BSc Research thesis_2010

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ANALYSIS OF THE IMPACT OF FARM MECHANISATION ON

PRODUCTIVITY. A CASE OF CHIPINGE RESETTLED FARMERS.

BY MAKATE CLIFTON R076606B

A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILMENT OF THE

REQUIREMENTS OF A BSC HONOURS DEGREE IN AGRICULTURE

SURPERVISOR: DR. SIZIBA

AUGUST, 2010

Department of Agricultural economics and extension

Faculty of Agriculture

University of Zimbabwe

P.O.BOX MP167

Mount Pleasant

Harare

ZIMBABWE

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Contents Chapter 1: Overview of the study .......................................................................................... 5

1.1 Introduction ......................................................................................................................... 5 1.2 Background to the problem .............................................................................................. 6

1.3 Problem statement ............................................................................................................ 7 1.4 Research Objectives ......................................................................................................... 8

1.5 Research Questions.......................................................................................................... 8 1.6 Research Hypothesis ........................................................................................................ 8 1.7 Justification of study .......................................................................................................... 9

Chapter 2: Literature Review ................................................................................................ 10

2.1 Introduction ....................................................................................................................... 10

2.2 Background and insights to agricultural mechanisation ............................................ 10 2.3 Overview of farm mechanization in Zimbabwe ........................................................... 11 2.4 Farm mechanization meaning and scope.................................................................... 12

2.5 Why mechanize agriculture in Zimbabwe or the whole of Africa? .......................... 13 2.6 Meaning and measures of agricultural productivity................................................... 14

2.6.1 Labour productivity- ................................................................................................. 14 2.6.2 Multi-factor productivity ........................................................................................... 15

2.7 Farm mechanization and productivity, production, labour and income. (Empirical

evidence) ................................................................................................................................. 15 2.8 Increasing agricultural productivity is central to reducing poverty -Technology’s

Role .......................................................................................................................................... 18 2.9 Other benefits attached to farm mechanization .......................................................... 19 2.10 What influences the adoption of new technology by farmers? .............................. 19

2.10.1 Secure output markets .......................................................................................... 20 2.10.2 Effective input supply systems, including credit ................................................ 20

2.10.3 Supporting infrastructure – particularly irrigation .............................................. 21 2.11 How does new agricultural technology benefit the poor? ....................................... 22

2.11.1 The impact on employment .................................................................................. 22

2.11.2 Food prices ............................................................................................................. 23 2.11.3 Nutrition and food utilization ................................................................................. 24

2.11.4 Access to land and other resources.................................................................... 25 2.11.5 Gender issues......................................................................................................... 25 2.11.6 Sustainability issues .............................................................................................. 25

2.11.7 Biodiversity .............................................................................................................. 26 2.12 But have the poor benefited from new agricultural technology? ............................ 26

Chapter 3: Research Methods .............................................................................................. 28

3.1 Introduction ....................................................................................................................... 28 3.2 Data collection (Secondary data source) ..................................................................... 28

3.3 Study area selection and description ........................................................................... 28 3.4 Conceptual framework .................................................................................................... 29

3.4.1 Introduction................................................................................................................ 29 3.4.2 Illustration of the conceptual framework ............................................................... 29

3.5 Theoretical framework .................................................................................................... 30

3.6 Analytical framework ....................................................................................................... 31 3.7 Regression analysis ........................................................................................................ 31

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3.8 Correlation analysis ......................................................................................................... 33 3.9 Analytical techniques ...................................................................................................... 33

CHAPTER 4: DATA ANALYSIS AND RESULTS ............................................................... 34

4.1 Introduction ....................................................................................................................... 34

4.1.1 Household characteristics ....................................................................................... 34 4.1.2 Gender and marital status of farmers ................................................................... 35 4.1.3 Levels of education and household head occupation ........................................ 35

4.1.4 household sizes ........................................................................................................ 36 4.2 Farm characteristics ........................................................................................................ 37

4.2.1 Farm size ................................................................................................................... 37 4.2.2 livestock ownership .................................................................................................. 37 4.2.3 Agricultural implements ........................................................................................... 38

4.2.4 Sources of income for the households ................................................................. 39 4.2.5 labour sources of households ................................................................................ 39

4.3 Regression and correlation analysis............................................................................. 40 4.3.1 Capital equipment intensity and productivity ....................................................... 40 4.3.2 Insights from the analysis ....................................................................................... 41

4.3.3 Mechanization and labour use on the farm. ......................................................... 41 4.4 Factors hindering effective use of farming implements amongst the resettled

farmers in chipinge district. ................................................................................................... 42 4.4.1 Lack of manpower .................................................................................................... 42 4.4.2 Weaknesses in draught animals ............................................................................ 43

4.4.3 Lack of access to credit facilities ........................................................................... 43 4.4.4 Relief and nature of surface ................................................................................... 43

4.4.5 Lack of effective extension services...................................................................... 43 CHAPTER 5: CONCLUSIONS AND POLICY RECOMMENDATIONS ........................... 44

5.1 Introduction ....................................................................................................................... 44

5.2 Impact of farm mechanization on productivity............................................................. 44 5.3 Impact of farm mechanization on employment of labour .......................................... 44

5.4 Policy recommendations ................................................................................................ 45 Bibliography .............................................................................................................................. 47

List of Figures Figure 4.1 Marital status of households...................................................................................................................................35 Figure 4.2 Correlation between labor use and capital ..........................................................................................................42

List of Tables Table 2.1 Provincial allocations of farming equipment procured by RBZ, 10 January 2007.........................................12 Table 2.2 Comparison of basic measures of partial productivity. ......................................................................................15 Table 2.3: Irrigation in Africa and Asia, 1961/1963 – 1997/1999 .......................................................................................21

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Source FAO 2003 ......................................................................................................................................................................22 Table 3.1Types and sources of change in agricultural output .............................................................................................29 Table 3.2.Research Analytical Framework...............................................................................................................................31 Table 3.3 Independent Variables ...............................................................................................................................................32 Table 4.1 Age distribution of households ................................................................................................................................34 Table 4.2 Educational status of households ............................................................................................................................36 Table 4.3 Land ownership by the farmers ...............................................................................................................................37 Table 4.4 Livestock ownership (means) ...................................................................................................................................37 Table 4.5 Agricultural implements owned..............................................................................................................................38 Table 4.6 Farmers’ sources of labour .......................................................................................................................................39 Table 4.7 Results of the linear regression model ...................................................................................................................40 Table 4.8 Farm mechanization level and yield means ..........................................................................................................41

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Chapter 1: Overview of the study

1.1 Introduction

The Zimbabwean economy, like most other African countries, is primarily agricultural.

Agriculture provides employment to a large portion of the Zimbabwean population, with

most of its people earning their livelihoods from it. The sector has expanded immensely

since Zimbabwe’s attainment of independence in 1980. It has contributed approximately

15 to20 % of the Gross Domestic Product (GDP) and about 40% of the total foreign

currency earnings in Zimbabwe (Rukuni and Muir 1994).

With the growth and commercialisation of the agricultural community in the world,

various agricultural communities have been adopting modern agricultural equipment in

order to maintain competitive advantage in the industry. The main objective for such

capital equipment adoption has been linked to the need for productivity enhancement

through efficiency and timeliness of operations and by reducing the drudgery of the

human beings and draught animals in these communities. This increase in productivity

is believed to translate into increased farm profits for the farmers and overall profitability

of the farming business.

Agricultural mechanisation has therefore grown to be a strategy adopted by almost all

farming communities in the world including Zimbabwe as well. It is believed to increase

land and labour productivity. Machinery however works as complementary input

required for achieving higher land productivity for example, through the introduction of

pump sets, or faster turning around times to achieve higher cropping intensities. The

underlying assumption being that mechanisation alone cannot increase productivity.

Mechanization is also believed to be a necessary component in farming systems

adaptation, cropping practises adaptation as well as influencing the total area of land

farmers can put under crops.. It therefore plays a vital role as a necessary component

for supplying the needed impetus and input towards agricultural development and

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modernization.

