took place on poor soil under

erratic rainfall. In India widespread deficiency of sulphur and zinc in growing regions of

chickpea has also adversely effected production (Rao et al.2010). Hybrid varieties of

chickpea improved production and farm income, made farming more diverse and sustainable,

lead to decline in crop protection related expenses, losses and human health risks as better

quality crops were available to people (Rao et al.2010). However during the last 25 years the

area under chickpea production in India has contracted at a rate of 0.3% per year while

chickpea cultivation has increased in non-traditional areas of Canada, USA and Australia.

Objectives of the study

To analyse the trends and variation of mechanization in India by states using meso

level (districts) data from 1966 – 2003.

To analyse different farm machineries and equipments used for chickpea production

in Andhra Pradesh using micro level farm household data collected during the

cropping season 2012 -2013.

To analysis the factors that affect the adoption of agricultural machinery in chickpea

production using household data.

To analyse the determinants of mechanization in chickpea cultivation in Andhra

Pradesh

2. Review of literature

Mechanisation is defined as the process of injecting power and machinery between man and

material in a production system. Farm Mechanization has been helpful in bringing about a

significant improvement in agricultural productivity in many developing countries. The

quality and precision of the agricultural operations are equally significant for realizing higher

yields. The various operations such as land levelling, irrigation, sowing and planting, use of

fertilizers, plant protection, harvesting and threshing need a high degree of precision to

increase the efficiency of the inputs and reduce the losses. There is a good chance to reduce

the cost of production if farm operations are mechanised as it saves labour both human and

bullock. The output per hectare for almost all crops increases as the level of mechanization

increase from irrigated non-mechanised farms to tube wells, tractor-thresher farms (Verma et

al. 1990). Mechanisation often leads to increase cropping intensity which in turn increases the

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demand for farm labour. In fact the cropping intensity of tractor using households exceeds

that of bullock farms by almost 8% (Verma et al. 1990). In the absence of mechanization the

ever increasing wage rate of human labour and cost of upkeep of draught animal could drive

up the cost of production.

Mechanization further reduces the weather risks by enabling the farmers to diversify and

decrease the risk of non-availability of labour and thus wastage is minimized. A major benefit

of mechanization is greater efficiency during the harvest operation which maximises yield

and minimizes quality loss due to extended exposure to bad weather (Sassenrath et al.2007).

Timely marketing is also possible by quick mechanical transportation, cleaning and handling.

Demand for semi-skilled labour in place of unskilled labour also increases as more skilled

workers are required to operate the complex farm equipment. This poses a great threat in a

developing country like India where availability of skilled labour in the agricultural sector is

less (Gowda & Gaur et al 2004). The drudgery for human labour is reduced and unhygienic

operations such as handling of farm yard manure are done with machinery. Mechanization

was first adopted by large farmers who then provided these services to medium and small

holder farmers. Higher minimum support prices for crops and custom hire possibilities could

further enhance the profitability of acquiring agricultural mechanization inputs by farmers.

Increase in demand for machineries often lead to the development of competitive and viable

manufacturing industries in the country.

However there are various constraints faced by Agricultural mechanization:

Small size and scattered holdings of the farmers stand in the way of mechanization.

As a result farm machinery remains under-utilized.

Majority of small cultivators are poor who are not in a position to purchase costly

machinery like tractors, combine harvesters etc.

Non-availability of credit facilities.

Lack of affordable and secure access to complementary inputs such as fuels,

electricity and larger piece of land.

Lack of higher efficiency and capacity of public sector for implementing policies.

Despite all the above mentioned constraints the demand for major machinery has shown an

increasing trend. Electric pumps are preferred over diesel engines operated pumps due to

lower cost and higher energy-use efficiency. This is evident from the fact that growth rate of

electric pumps was 8.20% as against 4.89% in case of diesel pumps (Singh et al. 1999). The

average farm power availability increased from about 0.25kw per hectare in 1951 to about

1.35kw per hectare in 2001 (Department of Agriculture 2002). The power tillers are being

used presently in rice and sugarcane producing areas of Tamil Nadu, AP, Kerala, Karnataka,

West Bengal, Bihar and Maharashtra (Singh et al. 1979). Use of power threshers for wheat

cultivation became very popular even among the small farmers. Tractorization has proceeded

much faster in states like Punjab and Haryana which have gone through green revolution as

compared to other states (Singh et al. 1979).

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Agricultural mechanization generates many non-farming and subsidiary activities among the

farming households. A tractor owner is able to increase his household income by

undertaking supplementary activities such as dairying and provisions of custom hiring.

Mechanization is determined by a number of social, agricultural and economical factors.

Age-old customs, lack of support services through government extension agencies for

providing knowledge and information of modern agricultural technologies and lack of access

to institutional credit are some of the main hindrances for getting the optimum benefits from

the farm mechanisation especially for small and marginal farmers. The demand for major

machinery has shown an increasing trend, in fact, there is 1-1.5% per annum progress of farm

mechanization in terms of demand of agricultural equipment (department of agriculture,

2004). In India the farm mechanisation started with the establishment of Central Tractor

Organisation (CTO). The first and most important mechanization in India was groundwater

pumping using engine and electric motor driven irrigation pumps.

In 1930s the pump sets were introduced in the country and the manufacturing of irrigation

pump sets started in late 1950s while tractors and power tillers in 1960. India is the largest

producer of tractors with 500000 units and exports over 50000 units. By 2010 tractors

reached at about 4 million units and are expected to stabilize at around 7 million by 2050.

The number of electric motor pumps increased from 4 million in 1981to 20 million units in

2010 and is expected to increase to 25 million units by 2015 (Singh et al. 2012). Draft animal

population will decrease drastically whereas power tillers, diesel engines and electric motors

will increase significantly during 2010 to 2050. Further the area under feed and fodder for

draught animals could be reduced due to decline in their use. The land thus, released can be

brought under farm mechanization.

3. Methodology

The objective of this report is to observe how mechanization has changed over time across

India for all major crops and then specifically in Andhra Pradesh for chickpea cultivation. For

this Meso data collected by ICRISAT is used that depicts various agricultural machinery

utilized in 19 different states of India in years 1966,1977,1987,1992 and 2003. Then Andhra

Pradesh is observed solely in terms of chickpea production. First we analyse the various

varieties of chickpea grown in Andhra Pradesh using the primary data collected by ICRISAT

comprising of 269 households and 9700 plots across four major chickpea producing districts

of Andhra Pradesh namely, Anantapur, Kadapa, Kurnool and Prakasam. Analysis of this is

done using basic statistical graphs and Tables.

In the next phase we observe the uses of labour force, bullocks and various agricultural

machineries (tractors, threshers, sprayers) in Andhra Pradesh for chickpea production. The

data used for this purpose is the primary data collected by ICRISAT comprising 9700 plots of

chickpea crop in Andhra Pradesh. Here also the analysis is done using statistical graphs and

Tables.

