Ebere Omeje

TAPHEE, BARNABAS GAIUSPG/PH.D/10/57381

RESOURCE PRODUCTIVITY AND EFFICIENCY OF SMALL- SCALE GROUNDNUT FARMERS IN TARABA

STATE, NIGERIA

FACULTY OF AGRICULTURE

THE DEPARTMENT OF AGRICULTURAL ECONOMICS

Ebere Omeje Digitally Signed by: Content manager’s

DN : CN = Webmaster’s name

O= University of Nigeri

OU = Innovation Centre

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BARNABAS GAIUS

RESOURCE PRODUCTIVITY AND EFFICIENCY OF SCALE GROUNDNUT FARMERS IN TARABA

FACULTY OF AGRICULTURE

THE DEPARTMENT OF AGRICULTURAL

: Content manager’s Name

Webmaster’s name

O= University of Nigeria, Nsukka

OU = Innovation Centre

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RESOURCE PRODUCTIVITY AND EFFICIENCY OF SMALL-SCALE

GROUNDNUT FARMERS IN TARABA STATE, NIGERIA

BY

TAPHEE, BARNABAS GAIUS PG/PH.D/10/57381

A PH.D THESIS SUBMITTED TO THE DEPARTMENT OF AGRICU LTURAL ECONOMICS,

FACULTY OF AGRICULTURE, UNIVERSITY OF NIGERIA, NSUK KA

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF DOCTOR OF PHILOSOPHY (PH.D) DEGREE IN AGRICULTUR AL

ECONOMICS OF THE UNIVERSITY OF NIGERIA, NSUKKA

JANUARY, 2015

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CERTIFICATION

TAPHEE, BARNABAS GAIUS, a postgraduate student of the Department of

Agricultural Economics, Faculty of Agriculture, University of Nigeria, Nsukka, with the

registration number, PG/Ph.D/10/57381, has satisfactorily completed the requirements for

the award of degree of Doctor of Philosophy (Ph.D) in Agricultural Economics

(Production Economics and Farm Management). The work embodied in this thesis is

original and has not been submitted in part or full for any other degree or diploma of this

or any other University.

_________________ _____________ ________________ ____________ Prof. E.C. Okorji Date Dr. F.U. Agbo Date (Supervisor) (Supervisor)

_________________________________ ___________________ Prof. S. A. N. D. Chidebelu Date

(Head of Department) ___________________________ _____________________ External Examiner Date

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DEDICATION

This work is dedicated to my wife Mrs. Domitilla Gaius Taphee, Taphee (Jr),

Midavahphee, Avapheeshiu and the eternal memory of Mr. Barnabas Taphee and Mrs.

Rejoice Barnabas, I love you all.

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ACKNOWLEDGEMENT

I want to acknowledge God the Almighty for the life and strength he gave me to

put this work together. My unfathomable gratitude goes to my supervisors, Prof. E.C.

Okorji and Dr. F.U. Agbo who painstakingly went through the entire work, for proper

guidance. How does someone say “thank you” when there are so many people to thank?

The various contributions of the following are highly appreciated: - Prof. S.A.N.D

Chidebelu (the Head of Department), Dr. A.A Enete Post Graduate Seminar Coordinator,

Prof. (Mrs.) A.I. Achike, Dr. (Ms.) Amaechina, Prof. C.J. Arene, Prof. E. C. Eboh, Prof.

E. D. Arua, Prof. C. U. Okoye, Prof. Noble J. Nweze and. Others include Dr. N. A.

Chukwuone, Dr. P. I. Opata, Dr. B. C. Okpukpara, Mr. R. N. Arua, Mr. P.B.I. Njepuome,

Mr. J. I. Okpara, Mr. A. N. Onyekuru, Mrs. C. U. Ike and Mrs. C. S. Onyenekwe.

Obviously this thesis is a thank you to my father who has been and always my

powerful role model, and to my mother who taught me love and kindness and how to

show same to people. Yet, the people most directly responsible for this thesis becoming a

reality include my wife, Domitilla, who makes my life complete. Domitilla is my partner

in marriage and in life. Without her I will be lost. To my three sons, Master Taphee (Jr.),

Midavahphee a.k.a Babawo (Jr) and Avapheeshiu (Boss) who have endured my absence.