According to Adrians (1989) agricultural mechanization embraces the use of tools,

implements, and machines for agricultural land developments, crop production,

harvesting, and preparation for storage and on farm processing. It includes three main

sources; human, animal and mechanical. The manufacture, distribution, repair,

maintenance, management, and utilization of agricultural implements are covered under

this discipline as well. Kline (1969) defines agricultural mechanization as any

mechanical means that can be used in the process of agricultural production.

1.2 Background to the problem

Following the land reform program of the year 2000 there has been a decline in output

of major crops such as maize, wheat and soya beans, just to name a few. Newly

resettled farmers were facing challenges in increasing their land under crop cultivation,

farm productivity and hence their total farm output. This was mainly due to the fact that

the newly resettled farmers could not have the adequate financial resources to acquire

the needed implements and other capital resources to improve their farming operations.

In addition, the balance of payment pressures sustained by the government of

Zimbabwe since early 2000 and the isolation of the country by some players in the

international community lead to unavailability of critical strategic inputs and some

commodities which contributed to low agricultural output and hence moving towards

food insecurity of the nation, This was worsened by the fact that most lines of credit

from financial institutions had dried up and the government had to rely on non-

traditional sources of financing through the Reserve Bank of Zimbabwe (RBZ) to

procure strategic imports (Gono, 2007).The government through Reserve Bank then

acquired a variety of specialized agricultural equipment through the mechanisation

program in a bid to intensify efforts towards promoting the newly resettled farmers and

to complement the land reform’s objectives. The main objective of such an initiative was

to equip the poor resource farmers so that they can raise productivity of their farmland

for the benefit of the nation. The equipment included combine harvesters, tractors, and

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various other farm implements which were delivered to farmers across the country to be

used in production. In a bid to equip indigenous farmers the government launched the

program with a target to built a fleet of at least 50 000 tractors and other equipment in a

program which was expected to run until 2010.The RBZ also embarked on a drive to

create jobs through a massive project to set up institutions throughout the country that

manufacture animal drawn implements.

1.3 Problem statement

Lack of modern farm implements among resettled farmers in communal areas of

Zimbabwe has been a major threat towards optimal crop production, increased

agricultural productivity, intensifying crop production, increased farmer incentives and

adoption of better farming methods. Inadequate tillage implements, boom sprayers,

planters, harvesters, fertilizer spreaders among other farm implements have to some

extent contributed to low agricultural productivity and thus food insecurity in Zimbabwe.

Finance, a critical component in procuring the much needed farming implements has

also been a major challenge to the newly resettled farmers hampering their efforts to

raise productivity levels as well as profitability.

Over the past few years there has been growth in outmigration of the economically

active population in search of greener pastures in neighbouring countries and abroad.

This has caused shortages in labour in the country’s primary secondary and tertiary

industries with agriculture being one of the greatly affected industries. Moreover, the

growth of the demand in agricultural commodities for food and feed has caused

increased pressure on the land as a resource in competing for space for the different

types of crops to grow. This has emerged as a big problem in Zimbabwe since some

crops are being preferred to be grown at the expense of others regardless of their need

to the society. Low productivity amongst the farmers, shortages in labour poor farming

methods and low cropping intensities have however been identified as the major

problems with the agricultural community in Zimbabwe. It is not clear whether dwindling

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levels in agricultural productivity have been due to lack of mechanization or some other

factors as highlighted above. It therefore remains to be examined to ascertain whether

farm mechanization has any impact on farm productivity. Precisely stated, this study

seeks to analyze the impact of farm mechanization focusing on Chipinge resettled

farmers.

1.4 Research Objectives

To determine whether the use of farm equipment by the resettled farmers had an

impact on farm productivity.

To determine the correlations between labour use on a farm and capital

equipment use.

To identify the factors limiting effective use of farm machinery amongst the

resettled farmers in Chipinge district.

1.5 Research Questions

Does the use of farm equipment have a significant contribution towards

increasing farm productivity amongst the resettled farmers in Chipinge district?

Do capital/ mechanization move together with quantities of labour used on a

farm?

What are the factors hindering effective use of farm machinery amongst the


1.6 Research Hypothesis

Productivity is positively related to the use of capital equipment among the


Labour use on a farm is negatively related to the level of mechanization i.e. as

the use of farm machinery increases labour use decreases.

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1.7 Justification of study

The analysis of the impact of farm mechanization on productivity still remains an object

of empirical research. Research has concentrated much on developed countries with

very little of that research trickling down to developing countries. In the Zimbabwean

context, this study will be highly useful as it will yield possible policy recommendations

that can be used by development planners, policy makers, implementers and

beneficiaries participating in development projects. In addition, this study will also

review the actual impacts and contributions of farm mechanization to the livelihoods of

communal rural farmers in Chipinge district and also help similar projects elsewhere in

the country. And more importantly the study will reveal the possible support services in

terms of skills and education required by the different farming communities in order to

maximise benefit from the use of farm machinery for example setting of tractor servicing

and Driver training centres in communal areas.

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Chapter 2: Literature Review

2.1 Introduction

This chapter is a summary of all literature that was reviewed from studies done on the

impacts of farm mechanization on production, productivity, incomes, cropping intensity

labour employment and many other related variables in Zimbabwe and around the

world. Literature reviewed includes the meaning and scope of farm mechanization,

related researches and findings on farm mechanization, the reasons for mechanizing

agriculture in Zimbabwe and or Africa, the need for mechanization and its role in

agricultural development and some of the factors that affect adoption of improved farm

machinery into present farming systems by most communal farmers and other benefits

attached to farm mechanization. This section will also include the meaning and

measures of agricultural productivity, the role of technology in enhancing productivity

and the benefits of technology to the poor, factors influencing adoption of technology by

farmers.

2.2 Background and insights to agricultural mechanisation

In the past agricultural mechanization in developing countries has been much criticized

because it often failed to be effective, and was blamed for exacerbating rural

unemployment and causing other adverse social effects. This was largely the result

from experiences during the 1960s until the early 1980s when large quantities of

tractors were supplied to developing countries either as a gift from donors, or on very

advantageous loan terms. In particular projects which were designed to provide tractor

services through government agencies had a miserable record. These projects proved

not sustainable because of the intrinsic inefficiencies of government-run businesses. An

overvalued foreign exchange rate and low real interest rates made agricultural

machinery artificially cheap as compared with labor and draft animals.

These experiences often combined with a very narrow perception and lack of

knowledge about mechanization, namely the one sided promotion of tractors and other

capital-intensive mechanical power technology, has caused the aid community to

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largely turn its back on mechanization. At the same time there are many examples were

mechanization has been very successful, contributing to increased food production,

productivity and advancement of rural economies. For example, privately owned

shallow tube wells for irrigation in South Asia, axial flow threshers in Southeast Asia,

single-axle tractors in Thailand, and various forms of farm mechanization in many parts

of China.

The introduction of agricultural technology, including mechanization, is a complex

process. The formulation of an Agricultural Mechanization Strategy (AMS) requires

comprehensive knowledge of many aspects of agriculture in its broadest sense. An

AMS will very much depend on country specific characteristics of the economy, its level

of development, and the agriculture sector. This means that the formulation process for

an AMS cannot be prescribed in a simple set of guidelines. Preferably mechanization

technology should be considered in the context of an overall (agriculture) technology

strategy.

2.3 Overview of farm mechanization in Zimbabwe

In Zimbabwe the government through the Reserve bank of Zimbabwe (RBZ) has since

embarked on mechanizing the farming community by importing various farming

implements from China and Japan with the aim of restoring productivity levels and

guarding the nation against adverse effects associated with food insecurity. The

initiative has seen a variety of farming implements being distributed across the country

including tractors, disc harrows, combine harvesters, ploughs, boom sprayers, fertilizer

spreaders, planters, scortchcarts and other electric equipment such as generators.