In the last section efforts have been made to understand what all factors influence expenditure

on machineries in chickpea producing districts of Andhra Pradesh. Data used belongs to the

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ICRISAT database consisting of 269 households and 9700 plots of chickpea in Andhra

Pradesh as mentioned above. Analysis of this is done using descriptive statistics and

regression model to prove the various hypotheses and to show which all explanatory

variables have a significant impact on mechanization.

Model specification

The following three regression models are constructed to analyse the factors influencing

agricultural mechanization in chickpea production in Andhra Pradesh.

Model 1: agricultural mechanization

Agricultural mechanization, as discussed in the theoretical analysis is influenced by several

economic and social factors. Accordingly, it is hypothesized that agricultural mechanization

in the study area is influenced by a set of independent variables: X1…..Xn

The model is specified as follows:

Y = a + b1X1 + b2X2 +………………….+ bnXn + ui

Where Y is the dependent variable i.e. expenditure on machineries, ‗a‘ is the intercept and b1,

b2………..bn are the coefficients of the explanatory variables (such as expenditure on seeds,

fertilizers, labour, total production, value of the production, operated land, irrigated land etc.)

X1, X2…………………..Xn. Here ui is the error term


In this some of the insignificant variables from the above model are dropped while some

significant variables are included


Y = a + b1X1 + b2X2 +………………….+ bnXn + ui

Where Y is the dependent variable i.e. expenditure on machineries, ‗a‘ is the intercept and b1,

b2………..bn are the coefficients of the explanatory variables (such as expenditure on labour,

value of the production, operated land, irrigated land etc.) X1, X2…………………..Xn. Here

ui is the error term.

Model 3: expenditure on tractors

Expenditure on tractors is hypothesized as being influenced by a set of independent variables:

X1, X2…………………..Xn


Y = a + b1X1 + b2X2 +………………….+ bnXn + ei

Where Y is the dependent variable i.e. expenditure on tractors, ‗a‘ is the intercept and b1,

b2………..bn are the coefficients of the explanatory variables (such as expenditure on labour,

value of the production, operated land, irrigated land etc.) X1, X2…………………..Xn. Here

ei is the error term.

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4. Results and discussions

4.1 Growth of agricultural mechanization (in all crops) in various states of India (1966-

2003)

Various technologies suitable for varied agro-climatic conditions and land holding have been

developed and found wide acceptance. Major research in agricultural engineering have led to

different methodology and equipment for tillage, sowing, interculture, harvesting, threshing,

soil resource conservation etc. About 300 improved agricultural equipment/technologies have

been developed countrywide for various pre & post-harvest operations by human, animal,

mechanical and electrical power. At present in India, tractors are being used for tillage on

22.78% of total area and sowing on 21.30% of total area (Kulakarni et al. 2010). Major

adoption of agricultural machinery in addition to irrigation equipment and tractor were

threshers.

To analyse the growth of mechanization in India, data collected from 19 states by ICRISAT

were used. In this data the growth trend of 4 major machineries is examined namely tractors,

power tillers, diesel pump sets and electric pump sets over a time span of four decades from

1966 to 2003.

4.1.1 Trends in tractors

In Indian agriculture sector, mechanization started with the advent of the green revolution.

Tractorisation has proceeded much faster in states such as Punjab, Haryana, U.P., Rajasthan

and Madhya Pradesh. In Punjab the number of tractors increased from 64310 units in 1977 to

324370 units in 1992 to 470740 units in 2007 while in Uttar Pradesh tractors increased from

458060 units in 1997 to 684620 units in 2003 to 733200 units in 2007 (Table 1.1). In 1992

there were about 44 tractors per thousand hectares in Haryana and 78 tractors per thousand

hectares in Punjab (Table 1.3). Though in Bihar the number of tractors increased from 13980

units in 1992 to 81380 units in 2003 (Table 1.1) which is significant increase but is far less

when compared to other northern states.

On the other hand in the southern states of the country like Andhra Pradesh, Tamil Nadu,

Karnataka and Kerala tractors were not much used. In Andhra Pradesh tractors were around

56860 units in 1992 which increased to 90350 units in 1999 but then declined to 86860 units

in 2003 (Table 1.2) while in Rajasthan these increased from 145770 units in 1992 to 218260

units 1997 to 312670 units in 2003 (Table 1.1 & 1.2) which are high as compared to Andhra

Pradesh.

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Table 1.1: Tractors used in various states of India

Units = ‗000 numbers

Year

Tractors in

Uttar

Pradesh

Tractors

in Punjab

Tractors in

Madhya

Pradesh

Tractors

in

Haryana

Tractors

in

Rajasthan

Tractors

in Bihar

1972 27.65 42.39 5.02 18.46 11.65 5.59

1977 142.28 64.31 16.43 24.768 10.57

1982 230.09 23.47 61.41 54.73 10.73

1987 345.82 45.54 102.84 86.93

1992 458.06 324.37 90.05 156.22 145.77 13.98

1997 684.62 298.84 175.31 262.13 218.26

2003 733.2 470.74 234.61 193.75 312.67 81.38

Source: ICRISAT database

Table 1.2: Mechanization in Andhra Pradesh

Units =‘000 numbers

Year Diesel Pump Sets

Electrical

Pump Sets Tractors Power tillers

1966 46.74 41.85 2.92

1972 116.56 145.41 6.36 0.29

1977 205 296.37 12.62

1982 250.68 437.6 21.47 6.18

1987 216.19 663.89 32.55 4.7

1992 165.71 934.01 56.86 10.47

1999 138.84 1115.44 90.35 9.464

2003 142.16 1370.54 86.86 9.8



Unit: ‗000 number

Graph 1.1: Machinery used in Andhra Pradesh

0

200

400

600

800

1000

1200

1400

1600

1800

1966 1972 1977 1982 1987 1992 1999 2003

Power tillers

Tractors

Electrical Pump Sets

Diesel Pump Sets

15

Table 1.3: Use of various machineries per 1000 hectares (based on NCA) in 1992

Unit: numbers

States TRACTORS

POWER

TILLERS

ELECTRIC PUMP

SET

DIESEL PUMP

SET

Andhra Pradesh 5.43 1.00 89.24 15.83

Assam 0.23 0.49 0.47 4.65

Bihar 2.04 4.08 44.67 53.28

Gujarat 6.48 0.34 21.47 41.60

Haryana 44.74 14.56 14.56 54.75

Himachal

Pradesh 4.51 2.60 2.17 2.25

Karnataka 3.74 1.64 46.03 11.56

Kerala 0.88 0.73

Madhya

Pradesh 4.61 1.54 36.55 10.72

Maharashtra 1.45 0.07 23.06 2.50

Orissa 0.58 0.16 1.92 3.78

Punjab 78.46

Rajasthan 8.61 0.68 21.91 30.34

Tamil Nadu 0.03 2.10 155.79 51.66

Uttar Pradesh 20.04 22.06 110.13



Unit: number

Graph 1.2: Total mechanization in different states in 1992

0

50

100

150

200

250

Mac

hin

ery

use

d p

er

10

00

he

ctar

es

States

16

4.1.2 Trends in electric and diesel pump sets

While green revolution brought mechanization in various states, there were certain states like

Himachal Pradesh, Assam and Orissa that witnessed poor growth rates in agricultural

machinery. In Himachal Pradesh the number of diesel pump sets (Diesel PS) and electric

pump sets (Electric PS) are very less. Though in the recent past there has been some

significant improvement in the usage of electric PS which increased from 1240 units to 7290

units between 1992 and 2003 (Table 1.4). Between 1966 and 1982 the diesel PS were used

more than electric PS such as in Himachal Pradesh around 1180 units of diesel PS were

employed while only 590 units of electric PS were used in 1982 (Table 1.4). However over a

period of time the number of electric PS increased and the demand of diesel PS declined as

the government started giving subsidies on rural electricity. There is great shortage of diesel

in the country as well, thus, to use such extensive oil based farm machinery is not desirable.