I say thank you, for your patience. To my elder and younger brothers Lazarus Barnabas

and Rev. Irmiya Barnabas, Master Warrant Officer Thomas Barnabas, George Barnabas

and Alexander Barnabas for their inspiration; Ephraim Ibrahim Jen, Anthony Zawati, Dr.

John Z. Dubagari, Dr. Aliyu Bakari, for the gift of relationship and encouragement.

Mention must be made of Mr. Obed J. Mafindi for his immense financial support

towards the realization of my academic dream.

I would especially like to thank Reuben Jonathan for long and enjoyable talks on

and about research and for his companionship during our stay; my Cousin brothers ASP

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Timothy D. Andrew and Thomas Audu for their moral and financial supports. In

particular, I would like to seriously thank Mr. James Titus M. for picking up the pieces

of This thesis and knitting them together.

To my colleagues in the College of Agriculture, Jalingo, particularly those in the

Department of Agricultural Extension and Management, thank you for your moral

support. And to my numerous friends and well wishers who for one reason or the other

your names did not appear, I say thank you and God bless you all.

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TABLE OF CONTENTS

Title Page i

Certification ii

Dedication iii

Acknowledgement iv

Table of Contents vi

List of Tables ix

List of Figures x

Abstract xi

CHAPTER ONE: INTRODUCTION

1.1 Background of the Study 1

1.2 Statement of Problem 4

1.3 Objectives of the Study 6

1.4 Hypotheses of the Study 6

1.5 Significance of the Study 6

1.6 Limitation of the Study 8

CHAPTER TWO: LITERATURE REVIEW

2.1 The Concept of Resource Productivity 9

2.2 Concept of Efficiency 9

2.2.1 Technical Efficiency (TE) 10

2.2.2 Allocative Efficiency (AE) 10

2.2.3 Economic Efficiency (EE) 10

2.3 Conceptual Framework 10

2.4 Groundnut Production and Its Trend 13

2.5 Cost Efficiency 15

2.6 Theoretical Framework 16

2.7 Analytical Framework 17

2.8 The Stochastic Frontier Production Function 19

2.9 Stochastic Frontier Analytical Techniques of Efficiency Measurement 24

2.10 Economic Approaches for Examining Factors Influencing Efficiency from Stochastic Frontier Analysis (SFA) 25

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2.11 Production Efficiencies and their Determinants: Empirical Evidence 26

2.12 Cost and Returns in Agricultural production 34

2.13 Socioeconomic Characteristics of farmers 36

2.14 Factors Affecting Groundnut Production 40

CHAPTER THREE: RESEARCH METHODOLOGY

3.1 The Study Area 42

3.2 Sampling Procedure 43

3.3 Data Collection 43

3.4 Data Analysis Techniques 43

3.4.1 The empirical stochastic Frontier Production Model 44

3.4.2 The empirical Stochastic Frontier Cost Production Model 46

3.4.3 Profitability analysis using budgeting techniques 47

3.4.4 Profit function (⌅) 48

CHAPTER FOUR: RESULTS AND DISCUSSION

4.1 Socio-Economic Characteristics of Groundnut Farmers 50

4.1.1 Age of groundnut farmers 50

4.1.2 Gender of the groundnut farmers 50

4.1.3 Marital status of groundnut farmers 50

4.1.4 Household size of the respondents 50

4.1.5 Level of education of groundnut farmers 51

4.1.6 Extension contact of the respondents 51

4.1.7 Farming experience of the respondents 52

4.1.8 Farm size of the groundnut farmers 53

4.1.9 Labour source of the respondents 53

4.1.10 Source of finance of the groundnut farmers 53

4.2 Effects of Socio-economic Characteristics of Groundnut farmers 56

4.3 Determination of Technical, Allocative and Economic Efficiencies

of Groundnut Farmers 59

4.3.1 Technical efficiency of groundnut farmers 59

4.3.2 Determination of allocative efficiency of groundnut farmers using

stochastic cost functions 61

4.3.3 Frequency distribution of allocative efficiency 63

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4.3.4 Frequency distribution of economic efficiency of groundnut farmers 63