Deliveries of the equipment has since started and was on provincial basis and

commensurate with the farming activities in particular provinces. Selection criteria of

beneficiaries was based on productivity which was evidenced by transcripts of deliveries

to the Grain Marketing Board (GMB), Cotton marketing companies, tobacco marketing

companies and horticultural farmers. Once farmers were identified the RBZ through the

ministry of Agriculture facilitated the release of the equipment to farmers. To ensure

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optimal use of the rather scarce resource at the moment the production contracts

stipulated that after farmer has completed farming on his or her piece of land, the

equipment was to be availed for hire by neighboring farmers. Deliveries were made as

follows (referring to phase one of the mechanization programme January 2007):

Table 2.1 Provincial allocations of farming equipment procured by RBZ, 10 January 2007

Province Tractors Disc

harrows

Combine

harvesters

Ploughs Boom

sprayers

Fertilizer

spreaders

Planters

Mash east 75 75 4 75 23 19 9

Masvingo 35 35 2 35 23 19 9

Mash west 75 75 5 75 23 19 13

Mat north 35 35 2 35 14 10 9

Mat sout 35 35 3 35 22 19 9

Manicaland 35 35 2 35 21 18 9

Mash centr 75 75 4 75 29 28 13

Midlands 35 35 2 35 22 18 9

Maguta 100 100 10 100 _ _ 20

Total 500 500 34 500 175 150 100

Source: RBZ publications (2007)

Phases two three and four have already been implemented in the same manner as was

the phase one, the different phases are only adding to the numbers of implements in the

different provinces without changes in the types of equipment given to farmers.

2.4 Farm mechanization meaning and scope

Improved farm implements and machinery are used for different farm operations to increase productivity of land and labour through timeliness of operations, efficient use of inputs, and improvement in duality of produce, safety and comfort of farmers and reduction in loss of produce and drudgery of the farmer. It is for increasing production and productivity, comfort and safety, return and

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profitability to farmer by timeliness of operations, saving in labour requirements increase in land productivity and reduction in human drudgery.

2.5 Why mechanize agriculture in Zimbabwe or the whole of Africa?

Most governments as well as individual farmers’ efforts over the past ninety years have

been directed in one way or another at mechanizing agriculture in Africa. These efforts

have in many cases been augmented by the donor community assistance. The main

thrust of these efforts has been aimed at replacing the cutlass and hoe cultivation with

draught animals, small tractors or large tractors. It would seem there are 5 major

reasons for this: first to increase the productivity of labour, second to increase the land

under cultivation, third to improve the quality of farming operations, fourth to reduce the

drudgery and hence make agriculture more attractive, and finally the need for timely

completion of certain key agricultural operations due to the limitations imposed by the

weather. One or all of the above reasons have been used to justify agricultural

mechanization projects and programs on the African continent. These reasons have

been used for both the large scale agriculture, as well as for smallholder and the

peasant subsistence farmers.

The first reason, increasing the productivity of labour is quite obvious, farming carried

out using the hand tool technology with entire reliance on human muscle power is in

most cases quite inefficient. A family using this type of technology can rarely till more

than one hectare especially in the tropics (Boshoff and Minto (1975), Mrema (1981,

1983), Nuuba and Kaul (1986)). There is a physiological limit to what a normal human

being can do in agriculture using the hand tool technology. Draught animals and internal

combustion engines do significantly increase the output of human energy expended in

agriculture. Mechanization has been a major factor in the development of agriculture in

industrialized countries, and it employs a high place value in connection with the

intensification of agricultural production in developing countries. Indeed, the number of

tractors and combines in service often serves as a measure of the modernity or

progressiveness of a country’s agriculture (Sievers (1983), Binswanger (1984), and

Farington (1984). One of the main reasons for increasing a farm’s degree of

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mechanization has always been and still to raise its labour productivity, i.e. to achieve a

higher output and or better income per man hour of work. In doing so, the farmer

substitutes capital in place of scarce/expensive manual labour. Increasing the degree of

mechanization, for example, buying a tractor, substitute’s capital for labour. Numerous

pertinent studies for example Abercrombie (1973), McInerny and Donaldson (1973),

Binswanger (1978), Bergmann and Mai (1984), have documented this effect familiar in

industrialized countries.

2.6 Meaning and measures of agricultural productivity

Agricultural productivity is a measure of the ratio of agricultural outputs to agricultural

inputs. Economists usually asses’ agricultural productivity by measuring the production

of an agricultural good for example the yield of a crop and by estimating its value on the

market thus knowing the potential for profits. While individual products are usually

measured by weight, their varying densities make measuring overall agricultural output

difficult. Therefore output is usually measured as the market value of final output which

excludes intermediate products for example corn feed used in the meat industry. This

output may then be compared to many different types of inputs such as labour and land

(yield) these are partial measures of productivity. Agricultural productivity can be

measured by what is termed Total Factor Productivity (TFP). This method of calculating

agricultural productivity compares an index of outputs to index of inputs. This measure

of agricultural productivity was established to remedy the shortcomings of the partial

measures of productivity notably that it is often hard to identify the factors that cause

them to change. Changes in TFP are usually attributed to technological improvements.

2.6.1 Labour productivity-

labour productivity is typically measured as a ratio of output per labour hour.

Productivity may be conceived of as metric of the technical or engineering efficiency of

production. As such the emphasis is on quantitative metrics of inputs and sometimes

output. Productivity is distinct from metrics of allocative efficiency, which take into

account both the monetary value (price) of what is produced and the costs of inputs

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used, and also distinct from metrics of profitability which addresses the difference

between the revenues obtained from output and the expenses associated with

consumption of inputs.(Courbois & Temple 1975; Gallop 1979; Kurosawa 1975; Pineda

1990; Saari 2006 ).

2.6.2 Multi-factor productivity

This is the ratio of real value of output to the combined input of labour and capital.

Sometimes this measure is referred to as total factor productivity. In principle multifactor

productivity is a better indicator of efficiency. It measures how effectively and efficiently

the main factors of production labour and capital combine to generate output. However

in some circumstances robust measures of capital input can be hard to find (Saari

2006). Labour productivity and multifactor productivity both increase over the long-term.

Usually the growth in labour productivity exceeds the growth in multifactor productivity,

reflecting the influence of relatively rapid growth of capital on labour productivity.

Table 2.2 Comparison of basic measures of partial productivity.

TYPE OF MEASURE VARIABLES TO BE

MEASURED

VARIABLES EXCLUDED

Physical Quantity Quality and distribution

Fixed price value Quantity and quality Distribution

Nominal price value Quality, & quantity, distribution

None

Source (Saari, 2006)

2.7 Farm mechanization and productivity, production, labour and income. (Empirical evidence)

The cornerstone for economic progress for Zimbabwe lies in the development of its

natural resources and manpower (Rukuni et, al; 2006). The role of mechanization in

agricultural development is seen as the employment of traditional capital in the

production or, in a neural sense to increase total labour employed in the production

function without being a substitute of any factor. The addition of power only increases

productivity in combination with other inputs, thus role of mechanization must be seen in

relation to the role of other factors in the production function

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Farm mechanization reduces the human drudgery and enhances agricultural

productivity. For example during the post green revolution the impact of farm

mechanization on agricultural production and productivity was well recognized in India.