Also electric PS has lower cost and higher energy-use efficiency. This trend was also visible

in states like Gujarat and Punjab. In 1977 there were 422050 units of diesel PS and only

79414 units of electric PS in Gujarat while in 2003 there were 436690 units and 468320 units

of diesel PS and electric PS respectively (Table 1.5).

It has been observed that while tractors are more popular in the northern states of the country,

electric pump sets are more employed in southern states of Andhra Pradesh, Tamil Nadu,

Karnataka and Kerala. In Andhra Pradesh electric pump sets have increased from 74440 units

in 1982 to 396950 units in 2003 while diesel pump sets have increased from 26930 units to

29550 units between 1982 and 2003 (Table 1.2). The usage of electric pump sets is much

higher in the state as compared to diesel pump sets, tractors and power tillers. Around 90

electric pump sets per thousand hectares were used in 1992 (Table 1.3) which increased to

134 per thousand hectares by 2003 (Table 1.6). In Tamil Nadu a similar trend was visible; in

fact among all the Indian states Tamil Nadu depicts the highest use of Machinery since 1972

(graph 1.1 & 1.2 ). In Tamil Nadu tractors and power tillers are still not much used but

electric pump sets and even diesel pump sets are employed in large numbers.

Table 1.4: Various machineries used in Himachal Pradesh


Mechanization in Himachal Pradesh

year Diesel Pump Sets Electrical Pump sets Power tillers Tractors

1972 0.26 0.34 0.05 0.28

1982 1.18 0.59 0.36 0.93

1987 1.35 0.61 0.75 1.31

1992 1.29 1.24 1.49 2.58

2003 3.62 7.29 2.7 7.05


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Table 1.5: Various machineries used in Gujarat


Mechanization in Gujarat

Year Diesel pump sets Electrical pump sets

1966 112.428 14.729

1972 371.12 48.38

1977 422.05 79.414

1982 392.01 172.26

1987 471.43 219.08

1992 400.95 206.92

2003 436.69 468.32


Various machineries used in Punjab

Units: ‗000 numbers


While electric pump sets are popular in southern states, there are still many states in the north

that prefer to use diesel pump sets such as Uttar Pradesh, Rajasthan and Gujarat. Uttar

Pradesh one of the largest agricultural state continues to use diesel pump sets. These have

increased from 78 units per thousand hectares in 1987 (Table 1.7) to 110 units per thousand

hectares in 1992 (Table 1.3) to 167 units per thousand hectares in 2003 (Table 1.6), while

electric pump sets have increased from 22 units per thousand hectares in 1992 (Table 1.3) to

29 units per thousand hectares in 2003 (Table 1.6). However a very different trend was

visible in Punjab. Between 1972 and 1977 diesel pump sets were largely used in Punjab but

by 2003 these declined from 339110 units in 1977 to 253350 units while electric pump sets

in the state increased from 128710 units in 1977 to 468770 units in 2003 (Table 1.5). This

indicates that over time Punjab will depend more on electric and not on diesel pump sets.

4.1.3 Trends in power tillers

Power tiller is one machinery which has not been adopted by many states on a large scale. In

states like Andhra Pradesh, Maharashtra, Punjab, Orissa and Rajasthan the number of power

tillers have increased but at very slow rate as compared to other agricultural machineries.

Power tillers were first introduced in the 60‘s but could not gain popularity like tractors due

to its limitation in the field and on the road. Between 1966 and 1972 hardly one power tiller

was used per thousand hectares. It was only in 1987 that this machinery found its way in the

fields of the farmers. Haryana was the largest user of power tillers, about 13 power tillers

were used per thousand hectares in 1987 which increased to 15 units per thousand hectares in

1992 but then declined to 7 units per thousand hectares in 2003 (Table1.3, 1.6,1.7, 1.8).

Mechanization in Punjab

Year Diesel pump sets Electrical pump sets

1972 249.71 79.08

1977 339.11 128.71

2003 253.35 468.77

18

Table 1.6: Use of machineries per 1000 hectare (based on NCA) in 2003

Unit: numbers

States TRACTORS

POWER

TILLERS

ELECTRIC PUMP

SETS

DIESEL PUMP

SETS

Andhra

Pradesh 8.50 0.96 134.18 13.92

Assam 0.55 4.80 0.03 0.21

Bihar 15.20 1.41 10.30 111.19

Chhattisgarh 4.72 0.12 19.68 8.98

Gujarat 14.98 1.10 47.54 44.33

Haryana 54.82 7.42 77.07 78.07

Himachal

Pradesh 13.04 5.00 13.49 6.70

Jharkhand 3.68 0.64 13.22 101.38

Karnataka 6.22

70.69 11.26

Kerala 0.98 0.81 181.26 13.49

Madhya

Pradesh 15.44 0.82 101.50 28.72

Maharashtra 5.96 0.58 55.46 6.90

Orissa 2.84 0.73 6.05 12.82

Punjab 8.40 8.40 111.59 60.31

Rajasthan 17.98 1.06 37.67 48.55

Tamil Nadu 10.63 2.18 167.77 50.56

Uttar Pradesh 40.58 3.63 29.11 167.54

Uttarakhand 16.62 2.81 11.11 39.80

West Bengal 3.38 3.31 13.67 104.70


Table 1.7: Use of machineries per 1000 hectares (based on NCA) in 1987

Unit: numbers

States TRACTORS

POWER

TILLERS

ELECTRIC PUMP

SETS

DIESEL PUMP

SETS

Andhra

Pradesh 3.11 0.45 63.39 20.64

Gujarat 6.57 0.26 30.07 64.71

Haryana 31.81 13.35 66.16 63.33

Himachal

Pradesh 0.24 1.30 1.06 2.34

Karnataka 3.13 1.41 37.61 9.94

Kerala 0.86 0.78 80.03 17.77

Madhya

Pradesh 2.36 0.54 23.57 9.15

Maharashtra 1.71 0.70 33.06 7.14

Orissa 0.34 0.20 4.16 2.54

Rajasthan 7.35 0.79 21.63 31.38

Tamil Nadu 4.52 1.18 150.04 45.19

Uttar Pradesh 7.73 0.99 21.24 78.05


19

Table 1.8: Use of machineries per 1000 hectares (based on NCA) in 1966

Unit: numbers

States TRACTORS

POWER

TILLERS

ELECTRIC PUMP

SETS

DIESEL PUMP

SETS

Andhra Pradesh 0.26

3.69 4.12

Assam 0.09 0.02 0.01 0.16

Bihar 0.29 0.05 0.92 0.50

Gujarat 0.33

1.51 11.54

Karnataka 1.25

2.70 2.46

Maharashtra 0.18

2.28 8.06

Orissa 0.11 0.11 0.03 0.12

Rajasthan 0.29 0.03 0.34 0.49

Tamil Nadu 0.60

34.26 7.04

West Bengal 0.28

0.11 0.76



Unit: number

Graph 1.3: Total mechanization in different states in 2003 and 1987

0.00

50.00

100.00

150.00

200.00

250.00

300.00

Mac

hin

ery

use

d p

er

10

00

he

ctar

es

States

050

100150200250

Mac

hin

ery

use

d p

er

10

00

h

ect

are

s

States

20

4.1.4 Trends in Andhra Pradesh

Andhra Pradesh despite being in the semi-arid region has excelled in the agricultural sector

with the extensive use of various agricultural machineries especially electric pump sets. Yet

within the state, district wise intensity of electric pump sets is highly variable. In districts like

Karimnagar, Warangal, Nalgonda and Chittor electric pump sets are used in large quantities

as compared to other districts. In Karimnagar about 163740 units of electric pump sets were

employed while in Warangal about 141520 units of electric PS were used in 2003 (Table

1.9). In the same state there are districts such as Nalagonda, Srikakulam, East Godavari and

Vishakhapatnam where these are hardly used. In Srikakulam only 9420 units of electric pump

sets were employed in 2003 (Table 1.9). The use of diesel pump sets is much lower than

electric pump sets across all the districts. In Mahabubnagar only 19530 units of diesel pump

sets were used while there were 212710 units of electric pump sets in 2003 (Table 1.9). The

demand of tractors and power tillers are less as compared to electric and diesel pump sets. It

is observed that the backward districts of Andhra Pradesh lag behind in usage of all

agricultural machinery.

Table 1.9: Mechanization in Andhra Pradesh

Units: no. of machinery per 1000 hectares

Districts Diesel Pump Sets Electrical Pump Sets Tractors Power tillers

Adilabad 4.55 29.32 1.08 0.09

Anantapur 3.79 67.75 2.97 1.24

Chittoor 4.59 109.69 5 0.38

Cuddapah 4.25 6.73 4.63 0.42

East Godavari 14.06 20.52 4.49 0.07

Guntur 3.06 14.92 2.05 0.13

Hyderabad 5.75 84.2 4.31 0.34

Karimnagar 8.41 163.74 9.06 1.84

Khammam 12.76 33.42 3.88 0.57

Krishna 9.23 15.59 4.74 0.33

Kurnool 6.73 30.72 3.55 1.05

Mahabubnagar 19.53 212.71 8.76 0.87

Medak 0.7 116.9 3.8 0.5

Nalgonda 2.28 137.28 7.88 0.22

Nellore 9.96 66.72 6.85 0.38

Nizamabad 0.35 77.39 2.27 0.28

Srikakulam 8.01 9.42 1.37 0.1

Visakhapatnam 5.3 10.73 1.02 0.18

Warangal 11.46 141.52 3.24 0.44

West Godavari 7.39 21.27 5.91 0.37


21

4.2 Analysis of chickpea production in Andhra Pradesh

Chickpea crop are generally produced in the temperate environment but with the advent of

hybrid variety which were short duration varieties, began to be produced in the tropical or

semi-arid regions as well. In Andhra Pradesh there are few districts such as Anantapur,

Kadapa, Kurnool, Prakasan and some parts of Mahabubnagar, Medak and Nizamabad where

chickpea are cultivated. In these regions, both desi and kabuli variety of chickpea are

produced. The major desi varieties are JG-11, JAKI-9218 and Annegiri while KAK-2, Bold

and Vihar of kabuli variety are produced. Among them JG-11 is the most popular variety as

73.72% of the total chickpea produced in Andhra Pradesh is of this variety followed by

12.13% of KAK-2 and 12.05% of Vihar whereas varieties like JAKI-9218, Annegiri and

Bold contribute small share in the total chickpea production (Table 2.1). Kurnool district is

the largest producer of chickpea where varieties such as JG-11, KAK-2 and VIHAR are

cultivated. About 527.697 tonnes of chickpea are produced in this district (Table 2.3), out of

which 87.98% are of JG-11 variety, 11.21% of KAK-2 and 0.81% of VIHAR variety (Table

2.4). Kurnool contributes approximately 38.94% of the total chickpea production in the state

(Table 2.2). Kadapa is the second largest producer of chickpea after Kurnool, producing

about 388.1 tonnes (Table 2.2) or 28.64% of the total chickpea production in Andhra Pradesh

followed by Prakasam and Anantapur at 18.13% and 10.56% respectively (Table 2.2).

Table 2.1: Production of various varieties of chickpea in Andhra Pradesh

Chickpea Production in Kg Production in tonnes % share

ANNEGIRI 22700 22.7 1.68

BOLD 1400 1.4 0.10

JAKI-9218 4340 4.34 0.32

JG-11 999106 999.106 73.72

KAK-2 164392 164.392 12.13

VIHAR 163250 163.25 12.05

Total 1355188 1355.188 100.00


Table 2.2: Production of chickpea in different districts of Andhra Pradesh

Districts Production in Kg Production in tonnes % share

Anantapur 143077 143.077 10.56

Kadapa 388100 388.1 28.64

Kurnool 527697 527.697 38.94

Mahabubnagar 3230 3.23 0.24

Medak 20600 20.6 1.52

Nizamabad 26850 26.85 1.98

Prakasam 245634 245.634 18.13

Total 1355188 1355.188 100.00


22

Table 2.3: Production of different varieties of chickpea in different districts of Andhra

Pradesh

Units: kg

Varieties Anantapur Kadapa Kurnool Mahabubnagar Medak Nizamabad Prakasam

ANNEGIRI 0 0 0 0 10900 11800 0

BOLD 0 0 0 0 0 0 1400

JAKI-9218 4340 0 0 0 0 0 0

JG-11 138737 271645 464277 3230 9700 15050 96467

KAK-2 0 12375 4250 0 0 0 147767

VIHAR 0 104080 59170 0 0 0 0

Total 143077 388100 527697 3230 20600 26850 245634


Table 2.4: Percentage share of production of various varieties of chickpea in different

districts of Andhra Pradesh

Districts Anantapur Kadapa Kurnool Mahabubnagar Medak Nizamabad Prakasam

ANNEGIRI 52.91% 43.95%

BOLD 0.570%

JAKI-9218 3.03%

JG-11 96.97% 69.99% 87.98% 100% 47.09% 56.05% 39.27%

KAK-2 3.19% 0.81% 60.16%

VIHAR 26.82% 11.21%

Total 100 100 100 100 100 100 100



Unit: Kg

Graph 2.1: Production of different varieties of chickpea in Andhra Pradesh

0

200000

400000

600000

800000

1000000

1200000

ANNEGIRI BOLD JAKI-9218 JG-11 KAK-2 VIHAR

Pro

du

ctio

n o

f ch

ickp

ea

Varities of chickpea

Prakasam

Nizamabad

Medak

Mahabubnagar

Kurnool

Kadapa

Anantapur

23

4.3 Analysis of labour force and bullocks used for chickpea cultivation in Andhra

Pradesh

In Andhra Pradesh, chickpea production continues to be labour intensive in nature mainly

because of the low yield of the crop, low stature of the plant, uneven ripening etc.