Depending on the use of other inputs such as irrigation, high yielding seed varieties,

chemical fertilizers, and pesticides different states in India have attained different levels

of mechanization, consequently the agricultural production and productivity has

witnessed 3 to 4 fold increase. Geoffrey C. Mrema (1990). Studies have been

conducted by various organizations and individuals highlighting the impact of farm

mechanization on farm production and productivity. Singh and Singh (1972) concluded

that tractorising farms gave higher yields of wheat, paddy rice and sugarcane and

produced a higher overall gross output per hectare than non tractorised farms. The

National Council of Applied Economic Research (NCAER) New Delhi (1973) compared

the values of annual farm output per hectare of net sown area, under different levels of

mechanization. The output per hectare was found to increase as the level of

mechanization increased from irrigated non mechanized farms to tube well, tractor

thresher farms. Moreover, Madras (1975) found that tractor owned farms obtained

increased productivity of paddy sugarcane and groundnuts by 4.1 to 28.3%, 13.1 to

34.2%, and 9.8 to 54.8% with an average value of 15.8%, 23.2%, and 31.8%

respectively. Pathak et al (1978) conducted surveys on 5 different categories of farms in

Ludhiana District of Punjab to asses the effect of power sources on production and

productivity. The yield of paddy, maize was reported to be higher on tractor farms than

on bullock farms. The use of tractors enhanced agricultural productivity due to better

seed bed preparation, timeliness of operations and precision in distribution and

placement of seed, and fertilizer owing to the use of the seed cum fertilizer drills.

Available evidence suggests that mechanization helps in overall increase in

employment of human labour. Gipe Poona (1967) concluded that tractorisation

generates greater demand for labour by facilitating more intensive cultivation thus there

was no significant displacement of human labour after tractorisation. USAID, New Delhi

(1970-71) conducted a study to assess the impact of tractorisation. It was concluded

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that the adoption of high yielding varieties accompanied by use of chemical fertilizers

and enhanced cropping intensity led to higher demand for labour. Permanent labour on

tractor holdings showed a decline of the tune of 2.2% compared to the conventional

level. The employment of casual male labour showed an increase of 38.5%. in respect

of labour employment the increase was even higher at 80%. A tractor on average

displaced 4 bullocks. Moreover, AECR, Viswa Bharati (1973-74) conducted a study on a

sample of 60 farms in Shabad District of Bihar India. It revealed that the displacement of

human labour in terms of value of all inputs was 11.6% in the case of farm owning

tractors and 5.1% in the case of farm using tractors. The maintenance of tractors by the

first category of farms led to the additional employment of labour which more or less

compensated for displacement of labour in farming operations.

Different studies conducted on farm mechanization indicated that net human labour

displacement in agricultural operations was not significant and it was more than

compensated by increased demand for human labour due to multiple cropping, greater

intensity of cultivation and higher yields. On the other hand the demand for non-farm

labour for manufacturer, services, distribution, repair and maintenance as well as other

complementary functions increased substantially and helped in relieving rural

unemployment to some extent. Mechanization in agriculture provides indirect

employment to skilled & unskilled persons engaged in operation, repair and

maintenance of farm equipment. NCAER(1980) assed the employment in the

manufacture distribution, repair and servicing of tractors based on sample survey of

private repair shop, in general, generated employment of 22.2 man days per tractor per

year.

Mechanization generates many non-farming subsidiary activities among the farming

households. On one hand additional employment is created in the manufacture of farm

machinery, distribution of equipment and spare parts, fuel lubricants, repairing servicing

etc. On the other hand many subsidiary activities like dairying and poultry keeping got

generated. NCAER (1980) showed that tractorised farms reduced their draught animal

stock and increased their milk stock. Tractor owners and tractor users had 82% and

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25% more milk cattle respectively as compared to bullock farms. A tractor owner was

able to increase his household income by undertaking supplementary activities such as

dairying and provision of custom hiring. Moreover farm mechanization helps greatly in

the farming community in the overall economic upliftment. A research by AECR (1970-

71) revealed that gross farm income was higher on mechanized farms than on non-

mechanized farms. Gross crop output per cultivated hectare was reported to be higher

for tractor operated farms as compared to non tractorised farm

2.8 Increasing agricultural productivity is central to reducing poverty -Technology’s Role

Agriculture plays a unique role in reducing poverty. Partly this reflects the sheer

numbers of poor people engaged in it. Around 75% of those surviving on less than

US$1 a day - the internationally agreed definition of absolute poverty – live in rural

Areas (IFAD, 2001) and agriculture is an important livelihood source. It is estimated that

70% of sub-Saharan Africa's labor force and 67% of South Asia’s works in Agriculture

(Maxwell, 2001). But the argument in favor of agriculture as the poverty- alleviating

sector par Excellence rests on more than population statistics. Improvements in

agricultural productivity has a powerful knock-on effect to the rest of the economy

through job creation in neighboring sectors such as food processing and input supply as

well as directly in farming; increasing the supply of affordable food; and stimulating and

supporting wider economic growth and development.To the extent that technology

raises agricultural productivity, it should be the major factor in creating these positive

effects. Thirtle et al., (2003) explored the relationship between agricultural productivity

and poverty. They drew on observations between 1985 and 1993 in 48 developing

countries and found that a 1% improvement in crop yields reduced the proportion of

people living on less than US$1 per day by between 0.6 and 1.2%. No other sector has

demonstrated such a comparably high impact on poverty. Thus, Lipton (2001) argues

that no other sector than agriculture offers the same possibilities to create employment

and lift people out of poverty. Indeed, the adoption of new technologies and subsequent

increases in agricultural productivity in different parts of the world explain, in large part,

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the regional differences in the reduction of poverty over the last few decades. Nkamleu

et al. (2003) calculate changes in agricultural productivity in 10 countries in sub-

Saharan African countries between 1972 and 1999. In contrast with significant progress

in Asia, Nkamleu et al found that, on average, total factor productivity decreased in that

period by 0.2% annually. They suggest that, whilst efficiency was constant,

technological change was the main cause of the failure of total factor productivity to

increase.

2.9 Other benefits attached to farm mechanization

Mechanization raises the potential for multiple cropping. That is raising three or more

crops in communal areas and the rest of the farmers requires additional power and

improved technology to increase operational efficiency and hence production.

Agricultural mechanization also makes significant contribution in enhancing cropping

intensity. A researcher by the name Chopra (1974) carried out a study on a sample of

Punjab farms. He made a comparison of tractor owning farms in terms of the situation

before and after the introduction of tractors. The cropping intensity was reported to be

higher after the introduction of tractors. Intensified annual production can be made

possible by farm mechanization. Agriculturalists think in terms of production for as many

days as possible. The objective of modern agriculture is to produce as much

food/hectare and is economically feasible with the help of appropriate technology.

Moreover finding solutions to environmental problems in agriculture requires improved

agricultural tools and machinery, for example for soil tillage and pesticide application the

latter also addressing health concerns, similarly machines are required to assist with

post harvest loss reduction and on farm processing. Thus it is now recognized that

agricultural mechanization is crucial in the fight against hunger and poverty and at the

same time to address environmental and health concerns.

2.10 What influences the adoption of new technology by farmers?

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A range of factors appear to have been critical in determining the rate at which farmers

have innovated new ideas and so been able to raise productivity for the benefit of

growth and the pace of poverty reduction.

2.10.1 Secure output markets

Farmers will innovate to increase subsistence production, but as innovation generally

implies some type of investment (in cash, labor or learning) the chances of farmers

investing and innovating are greatly enhanced by the existence of secure markets. As

the evidence shows, it is difficult to overestimate the importance of reliable output

markets as an incentive to new technology adoption. Dorward et al. (2004) argue that a

key feature of many successful early Green Revolution environments was government’s

role in stabilizing output prices, a function which has been progressively dismantled in

Africa where innovation has been limited. Wiggins’ (2000) survey of African case

studies found a number of success stories that contradict the general pessimism about

African agriculture, but virtually everyone was associated with well-functioning output

markets. In Malawi, Orr and Orr (2002) argue that unreliable maize markets lock many

farmers into inefficiently producing as much of their own grain needs as possible, rather

than innovating with new crops in which they may well have a comparative advantage.

2.10.2 Effective input supply systems, including credit

While there is danger in relying too heavily on “technology on the shelf”, effective input

supply systems are essential, particularly when technological change or advance

depends on purchased inputs. Inadequate formal seed supply systems have been

shown to dampen, or even preclude the diffusion of new crop varieties (Tripp, 2001).