Tradionally, the crop has been harvested manually by pulling the stems to avoid excessive

losses. This has its own disadvantages as the nodules of nitrogen-fixing bacteria are lost due

to which the quality of the plant deteriorates and the labour costs are increased compared to

other harvesting system (Golpira et al 1999).

Apart from bullocks and human labour, tractors, threshers, sprayers and some other

machinery like diesel and electric pump sets (used for irrigation purpose) are employed for

chickpea production in Andhra Pradesh. To examine the distribution of various machinery

and labour used in chickpea production across different districts of Andhra Pradesh, we use

the primary data collected by ICRISAT. The data consists of 9700 plots spread across 4

major chickpea producing district of Andhra Pradesh namely Anantapur, Kurnool, Kadapa

and Prakasam. In this bullocks and labours (both male and female) are measured in days

while tractors, sprayers, threshers and other machinery (includes diesel & electric pump sets)

are measured in hours i.e. the number of hours for which they are used.

In chickpea production labour are generally used for harvesting, weeding and threshing

(Table 3.1) while bullocks are employed for land preparation, interculture and in some cases

for transportation too (Table 3.3) like bullocks are still used in Prakasam for land preparation

whereas in Kadapa these are used for interculture, land preparation, planting & sowing,

threshing & cleaning and compost and sheep penning (Table 3.3). Also female workers are

used more for sheep penning, tank silt application and fertilizer application while more male

workers are used for harvesting (Table 3.1). In Prakasam labours are used in large number for

chickpea cultivation followed by Anantapur, Kurnool and Kadapa (Table 3.1 & graph 3.1).

While bullocks are used more in Kurnool followed by Anantapur, Prakasam and Kadapa

(Table 3.3).


Unit: days per ha

Graph 3.1: Labour used in chickpea production in different districts of Andhra Pradesh

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

45.00

Anantapur Kadapa Kurnool Prakasam

24

Table 3.1: Labour used for various operations of chickpea cultivation in Andhra

Pradesh

Unit: no. of days per ha

Farm Operations

Female

labours

Male

labours

Total

labour

Compost/Sheep penning/Tank silt appl. 1.52 2.21 2.09

Fertilizer application 0.79 0.91 0.87

Harvesting 15.86 2.75 9.43

Interculture 4.38 2.30 2.32

Irrigation 0.00 3.62 3.62

Land preparation 0.31 1.47 1.46

Marketing inc. transport and storage 0.36 1.02 1.02

Micronutrient application 0.00 0.33 0.33

Plant protection 1.26 1.40 1.35

Planting and sowing 1.38 1.00 1.10

Seed treatment 0.44 0.37 0.38

Threshing and cleaning 2.86 4.89 3.94

Weedin/Weeding application 14.01 0.98 8.57

Total 43.17 23.25 36.47


Table 3.2: Labour used for chickpea cultivation in different districts of Andhra Pradesh

Unit: no. of days per ha

Farm Operations Anantapur Kadapa Kurnool Prakasam

Compost/Sheep penning/Tank silt appl. 1.76 1.78 2.23 2.57

Fertilizer application 0.65 0.68 0.91 1.38

Harvesting 8.05 9.02 8.30 15.11

Interculture 2.25 1.89 2.58 0.00

Irrigation 4.49 0.00 3.39 0.00

Land preparation 1.96 1.00 1.47 1.16

Marketing inc. transport and storage 0.89 0.93 1.15 1.08

Micronutrient application 0.00 0.00 0.00 0.33

Plant protection 1.30 1.15 1.45 1.37

Planting and sowing 1.26 0.70 1.17 0.99

Seed treatment 0.35 0.43 0.37 0.40

Threshing and cleaning 3.18 3.45 3.50 6.74

Weeding/Weeding application 8.66 7.94 8.18 10.48

Grand Total 34.80 28.97 34.71 41.60


25

Table 3.3: Bullock used for chickpea production in different districts of Andhra

Pradesh Units: no. of days per ha

Various activities done by bullocks Anantapur Kadapa Kurnool Prakasam

Compost/Sheep penning/Tank silt appl. 1.48 1.25 1.00 0.00

Interculture 1.41 1.07 1.29 0.00






Total 11.10 6.90 11.77 9.96


4.4 Analysis of various machineries used in chickpea production in Andhra Pradesh

Agricultural machineries used in chickpea production are namely tractors, threshers, sprayers

and some other machinery (which consists of diesel and electrical pump sets which are used

for irrigation purposes) that are used in Andhra Pradesh. Among these sprayers and electric &

diesel pump sets are used heavily by the chickpea producers. Anantapur and Kurnool are the

two districts where agricultural machineries are used in large numbers for the cultivation of

chickpea. Tractors are mainly used for land preparation, planting and sowing, plant protection

and marketing & transportation in Kurnool, Kadapa and Prakasam. Tractors are also used for

harvesting in Kadapa (Table 4.1). Threshers are used for threshing and cleaning while

sprayers are used for plant protection and in Anantapur these are also used for weeding and

weeding application.

Table 4.1: Uses of tractors in different districts of Andhra Pradesh

Units: no. of hrs per ha

Various activities done by Tractors Anantapur Kadapa Kurnool Prakasam

Compost/Sheep penning/Tank silt

appl. 2.03 1.98 2.05 3.44

Harvesting 0.00 1.00 0.00 0.00






Weeding and weeding application 0.00 0.00 0.00 2.19

Total 12.95 16.62 14.12 17.84


26

In Andhra Pradesh, Anantapur is the district where agricultural machineries are used

extensively in chickpea production while Prakasam lags behind in this context. In Anantapur

sprayers and other machinery (diesel and electric pump sets) are mainly used for about 10.76

and 18.16 hrs. Per ha respectively (Table 4.2). Whereas in Prakasam sprayers are used only

for 5.52 hrs per ha (Table 4.2) which is very low as compared to other districts. Among the

machineries sprayers are used extensively while threshers are used to a lesser extent. In fact

the sprayers are used for 36.48 hrs per ha, while threshers are used for only 8.35 hrs per

hectare in the four districts together (Table 4.2).