Increasing fertilizer use has long been plagued by difficulties in providing the right

products in affordable pack sizes (Omamo and Mose, 2001). Establishing the systems

to provide those inputs is, however, one of the major challenges for many technologies,

and not merely the conventional seed-and chemical technologies. Delivery of tissue

culture banana plantlets in Africa requires the development of a network of intermediary

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nurseries (Wambugu and Kiome, 2001). Nurseries are also crucial for the spread of

many agro forestry technologies, and efforts at encouraging farmer groups to take on

this role have largely failed (Bohringer and Ayuk, 2003). The delivery of veterinary

technologies depends largely on the delivery role of the private sector (Leonard, 1983).

But an operational system of input provision is often ineffective in the absence of

effective credit systems. Previous experiences with state-subsidized credit provision

have received much justified criticism (Adams and Vogel, 1990) and new approaches

are being considered, including linking input supply and output procurement (Dorward et

al., 1998).

2.10.3 Supporting infrastructure – particularly irrigation

The presence of supporting infrastructure is fundamental to effective innovation on new

technology and was a major factor in Asia’s successful Green Revolution. Roads are

critical to supporting input and output marketing (Dorward et al., 2004), but the

expansion of irrigation probably constituted the most important element of supportive

investment. The expansion of irrigation in developing countries has been greatest where

attaining increasing agricultural output through land expansion has been difficult and so

gains are made by intensification. Thus, both South and East Asia have a much higher

use of irrigated land use compared to Africa (Table 1). By 2030, it is projected that

about 80% of future production gains will be made from intensification (in part

dependent on irrigation) with a much smaller proportion through land expansion (de

Haen et al., 2003).

Table 2.3: Irrigation in Africa and Asia, 1961/1963 – 1997/1999

Irrigated land in use(million hectares)

1961/1963 1979/1981 1997/1999

East Asia

40

59

71

South Asia

37

56

81

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Sub-Saharan Africa 3 4 5

Source FAO 2003

2.11 How does new agricultural technology benefit the poor?

A number of factors influence the extent to which the poor benefit from changes in

agricultural productivity through the adoption of new technology. These are discussed

below, beginning with the two most important factors – impacts on employment and

food prices.

2.11.1 The impact on employment

Employment on the farms of others is of critical importance to the livelihoods of the

poor. This is not just true for the classically landless; employment is also a vitally

important way for many farmers to supplement their incomes. The impact of new

technology on labor markets – specifically its impact on the demand for labor and wage

rates - is of great importance to the poor. Most evidence on this issue comes from the

Asian Green Revolution experience and, while often technology-specific, a number of

general principles emerge with respect to the impact of new technology on the demand

for labor and wage rates. In terms of the impact on the demand for labor:

• the adoption of high yielding rice and wheat varieties generally increased demand for

labor due to the higher harvesting and threshing requirements associated with their

greater yields

• the majority of additional labor used was hired rather than family labor (Lipton and

Longhurst, 1989). This is particularly important for the poorest

• increased labor demand was greatest when new varieties were introduced into high

potential areas and often associated with an increase in cropping intensity. The impact

was less pronounced when in low potential areas. (David and Otsuka,1994; Lipton and

Longhurst, 1989). The impact on wage rates is more difficult to determine because

there are numerous causal, and on occasion counteracting, factors.

Some conclusions can be drawn though, including that:

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• Generally wages appear to have increased (IFPRI, 2002)

• labor saving technology has probably dampened the rate of wage increases, although

this does not mean that wages have fallen because of the adoption of new technology.

Lipton and Longhurst (1989), show that while a doubling of yields increased wages by

40% early in the Green Revolution, a similar yield increase 20 years later resulted in

only a 10-15% increase in wages due to mechanization. Bautista (1997) describes

disappointing increases in the demand for agricultural labor in the Philippines, explained

in part by subsidized farm mechanization

• In some cases, e.g. herbicide adoption in rice systems (Naylor, 1994), the introduction

of labor-saving technology has been a response to rising rural wage rates caused by

growth in non-farm wage rates

• Even where wage increases have been modest, the adoption of new technology has

frequently increased the number of employment days, and on occasion, facilitated the

introduction of contracts for casual laborers (Leaf, 1983).

2.11.2 Food prices

For the poor, the price of food is critically important given the relatively larger proportion

of their income generally spent on it. A relative lowering of food prices – particularly of

staples - allows the poor to eat more and possibly better which has a positive impact on

nutrition, health and food security. But cheaper food also releases income which can be

spent on other goods and services with immediate positive benefits to the poor such as

improved shelter or access to key services such as health and education. This release

of income also creates demand for goods and services which can have a powerful

multiplier effect on the wider economy. In many developing countries - and for the

developing world as a whole - increases in the production of staple foods have

comfortably outstripped population growth since the mid-1960s when the Green

Revolution began to be adopted widely. Only in Sub- Saharan Africa have food supplies

grown slower than population during the last thirty years. Given this significant increase

in per capita supply, and the relatively low elasticity of demand for basic foods, the real

world market prices of the major traded grains have steadily fallen since the early

1950s. At the individual country level, increased production of food grains can have a

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dramatic effect on prices. This is of great benefit to the poor, both in urban and rural

areas, where many people buy, as well as grow their own food. (De Janvry and

Sadoulet, 2002; Jayne et al., 1999). But increasing production can also be a double-

edged sword if it reduces prices to the extent that producer incomes fall.

However, where productivity increases due to technology match or even outpace the

corresponding fall in prices, both net consumers and net producers can benefit.

Bangladesh provides an excellent example of this. Between 1980 and 2000, production

of rice and wheat increased from below 15 to over 25.7 million tonnes, increasing per

capita availability over the same period from 425 to 510 grams per day, despite

population increasing over the same period from 90 to 191 million people. Real

wholesale prices in Dhaka markets of rice and wheat have consequently fallen

dramatically, with the price of rice falling from just over Taka 20 to around Taka 11 per

kg in two decades. But despite declining market prices, farmers have successfully

increased their production, yields and incomes - rice yields have risen from an average

of 2 tonnes to over 3.4 tonnes per hectare by the early 2000s – through the use of new

varieties, fertilizer and, above all an expansion of irrigation. These improvements have

allowed farmers to cut their unit costs of production and so offset the impact of falling

prices on their incomes. It also appears that smaller farmers have not been excluded

from this technology.

2.11.3 Nutrition and food utilization

There are numerous examples of how agricultural technology has benefited the

nutritional status of poor households. These include:

• Improved varieties with increased vitamin content that contribute to the reduction of

human disease;

• Post-harvest fortification of crops to reduce vitamin deficiencies;

• Longer cropping seasons to regulate food supply and reduce the number of months

that households go hungry; and

• Improved storage and processing to extend the shelf-life of food and reduce waste.

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2.11.4 Access to land and other resources

The extent to which agricultural technology can benefit poor people clearly relates to

existing inequalities in land and access to other resources. There are various

explanations of why poor people stay poor that are couched in terms of the allocation of

land and other resources. There is concern that technologies may exasperate inequality

in access to productive resources. One major criticism of the early Green Revolution

was the fact that early adopters tended to be larger (richer) farmers. (Indeed, a large

proportion of subsidies for Indian farmers continue to go to richer farmers (Gulati &

Narayanan, 2003)). These farmers were able to take greater risks and gain economies

of scale from applying new technologies to larger land holdings. Evidence suggests

that, subsequently, smaller farmers caught up and, in some cases, took better

advantage of the new technology (Lanjouw and Stern, 1998; Hazell and Ramasamy,

1991). Nevertheless, it is widely accepted that, initially at least, technology is an unlikely

way to overcome major inequalities in access to basic resources, especially land.

2.11.5 Gender issues

Gender-related effects of technology change are often important in determining the

impact of adoption on poverty. Technology generation has tended to favor crops

traditionally grown by men, who frequently have greater access to labor, markets, credit

and other inputs than women to a degree that may impact negatively on the intra-

household distribution of income and consumption (Doss, 2001). Addressing these

challenges goes well beyond technology design, as male dominated societal rules and

norms, and a complex household environment of ‘joint decisions, multiple objectives

and mutual dependence’ (Bonnard and Scherr, 1994) make it difficult to target, or

predict the gender-related outcomes of technology development. Simply targeting

technology to women’s crops is not necessarily the answer. (von Braun and Webb,

1989).

2.11.6 Sustainability issues

Whilst new technologies are important for poverty reduction, if not carefully managed,

26 | P a g e

they can create additional demand on resources which may simply not be sustainable in

the future. The most obvious example of this is water, for example the lowering of water

tables and loss of aquifer water, but other resources, including biodiversity and

chemicals, are also discussed.

2.11.7 Biodiversity

Technological advance is often blamed for the loss of biodiversity, but the issues here

are complex. Agricultural expansion generally has caused habitat destruction and, at

the local level, productivity increases can attract new farmers to the agricultural frontier

by making farming more profitable. But yield increases achieved through new

technology have curbed deforestation and the cultivation of marginal lands. If world crop

yields had remained at their 1960 levels, another 800 million hectares of land

(equivalent to the Amazon River basin) would have had to be brought into cultivation to

meet current demand (Ausbel, 1996). Modern crop varieties have frequently displaced

many local varieties. But the relationship of these changes to overall genetic diversity is

difficult to unravel. Recent work shows that the uptake of wheat MVs has not lowered

genetic diversity (Smale, 1997) as farmers often adopt a new crop variety and grow it

alongside their traditional varieties.

2.12 But have the poor benefited from new agricultural technology?

For many the key question remains: to what extent, and in what circumstances, have

poor people benefited from new agricultural technologies or have the benefits been

confined to the better off? Assessing this “distributional” impact of new technology is

difficult, not least as the uptake of innovations is inevitably skewed with the better-off

usually being “early adopters.” Propositions regarding distributional impact should

therefore be carefully specified, and any assessment of ultimate impact should not be

based on the adoption pattern seen in the early years after technology release. (Rogers,

1994). Among the most useful (but rarest) assessments of technology’s impact on

poverty are those that follow farming communities’ experiences over a longer-term

period (Lanjouw and Stern, 1998; Hazell and Ramasamy, 1991). These studies tend to

27 | P a g e

show that the poor have benefited from new technologies, principally through increased

employment opportunities and higher wage rates.

On the other side, a review by Freebairn (1995) of over 300 other studies related to the

Green Revolution revealed a general increase in inequality between regions as a result

of technology uptake. This conclusion however, requires qualification. First, it is

inevitable that technological advance will lead to an adopting area becoming relatively

better off compared with a non-adopting area. This simply underlines the importance of

balancing investment in technology generation between marginal and favored

environments. Secondly, the review itself identified the difficulties in separating the

impact of technological change from concomitant changes in population, policies or land

tenure. Rigg (1989), identified a similar issue: many negative assessments of the

poverty impact of the early Green Revolution are examples of ‘guilt by association’ – the

technology was seen as responsible for increasing inequality which was primarily the

result of other factors including: farm concentration, urban migration, and so on, which

accompanied technology dissemination.

Most of the evidence about the poverty reducing effect of agricultural technology comes

from Asia. In Africa there are far fewer examples of where agricultural technology has

benefited poor people. However, evidence from Zimbabwe reveals a Post-

independence Green Revolution amongst smallholders which had a very Significant

impact on poverty. This was achieved through the introduction of hybrid Maize,

expanded access to credit, guaranteed prices and marketing subsidies. The Outcome

was a doubling of maize production between 1980 and 1986 (Eicher, 1995).

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Chapter 3: Research Methods

3.1 Introduction

This chapter includes a discussion of the methods that were used in gathering

necessary data that were used to determine the impact of farm mechanisation on maize

yield, impact on farm labour use and on the area put under crop cultivation by the newly

resettled farmers in Chipinge district. It also covers the description of the study area

from where data were collected. The section also includes the methods that were used

in data analysis and the econometric models that were used in data treatment and

analysis.

3.2 Data collection (Secondary data source)

Secondary data obtained from the African institute of Agrarian studies (AIAS) was used

in this study. It was once primary data that were used in a related study carried out in

2009 by the same institution. The data obtained from AIAS referred to newly resettled

farmers in Chipinge district with most of the farmers being beneficiaries of the

agricultural mechanisation programme by the government of Zimbabwe. The set of data

contained variables that were very useful and relevant in making conclusions on the

study.

3.3 Study area selection and description

Chipinge lies in natural region one and receives annual rainfall that is above 1000mm

per year. The area is characterised by a diverse of farming activities practised covering

cereal crop production, market gardening, coffee and various other cropping activities.

The maize crop is one of the major crops grown by the newly resettled farmers in the

district. The area was selected by the researcher mainly due to the fact that the

researcher is quite familiar with the area and bears some background knowledge of the

area. Moreover resettled farmers in the area were some of the beneficiaries of the farm

mechanisation programme launched by the government of Zimbabwe a few years ago.

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3.4 Conceptual framework

3.4.1 Introduction

This conceptual framework outlines the types and sources of change in agricultural

output and the role of agricultural productivity in bringing the change. It will further

outline the necessary inputs such as labour capital equipment and the various other

inputs such as seed fertiliser. It further outlines the factors that affect agricultural

productivity.

Table 3.1Types and sources of change in agricultural output

CHANGES IN AGRIC

OUTPUTS

= CHANGES IN AGRIC

INPUTS

+ AGRIC PRODUCTIVITY

(Conventional market measured outputs)

Crops

Livestock

(Conventional market measured inputs)

Intermediate inputs

Fertilizer Pesticides Energy

Feed & seed livestock

(Changes in outputs not accounted for by the

changes in inputs)

Sources are: Agricultural research & development

Extension Education Infrastructure

Government programs Mechanization

3.4.2 Illustration of the conceptual framework

Agricultural productivity will be shown by the out per acre. Other than other value

measures productivity will be shown by the physical measure which is output, that is

maize yield. Variable to be measured is output and the variables to be excluded include

quality and distribution. Partial productivity results will be assumed to be true indicators

of total productivity. The term partial productivity illustrates well the fact that total

productivity is only measured partially-or approximately (Saari 2006)

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3.5 Theoretical framework

Agricultural productivity levels can be determined by a comparative analysis of the

productivity levels of different crops on the farm. This can be difficult for those farmers

who have not been farming for the past seasons, which is the case for most of the fast

track farmers. In this case it is therefore important to carry out inter-farm comparative

analysis in order to establish other factors other than input availability, which can

influence output. The major assumption is that capital alone without other inputs and the

ancillary services such as extension farmer education including research and

development cannot increase productivity.

Yield per hectare that is productivity can be modelled in a linear multiple regression

model of the form nn XXXY .......22110

Where Y is the yield per hectare and α0 to αN are the regression coefficients and the X1

to XN representing the various explanatory variables expected to explain change in the

dependent variable.

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3.6 Analytical framework

Table 3.2.Research Analytical Framework

Research question

Objectives Hypothesis Variables under

study

Data analysis

Does the use of farm equipment have a significant

contribution towards

increasing farm productivity amongst the

resettled farmers in Chipinge

district?

To determine whether the use of farm

equipment by the resettled

farmers had an impact on farm productivity.

Capital intensity positively affects farm productivity

in Chipinge District.

Maize yield, capital

intensity.

Regression analysis and comparison of

the yield means of farmers with

different levels of mechanization.

Does the levels of mechanization

move together with the use of

labor on a farm

To determine the correlations

between the level of

mechanization labor use

Labor use on a farm is

negatively related to the

level of mechanization. farm machinery

Quantities

of labor and

capital,

Correlation analysis

3.7 Regression analysis

Hypothesis one

To test whether capital intensity has a significant contribution on farm productivity. In

this study, the linear multiple regression model was used. The dependent variable was

maize yield with the assumption that maize productivity will be a true approximate of

total farm productivity. Table 3.3 below shows the independent variables used in the

linear regression model.