Table 4.2: Machinery used in chickpea production in various districts of Andhra

Pradesh

Units: no. of hrs per ha

Districts Tractors Threshers Sprayers Other

Machinery

Anantapur 2.99 1.62 10.76 18.16

Kadapa 3.17 1.78 11.34 0.00

Kurnool 2.96 1.59 8.86 3.59

Prakasam 3.45 3.37 5.52 0.00

Total 12.57 8.35 36.48 21.75



Unit: hrs per ha

Graph 3.2: Machinery used in different districts of Andhra Pradesh

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

Anantapur Kadapa

Kurnool Prakasam

Other Machinery

Sprayers

Threshers

Tractors

27

5. Regression analysis of the household data

The study area comprises of the 4 districts in Andhra Pradesh namely Anantapur, Kadapa,

Kurnool and Prakasam where chickpea production is carried out in large quantities. Efforts

have been made to examine what are the factors that affect the agricultural machinery usage

in these districts of Andhra Pradesh. Earlier studies have been conducted to show how factors

like productivity, labour force, wages of agricultural labour, net sown area etc. influence the

farmers in using machineries for agricultural production (Singh & Jain et al. 1981).

Tractorization was found to be positively correlated with variables such as percentage of

double cropped area to percentage of net sown area, percentage of area irrigated to the net

sown area and wages of agricultural labours (Singh et al.1999). The farm mechanization is

mainly dependent upon the size of operational land holdings, land topography, availability of

credit facilities and per hectare profitability which in turn is affected by per hectare yield,

cropping intensity, market prices etc.

Investing in agricultural mechanization requires analysis of expenditure on various inputs

required in production of crops such as labour force, fertilizers and HYV seeds as well as

availability of credit facilities to acquire machineries and the quantity of draft animal and

irrigated land available with the farmer. In Andhra Pradesh chickpea production is mainly

labour intensive, however the use of machineries have been increasing over time. Based on

the conceptual review, a model was developed to test the hypothesis. The study is based on

household and plot-level data collected by ICRISAT covering four major districts

(Anantapur, Kadapa, Kurnool, Prakasam) of Andhra Pradesh where chickpea is cultivated.

The primary data collected for this analysis mainly comprises of 269 households and 9700

plots belonging to the four districts mentioned above.

5.1 Determination of dependent and independent variables

Factors associated with agricultural mechanization were analysed using stepwise multivariate

regression analysis and descriptive statistics. As mentioned above, farmers in the study area

are growing chickpea for household consumption as well as for sale. Mechanization of

agriculture helps to increase cropping intensity and crop yield by substituting labour which is

becoming increasingly scarce in rural areas due to rural-urban migration. By providing basic

inputs such as irrigation water the prospects of harnessing the benefits of mechanisation

which helps to reduce the cost of production are enhanced in terms of increasing the overall

efficiency of the farm operations and thus would help the smallholder farmers to obtain

higher farm income. However, mechanization is a complex factor that comprises the use of

all kinds of machines used for farming. In this analysis machines used for chickpea

production that are taken into consideration includes tractors, threshers, sprayers and other

machineries (including diesel & electrical pump sets meant for irrigation purposes). The data

on these machineries is collected on the basis of plot area rather than on the basis of

household and these are measured in terms of hours i.e. for how many hours these

machineries are used. However while analysing mechanization we use expenditure on

machinery as the dependent variable. Here mechanization includes expenditure on tractors,

threshers, sprayers and other machineries.

28

Several variables were considered for inclusion in the regression model related to agricultural

mechanization. Following careful consideration of all variables, eight independent variables

were considered for the regression analysis. Each of the independent variable was

individually correlated with the dependent variable (expenditure on machineries) so as to

identify the significant variables. All the eight variables were found to be significantly

correlated with mechanization and therefore all of them were included in the regression

model. While variables such as caste of the household, sex of the head of the household and

variety of chickpea cultivated are dummy variables. Among the eight variables operated land,

expenditure labours, total assets, irrigated land, market value of the total production are

highly correlated with the regression analysis in view of their role in facilitating

mechanization in chickpea production. The production of mixed variety of chickpea and large

landholdings enable the farmers to adopt new machineries. Also getting high value for their

produce encourages them to spend more on machineries.

Often mechanization of the agricultural operations in developing economies does not lead to

labour displacement such as tractors are used in operations where labour displacement is not

the primary effect. Thus, as long as wage rates remain low there is little reason to expect

tractors to gain comparative advantage in labour intensive operations.

Correlation between explanatory variables

Total

value of

production Credit

Expenditure

on seeds

Expenditure

on

fertilizers

Expenditure

on labours

Operated

land

Total value of production 1

Credit 0.29 1

Expenditure on seeds 0.35 0.57 1

Expenditure on fertilizers 0.39 0.56 0.82 1

Expenditure on labours 0.54 0.55 0.84 0.79 1

Operated land 0.22 0.58 0.84 0.74 0.75 1

Owned land 0.14 0.59 0.66 0.61 0.63 0.84

Total assets(Rs.) 0.28 0.58 0.61 0.70 0.58 0.69

Total production (tonnes) 0.27 0.24 0.25 0.29 0.35 0.35

Value of production per

ha 0.09 0.54 0.69 0.62 0.61 0.79

Irrigated land 0.19 0.23 0.11 0.18 0.20 0.35

Draft animal -0.21 -0.01 0.04 -0.01 -0.02 0.17

As shown in the above table, expenditure on labour, seeds and fertilizers are highly correlated

to each other. Operated and owned land is also highly correlated. Two variables are said to be

highly related if correlation is equal or close to 1. As a result of which regression tend to

suffer from multicollinerity or specification bias. Thus, to avoid this situation these variables

(owned land, expenditure on fertilizers and seeds) are excluded from the regression model.

29

Descriptive statistics for important variables used in the analysis

Description of variables Mean

Std

deviation Min Max

Household assets

Total assets value (Rs.) 5361205.4 5936651.396 131000 36699000

Consumer durables (RS 1000/ha) 57.73 96.99 2.15 1397.50

Farm buildings (in sq. yards) 225.13 181.91 0.00 2000.00

Farm equipments (in numbers) 2.35 21.97 0.00 301.00

Livestock (in numbers) 3.40 7.09 0.00 100.00

land (hectares) 4.85 4.74 0.00 34.00

Household characteristics

Education of the head of the household (in

years) 6.22 5.04 0.00 17.00

Education of the household for all members (in

years) 28.41 17.64 0.00 103.00

Age of the head of the household (in years) 48.36 11.90 23.00 78.00

Sex of the head of the household (1=Male,

2=Female) 1.01 0.11 1.00 2.00

Family size (in number of members) 4.93 2.06 2 11

Plot and farm characteristics (hectares)

Farm size in acres (owned area) 4.84 4.75 0 34

Total operated area 6.07 4.93 0.4 32.8

Irrigated land 0.60 1.56 0 12

Dry land 4.20 4.23 0 28

Plot- level input used (Rs 1000/ha)