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Table 3.3 Independent Variables

Variable

Abbreviation

Variable description

Effect on productivity

Sex

Age

Agric training Marital status

Education level attained

Total labour

Capital intensity Draught power

SEX

AGE

AGTRAIN

MSTUS

EDU

LABOR

CAPTAL

DRGHT

Sex of plot owner

Age of plot owner

Formal agric training attained

Marital status

Highest education

level attained Total labour

Capital intensity

Number of cattle and donkeys owned

_

+

+

+

+

+

+

+

The regression model used in this study is presented as follows;

DRGHT

CAPTALLABOREDUMSTUSAGTRAINAGESEXY

8

76543210

Where Y is the dependent stochastic variable maize yield, α1 to α8 are the regression

coefficients and α0 is the constant or intercept

µ is the error term and it represents the unexplained variation in the dependent variable.

Problems associated with multiple regressions such as autocorrelation,

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heteroscedasticity and multicollinearity were corrected in the Shazame stastical

package.

3.8 Correlation analysis

Hypothesis two

In order to determine the correlations between capital and labour use, correlation

analysis was applied in this study. This type of analysis was carried out to see whether

capital in this sense mechanization move together with number of workers employed

permanently or temporarily on a farm. Correlation analysis generates a single value, the

correlation coefficient, that shows how much the two variables move together usually

symbolised by the letter r. This coefficient r ranges from a value of zero indicating no

correlation to a plus or minus one indicating a perfect linear relationship. The plus or

minus sign indicates the direction of the relationship. For example if the value of r is

positive the two variables capital and labour use move in the same direction and if its

negative then the two variables move in the opposite direction. To indicate the actual

proportion of the shared variance the coefficient of determination r2 was computed.

3.9 Analytical techniques

STATA, EXCEL and SPSS were the major analytical packages used in this analysis.

Significance tests were conducted to ascertain valid conclusions and to ascertain the

stationarity of the data. After specifying the econometric model, T tests were conducted

for this analysis, causality tests and Cointegration tests were also conducted to

establish the relationships between variables.

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CHAPTER 4: DATA ANALYSIS AND RESULTS

4.1 Introduction

This section presents a description of the socioeconomic characteristics of the studied

resettled farmers. Socioeconomic characteristics to a large extent influences the

farmers productive capacity, his ability to assimilate information and hence the overall

performance of the farmer. Characteristics considered in the study include gender, age

distribution of farm owners, levels of education and farm characteristics.

4.1.1 Household characteristics

Table 4.1 Age distribution of households

AGE BRACKET PERCENTAGE (%)

Less than 30 3

31 to 50 years 35

51 to 70 years 40

0ver 70 years 22

Mean age 51 years

(Source survey data 2010)

Table 4.1 above indicates that in general the population had very few young people who

are under 30 years of age. A large portion of farmers were found to be in the age

bracket of 51 to 70 years of age.

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4.1.2 Gender and marital status of farmers

Gender of the household head has been shown to be a critical variable influencing

choice and adoption of technologies among communal households (Zilberman, 1993,

Young, et al, 1998). As such it was found it expedient to identify the gender composition

of the households under review. Gender wise the population had 20.1 % female farmers

and 79.9% male farmers. Figure 4.1 below illustrates the marital status of households.

Figure 4.1 Marital status of households

4.1.3 Levels of education and household head occupation

Education influences the extent or the capacity to which an individual assimilates

information about agricultural machinery use. It is a human capital variable that has

36 | P a g e

been shown to be critical in the choice and efficient use of agricultural technology (Dinar

and Yaron, 1990). In the survey farmers were asked to give information regarding their

levels of education and the results are shown in the table below

Table 4.2 Educational status of households

LEVEL OF EDUCATION PERCENTAGE (%)

Advanced level 3.7

No formal education 6.5

Ordinary level 32.5

Primary level 23.2

Standard six 10.5

Tertiary 8.2

ZJC 15.3

Total 100


In this survey farmers were asked to give the highest level of education attained.

Farmers were also further requested to give their occupational statuses. As shown in

table 4.2 above over 90% of the farmers attained at least primary and secondary

education which shows high literacy levels within the Chipinge farming community.

4.1.4 household sizes

A household typically consists of individuals residing together for at least three months

of the year (smith, 1995). Household size is used as a proxy for labour availability for

households. However it is important to differentiate between effective labour and

children. The mean size of household was 4.53 with a minimum of one and a maximum

of ten members per household.

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4.2 Farm characteristics

4.2.1 Farm size

Farm size describes the total land holdings of a household and is important in decisions

regarding the use of technologies (Adesina, 1993). The mean of plot size was found to

be 24.2 acres. Since not all of the land will be under cultivation, respondents were also

asked about the amount under cultivation. The amount of land under cultivation on

average was found to be 8.01 acres.

Table 4.3 Land ownership by the farmers

MEAN SIZE (acres)

Total amount of land owned 24.2

Total amount of land cropped 8.01


4.2.2 livestock ownership

In the communal areas of Zimbabwe, livestock are important in two aspects. Firstly

cattle and donkeys are critical for draught power purposes and secondly as household

wealth (Blackie, 1984). In the survey farmers were requested to give the numbers and

types of livestock owned.

Table 4.4 Livestock ownership (means)

LIVESTOCK TYPE MEAN/household

Cattle 12.1

Donkeys 3.5

Chicken 18.0

Turkeys 3.6

Pigs 0.22

goats 5.77

38 | P a g e

sheep 0.77

rabbits 0.30


4.2.3 Agricultural implements

Agricultural implements are essential in the process of agricultural production in

communal areas as they are inputs of farm activities. They are believed to reduce the

drudgery of farming activities and also to increase farm productivity.

Table 4.5 Agricultural implements owned

NAME OF IMPLEMENT MEAN PERCENTAGE (%)

Ploughs 1.72 97.1

Cultivators 1.22 80

Harrows 1.29 74.3

Oxcart 1 90

Tractors 0.4 2.9

Wheel barrow 1.04 80

Hoes 10.1 100

Combine harvester 0 0.5

Axes 2.45 95

Ridger 1.5 40

Adequacy of

implements

66%


In terms of the basic agricultural implements such as ploughs, cultivators, harrows, ox

carts, hoes tractors, ridgers, wheel barrows, at least 80% of the population did own one

or more. Respondents were further asked about the adequacy of the above elements

and how the farmers dealt with the problem of shortage. At least two thirds (66%) said

39 | P a g e

that their implements were adequate. However those farmers who indicated shortage

took advantage of existing social networks and either borrowed from relatives or friends

within their community and some hired tractor services from the nearest District

Development Fund (DDF). About 25.7% of the farmers borrowed from relatives, 5.3%

borrowed from friends, and about 10% borrowed tractors services from the DDF .

4.2.4 Sources of income for the households

Households were asked to give information regarding the various sources of income

available to them. Most farmers could not give approximate figures of the contribution of

each source per year per household. However ranking the sources showed that about

80% of the income sources came from retained farm earnings, the sale of livestock,

gardening and sale of agricultural crops to mention but a few. Other sources of income

included remittances, small businesses credit /loans and formal employment.

4.2.5 labour sources of households

Labour is an essential input on the farm as it has profound effects on farm productivity.

Labour is needed mainly for planting weeding and harvesting. Respondents were asked

to give information about their various sources of labour.

Table 4.6 Farmers’ sources of labour

CATEGORY OF LABOUR PERCENTAGE (%)

Casual labour 31.5

Family labour 51.5

Casual and family 17


Half of the respondents used exclusively family labour for productive activities. About

17% used a combination of both hired and family labour whilst 305 used hired labour

exclusively as shown in table 4.6 above.

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4.3 Regression and correlation analysis

4.3.1 Capital equipment intensity and productivity

In this section, findings relating to the relationships between farm productivity and

capital equipment intensity are presented. Productivity here was measured by the yield

of maize per hectare (10000m2)

The regression model applied to the study produced the results presented in table 4.7

below;

Table 4.7 Results of the linear regression model

VARIABLE OLS Coefficient Significant t

Sex 0.379 0.383

Age -265.950 0.441

Education -433.880 0.562

Formal training 297.007 0.248

Marital status 340.301 0.046**

Labour -105.156 0.873

Capital intensity 766.828 0.070*

Draught power 179.950 0.002***

Constant -2625.013 _

R-squared 0.712 _

Adjusted R-squared 0.6534 _

F value 8.226 0.0011***

Durbin Watson test statistic 2.156

Notes: ***significant at 1%, **significant at 5%, * significant at 10%

The R-squared value for the regression equation was found to be 0.712 showing that

the model’s predictor variables accounted for 71% of the total variation in maize

yield/productivity. Adjusted R-squared was 0.6524. The F value for the significance of

whole regression equation is greater than the tabulated F and hence the equation is

41 | P a g e

significant at 1% level. Capital intensity showed to have a positive effect on the yield of

maize shown by a regression coefficient of positive 766.828 in the estimated model,

significant at 10%. Draught power and marital status variables were also significant in

the model.

Table 4.8 Farm mechanization level and yield means

MECHANIZATION LEVEL PERCENTAGE OF

FARMERS IN CATEGOR

MAIZE YIELD

MEAN/HACTARE(TONNES)

Highly mechanized 4.5 1.85

Medium 31 1.72

Low 64.4 0.85


By comparing also the maize yield means at different levels of capital intensity

(mechanization level) yield averages of maize were found to increase as capital

intensified as shown in table 4.8 above.

4.3.2 Insights from the analysis

The results obtained from the study show that mechanization have a significant impact

on productivity amongst farming communities in Chipinge district. The other variables

also having a significant contribution to productivity in the estimated model were draught

power and marital status of the household head. Household, labour and education had

a negative impact on productivity but sot significant.

4.3.3 Mechanization and labour use on the farm.

Results from the correlation analysis showed a correlation coefficient r of 0.457 and by

raising the r to the power of 2, the coefficient of determination was found to be 0.208849

showing that capital and labour are positively correlated though the relationship is a

weak one. Precisely stated, the two variables share about 20.88% of the variance.

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Correlation however shows the degree, direction, and significance of relationships

between the variables but assuming no causality and without identifying one as

dependent or independent. The weak positive correlation can be shown graphically as

in figure 4.2 below

Figure 4.2 Correlation between labor use and capital

0

10

20

30

40

50

60

0 20 40 60 80 100 120 140 160 180

The above plot shows the extent of the relationships though not showing the actual

relationship it’s just a sketch not drawn to scale.

4.4 Factors hindering effective use of farming implements amongst the resettled farmers in Chipinge district.

Respondents in this study identified a number of factors hindering effective use of farm

machinery in their area. The identified factors are summarised below.

4.4.1 Lack of manpower

Most farmers pointed out that lack of labour is the main factor limiting the effective use

of farm machinery. They indicated that most of their children who used to help them in

using some of the implements and other farming activities e.g. planting are at schools

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and other institutions were they are now employed. Therefore it is difficult to effectively

use the implements they have due to lack of labour.

4.4.2 Weaknesses in draught animals

According to the result of the survey, 50% of the farmers indicated that they could not

effectively use ploughs and most of the animal drawn implements due to weaknesses

with the draught animals such as animal diseases and inadequate grazing.

Weaknesses in their draught power especially donkeys results in more labour hours in

cultivating big fields.

4.4.3 Lack of access to credit facilities

Results indicated that lack of financial resources to buy the complementary inputs such

as seed and fertiliser were a major challenge in trying to maximise benefit even with the

much needed implements on their farms. Some farmers could not use the equipment

due to lack of credit to buy fuel for the engine powered implements.

4.4.4 Relief and nature of surface

In certain areas relief was noted as a factor hindering effective use of farm machinery

especially the tractors and the animal drawn scortchcarts. Some areas could not receive

tractor services due to inaccessibility of the areas as they had some hills and steep

landscape. In addition some farms were noted to have some rocky surfaces which

again limited the effective use of the implements especially areas around Chirinda farms

(survey 2010).

4.4.5 Lack of effective extension services

Some of the farmers pointed out that they could not get the best of advice and help from

extension workers in their respective areas on time relating to the use of some kind of

implements especially knapsack sprayers due to a shortage of extension workers. The

pool of the extension workers were observed to be outnumbered by the pool of the

farmers in need of their services. With this farmers blamed extension services as one of

the factors hindering them to effectively utilise their capital equipment.

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CHAPTER 5: CONCLUSIONS AND POLICY

RECOMMENDATIONS

5.1 Introduction

This section presents the conclusions drawn from the analysis done. Policy implications

and recommendations arising from the analysis are also discussed.

5.2 Impact of farm mechanization on productivity

Basing on the regression analysis we failed to reject the hypothesis that mechanization

increases productivity at 5% level and hence concluded that mechanization increases

productivity on a farm. The regression coefficient for capital was found to be 766.828

and significant at 10% which shows that capital intensity had a significant contribution

towards productivity. Timeliness of farming operations and efficiency in performing

farming activities like ploughing and harvesting were found to be the major reasons

leading to increased productivity. The results of this study are consistent with other

studies that were carried out during the post green revolution period in India1.

5.3 Impact of farm mechanization on employment of labour

The hypothesis that capital is inversely related to labour use on a farm was rejected at

the 5 % significance level as the results clearly show that they instead move together in

a positive direction. The correlation coefficient of 0.457 was found and it was the basis

for rejecting the initial hypothesis. Such results imply that as capital

equipment/mechanization increases, the use of labour on a farm tends to increase as

well. This was true for farming communities in the Chipinge district studied.

The increase in labour use as the level of mechanization increases was attributed to the

intensification of production that farmers were now adopting with the availability of

1 1 See Singh and Singh (1972), Aggarwal (1983) for further details.

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various capital equipment. The use of some engine powered machinery such as tractors

resulted in an increase in labour as the machines need maintenance and drivers. GIPE

Poona (1967) concluded that use of machinery especially tractors generated greater

demand for labour by facilitating more intensive cultivation. Thus there was no

significant displacement of human labour after tractorisation.

5.4 Policy recommendations

From the study farm mechanisation was found to be effective in enhancing farm

productivity and reducing the drudgery of the farming community. From a sustainability

point of view it is recommended that the rate of mechanising agriculture in Zimbabwe

especially to the just resettled farmers both communal and commercial be continued as

a means of improving productivity on the farms. With the aim of restoring the status of

the country being the Bread Basket of Southern Africa mechanization remains a

positive move in reviving agricultural production taking note that production can be

increased by either putting more land under cultivation or increasing the productivity of

the land already under cultivation which is mostly possible with mechanization.

Mechanization is however demand driven: it is the farmer who will decide what machine

to buy or use, from whom to buy and how to use it (FAO 1997). Experience has shown

that mechanization must be left to the private sector as much as possible. Government

should not be actively involved in the manufacture, import, distribution, and repair of

agricultural machinery and its operations but provide the incentives for private sector

response.

It has also been shown that a lot of factors are hindering effective use of the various

farming implements that maybe at their disposal. With this in mind it is also

recommended that farmers be advised to seek training in agricultural colleges in order

to make proper use of the implements at their disposal. It is also recommended that

Government reduce the ratio of farmer to extension worker by deploying more extension

workers that will assist farmers in maximising benefits associated with the use of farm

equipment.

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Mechanization alone cannot increase productivity and or production. It is a

complementary input that works best with other inputs such as fertiliser, pesticides,

irrigation and high yielding seed varieties. Most resettled farmers lack adequate credit

facilities to buy other inputs as mentioned and therefore it is highly recommended that

reliable credit facilities with less stringent conditions for acquiring loans be made

available to farmers to ease inputs acquisition problems. Moreover subsidising inputs

for the farmers is also another possible channel that the government should follow to

make sure that the farming community brings production to full swing.

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