Expenditure on labours (male & female) 0.57 0.80 0.00 6.00

Expenditure on labours (male) 0.35 0.40 0.00 4.00

Expenditure on labours (female) 1.02 1.14 0.01 6.00

Expenditure on machineries 1.37 1.35 0.03 11.25

Expenditure on fertilizers 3.07 2.00 0.04 16.25

Expenditure on seeds 6.11 1.82 0.45 18.00

Expenditure on bullocks 0.79 0.91 0.03 7.50

Household-level returns

Total production (tonnes) 10.96 20.80 0 202.4

Production per acre (tonnes) 0.77 0.76 0 3.310198413

Production of by-product (tonnes) 29.17 56.07 0 450

Value of production (Rs.1000/ha) 31.75 34.55 0.00 161.41

Crop patterns and crop choice

Chickpea variety: 0= Desi, 1=Kabuli, 2=Mixed

variety 0.26 0.60 0 2

Social capital indicators

Years of farming 23.12 11.33 2.00 60.00

Caste of the household; 1=BC, 2=OC, 3=SC,

4=ST 1.64 0.63 1 4

30

5.2 Model Specification


In this regression model, the joint regression analysis is significant as the F- statistics

(107.06) is greater than the F-critical value at 0.05 level of significance. Thus, we will accept

the hypothesis that is all the explanatory variables are having a significant impact on the

dependent variable.

R^2 = 0.87 which means that 87% of variation in the dependent variable i.e. expenditure on

machineries is explained by the explanatory variables as mentioned in Table A.

Table A

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.93

R Square 0.87

Adjusted R Square 0.86

Standard Error 11514.89

Observations 269.00

F – value 107.0642

Significance F 2E-102

Coefficients

Standard

Error t Stat P-value

Intercept 1448.81 7736.22 0.19 0.85

Expenditure on seeds (Rs.) 0.35 0.07 4.99* 0.00

Expenditure on fertilizers (Rs.) -0.06 0.07 -0.88 0.38

Expenditure on labours (Rs.) 0.68 0.05 14.06* 0.00

Operated land (ha) -77.93 402.06 -0.19 0.85

Owned land (ha) -3687.39 1507.54 -2.45** 0.02

Irrigated land (ha) 2336.57 1696.07 1.38 0.17

Credit (Rs.) 0.05 0.07 0.69 0.49

Draft animal (No.) -2910.46 837.96 -3.47* 0.00

Total assets(Rs.) 0.0004 0.00 2.53** 0.01

Total production (tonnes) 90.21 41.17 2.19** 0.03

Value of production per ha 3197.82 1574.29 2.03** 0.04

Experience (Years) -47.97 67.16 -0.71 0.48

Education of the head of the household 101.37 160.60 0.63 0.53

Caste of the household -313.69 1136.00 -0.28 0.78

Sex of the head of the household 897.91 6822.41 0.13 0.90

Family size (No.) -251.96 381.60 -0.66 0.51

Note: *, ** & *** indicate level of significance at 1%, 5% & 10% respectively.

31

As shown in Table A, expenditure on machineries has a positive relation with expenditure on

seeds, labours, irrigated land, education of the head of household, credit availability, total

production and value of the production per hectare. On the other hand the dependent variable

has a negative relation with expenditure on fertilizers, owned and operated land, draft animals

and family size.

However in most of the studies it was observed that farmers tend to use more machineries on

larger operated land i.e. there is usually a positive relation between the two variables. But this

is not the case in our model thus leading to a contradiction. This is mainly due to

multicollinearity which arises due to high linear relation between operated and owned land as

well as between expenditure on seeds and fertilizers. To solve this we exclude these linearly

related explanatory variables from the above model. Also we undertook regressions to test the

significance of each individual explanatory variables. By doing this we found that experience,

gender of the head of household and caste of the household included in Table A are

individually insignificant that is, these variables have negligible impact on the dependent

variable.

H0: β0=0 Experience is insignificant

H1: β0≠0 Experience is significant

By doing an individual regression analysis between expenditure on machineries and

experience of the farmers in agriculture, we found out that the t- statistics (1.45) is less than

|2| which means we do not reject the null hypothesis and that experience of the farmer has no

significant impact on mechanization.

Similarly the other two variables that proved to be insignificant were gender of the head of

household and the caste to which the household belongs.

In case:

Of gender of the head of household: t-statistics (-0.98) ≤ |2|

Of caste to which the household belongs: t-statistics (1.53) ≤ |2|


This regression model is different from the first model in the sense that the insignificant

variables (experience of the farmer, caste of the household and sex of the head of household)

as well as linearly related explanatory variables proved in the above analysis are dropped

from this model.The relation between dependent and explanatory variables is significant at

0.05 level of significance as the F- value (205.83) is greater than F- critical. Variety of

chickpea is taken as a dummy variable where 0= Desi, 1= Kabuli, 2=Mixed variety. Here

R^2= 0.86 which indicates the high explanatory power of the model as a whole. The

expenditure on machineries is positively related to both expenditure on labour and operated

land (Table B).

32

Table B

SUMMARY OUTPUT


Multiple R 0.93

R Square 0.86



Observations 269.00

F – value 205.83

Significance F 8E-108

Coefficients Standard Error t Stat P-value

Intercept 1649.68 1401.77 -1.18 0.24

Total value of production 0.05 0.03 1.71*** 0.09

Credit 0.01 0.07 0.20 0.85

Expenditure on labours 0.71 0.05 15.43* 0.00

Operated land 977.67 288.52 3.39* 0.00

Total assets(Rs.) 0.00043 0.00 2.63* 0.01

Irrigated land -2079.69 511.93 -4.06* 0.00

Draft animal -3479.93 820.25 -4.24* 0.00

variety of chickpea 5344.40 1493.12 3.58* 0.00

Note: *, ** & *** indicate level of significance at 1%, 5% & 10% respectively.

Regression Equation:

Expenditure on machineries = 1649.68 + 0.71(expenditure on labour) + 977.67 (operated

land) + 0.05(total value of production) + 0.01(credit availability) + 0.00043(total assets) –

2079.69(irrigated land) – 3479.93(draft animal) + 5344.40(variety of chickpea)

As seen in the above equation there is a negative relation between machineries and draft

animals i.e. if the farmer has large number draft animals he will spend less on machineries.

Thus, if draft animal increases by one unit expenditure on machineries will decline by

3479.93 units. Similarly, irrigated land also has an inverse relation with expenditure on

mechanization. On the other hand explanatory variables like market value of production,

operated land and expenditure on labour have a positive impact on the dependent variables.

In Table B variety of chickpea is taken as a dummy variable where 0= desi, 1= kharif & 2=

mixed variety. Thus if farmers are producing mixed variety then they tend to spend more on

machineries.

33

Model 3: expenditure on tractors

Here the dependent variable is expenditure on tractors and the explanatory variables are same

as used in the above models and the relation between them is significant at 0.05 level of

significance as the F- value (153.20) is greater than F- critical. In this model tractorisation has

a positive relation with expenditure on labour, total production value, operated land and

variety of chickpea grown (0=desi, 1=Kabuli, 2=Mixed) while it has a negative relation with

draft animal and irrigated land (Table C).

Table C

SUMMARY OUTPUT


Multiple R 0.91

R Square 0.82



Observations 269.00

F – value 153.20

Significance F 8.5984E-94

Coefficients Standard Error t Stat P-value

Intercept 492.55 1216.53 0.40 0.685898

Total value of production 0.01 0.03 0.25 0.803265

Expenditure on labours 0.53 0.04 13.20* 8.39E-31

Total assets(Rs.) 0.0003 0.00 2.24** 0.02573

Credit 0.02 0.06 0.33 0.739553

Irrigated land -2138.79 444.28 -4.81* 2.51E-06

Draft animal -3080.42 711.86 -4.33* 2.15E-05

variety of chickpea 5356.55 1295.81 4.13* 4.82E-05

Operated land 862.84 250.39 3.45* 0.000663

Note: *, ** & *** indicate level of significance at 1%, 5% & 10%respectively.

Regression equation:

Expenditure on tractors = 492.55 + 0.01(total value of production) + 0.02( credit)+

0.53(expenditure on labour) + 862.84(operated land) + 0.0003(total assets) –

2138.79(irrigated land) – 3080.42(draft animal) + 5356.55(variety of chickpea grown)

As per the above equation if the draft animal increases by one unit then expenditure on

tractors decline by 3080.42 units keeping all other factors constant while if irrigated land

decreases by one unit then expenditure on tractors will increase by 2138.79 units keeping all

other factors constant. Similarly if the farmer grows desi variety (desi=0) of chickpea (JG-11,

JAKI-9218, Annegiri) then he will prefer to spent nothing on tractors keeping all other

factors constant while if he decides to grow kabuli variety (KAK-2, Bold, Vihar) then the

34

expenditure on tractors increases by 5356.55units (kabuli=1) keeping all other factors

constant. However farmers will spent even more on tractors if they grow both varieties of

chickpea as the expenditure will increase by 10713.10 units (mixed=2) as shown in Table C.

5.3 Analysis of the regression results

Farm mechanization as seen in the three models is influenced by various factors. While some

have positive impact others tend to have a negative relation with agricultural machineries.

Factors such as total production of chickpea and its market value have a direct impact on

mechanization as machineries tend to improve productivity and yield of chickpea. It was also

observed that mechanization of agricultural operations by and large displaced draft animal

labour and not human labour, in fact, labours showed a positive relation with machineries.

The main reason for this is that though there was labour displacement in the area of

ploughing and transport, it was more than compensated by higher employment in other

agricultural operations notably, fertilization, weeding, interculture, pest control, irrigation,

harvesting, threshing etc. Operated land also depicts a direct relation as it is more profitable

to use machineries on large land than on small. Large machines cannot be efficiently operated

on small plots since mechanization creates the potential for positive scale of economies.

Producing kabuli or mixed variety of chickpea also enables the farmer to use more

machineries as they get better returns on these. Irrigated land has a negative relation with

both tractorization and expenditure on machineries. The explanation for this behaviour could

be that if farmers have large irrigated land they don‘t feel the need to spent on machineries as

they expect irrigated land to give a higher yield and productivity.

35

6. Conclusion

Farm mechanization in India was adopted as a means of increasing productivity of land and

labour through timeliness of operations, precision placement and efficient utilization of inputs

and reduction of losses at different stages. There is need to integrate the use of available

human labour and animal power with mechanical sources of power. Selective farm

mechanization results in 7.5%- 40% increase in agricultural productivity. There has been

phenomenal growth in the harvesting and post-harvesting machines. Decreasing farm size has

increased the demand for simple and low cost farm equipment. Variety of technologies

suitable for varied agro-climatic conditions and land holdings have been developed and found

wide acceptance.

The mechanization of chickpea harvesting has been a long term objective of farmers on

fallow field in developing countries. Saving cost and time was the motivation to develop a

mechanized harvesting system for chickpea, which are manually uprooted on fallow fields in

developing countries. Haffar et al. (1991) and Siemens (2006) applied conventional combine

harvesters for chickpeas but losses were high. Chakraverty et al. (2003) reported that the

optimum losses for mechanized chickpea harvesting were 5.5%. The crop has been

traditionally harvested manually by pulling the stems to avoid excessive losses (Bansal &

Sakr, 1992; Konak et al., 2002). Hand-picking laborers collect the chickpea bushes by hand.

The process of pulling the entire plant by hand and uprooting the plant from the soil has

many disadvantages: the nodules of nitrogen-fixing bacteria are lost, the quality of the plant

residue for feeding animals is decreased due to a salty taste and the laborer costs are

increased compared to other harvesting systems. A tractor- pulled harvester with a modified

stripper header was designed and fabricated, in which passive fingers with V-shaped slots

removes chickpea pods from anchored plant and batted reel sweeps the pods across the

platform. Modified stripper harvester can work in unevenness ground where other machines

cannot operate. Stripping mechanization and methodology have potential to become an

effective system for chickpea harvesting by saving cost and time. Stripper headers have

rotating rotor and teeth to detach pods from anchored plant and an adjustable hood to deliver

materials. However the disadvantage of this machinery is that they have excessive losses in

low harvest yield or immature crops. Thus, future research needs to be focussed on reduction

of losses for the commercialization of the stripper harvester.

Mechanisation reduces the use of family labour while increasing the use of hired labour. The

net return per hectare of gross cropped area or net cultivate area are higher for tractorised

farms than the non-tractorised farms as a result of better utilisation of resources.

Mechanisation has helped in the shift from conventional crops to commercial crops.

Government of India expanded rural electrification and the adoption of high yielding variety

(HYV) seeds led to increased number of electric pump sets in many states where traditionally

diesel pump sets were used. Also size of irrigation pumps powered by electric motors has

increased due to lowering of water tables. Implementation of schemes like MNREGA led to

increase in labour wages which in turn gave a boost to mechanisation.

36

Tractor – pulled stripper harvester

In the future high capacity machines will be preferred while animal operated implements will

decrease due to continued reduction in the number of draft animals. Power operated farm

equipment will increase rapidly. However for better usage of mechanisation, investment in

infrastructure is required like development of road, electricity supply, irrigation system,

storage facilities etc. Exploitation of groundwater and water from ponds and reservoirs in

rural areas for irrigation should be the highest priority to increase agricultural productivity.

Minimum support price for chickpea and custom hire possibilities could further enhance the

profitability of acquiring agricultural mechanisation inputs by farmers. The government of

India provides support services for research and development, testing and standards and

human resource development in support of agricultural mechanisation.

However, timely benefits of mechanization or efficient use of seeds, fertilizers are not

reaching majority of farmers in full measures. Poor information and inadequate guidance lead

farmers to make wrong decisions leading to improper investments. Increasing fragmentation

of land is making individual ownership of agricultural machinery progressively more

difficult. There is a need for central and state level institutions to plan and give proper

direction to the development of agricultural mechanization and also undertake farm

machinery testing to help improve the quality of equipment.

37

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Mechanization of chickpea production in Andhra Pradesh: Meso and