4.4 Profitability Analysis of the Groundnut Farmers 65

4.5 Profit and Cost Relationship in Groundnut Production 66

4.6 Constraints Associated with Groundnut Product 70

CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIO NS

5.1 Summary of Findings 73

5.2 Conclusion 74

5.3 Recommendations 75

5.4 Contributions to Knowledge 76

5.5 Areas for Future Research 76

REFERENCES 77

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LIST OF TABLES

Table 1.1: Trend in Groundnut Output in Taraba State, Nigeria (2002 – 2012) 2

Table 2.1: Trend in Groundnut output in Nigeria (1961 to 2005) as modified 14

Table 3.1: Study Location and Sample Chosen 43

Table 4.1: Distribution of Respondents according to their Socio-economic Attributes (n = 270) 55

Table 4.2: Maximum Likelihood Estimate (MLE) of Stochastic Frontier Production Function for Small-Scale Groundnut Farmers 69

Table 4.3: Technical Efficiency Distribution of Groundnut Farmers 60

Table 4.4: Maximum Likelihood Estimate (MLE) of Stochastic Cost Function for Groundnut Farmers 62

Table 4.5: Allocative Efficiency Distribution of Groundnut Farmers 63

Table 4.6: Economic Efficiency Distribution of Groundnut Farmers 64

Table 4.7: Average Costs and Returns of Groundnut Farmers per hectare 66

Table 4.8: Summary of Regression Analysis (n = 270) 69

Table 4.9: Distribution of Respondents Based on Constraints Associated with Groundnut Production 72

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LIST OF FIGURES

Figure 2.1: Best practices, potential absolute frontier and measure of inefficiency 13

Figure 2.2: Farrell’s Efficiency Measure 20

Figure 2.3: Stochastic frontier production function 23

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ABSTRACT The study investigated resource productivity and efficiency of small scale groundnut farmers in Taraba State, Nigeria. The focus was on socio-economic attributes of the small scale groundnut farmers and the effects on their efficiency, determination of technical, allocative and economic efficiency of the respondents, profitability of groundnut production, and factors that influence production costs and problems of groundnut production. A total of 270 small-scale groundnut farmers were selected in 9 local government areas of the State. Structured questionnaire and interview schedule were used as instruments for data collection. Types of data collected were those on socio-economic characteristics, production, costs of production, yield and sales and problems of groundnut farmers within the local government areas. Data analysis was achieved by the use of descriptive and inferential statistics; Stochastic Frontier Analysis (SFA), Stochastic Frontier Cost Function, profit (⌅) function and gross margin analysis. The mean scores for literacy level, household size, farming experience and farm size were 9 years, 5 persons, 9 years and 1.60ha, respectively. The Maximum Likelihood Estimates (MLE) result of the stochastic frontier production function (SFPF) for groundnut farmers indicated the presence of inefficiency. Farm size and other agrochemical were significant at 1% Level of Probability (LOP), seed was significant at 5% LOP and family labour was significant at 10% LOP. In the efficiency effects, farming experience and household size were both significant at 1% LOP; extension contact and literacy level were significant at 5% LOP. The mean Technical Efficiency (TE) was 77%. The MLE of stochastic cost function for groundnut production also indicated the presence of inefficiency. The cost of seeds, fertilizer, family labour and ploughing were statistically significant at varying degrees of probability implying they were important determinants of the total cost associated with the production of groundnut. Farming experience, literacy level and household size were significant and positively related to costs efficiency among the sampled farmers. The mean allocative efficiency was 0.695 (70%) indicating that the respondents were not allocatively efficient. Mean economic efficiency was 0.54 (54%) implied that, the sampled groundnut farmers were not economically efficient in the use of productive resources. The gross margin was N47, 265.16 per hectare and return on investment was N0.29. Cost of seeds, transport, labour (family and hired) and storage significantly (P<0.01) affected profit margin. Major constraints identified included pests and diseases infestation (19.10%), lack of storage facilities (13.57%), inadequate research and extension services (10.86%), low price (10.77%), and inadequate credit facilities (9.50%). Remedial measures such as: loans and other credit facilities be given to farmers at reduced interest rate, farmers be encouraged to form cooperative groups, revitalization and prioritizing funding of extension delivery system and modern storage facilities were suggested.

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CHAPTER ONE

INTRODUCTION

1.1 Background of the Study

Groundnut (Arachis hypogaea Linnaeus) commonly called poor man’s nut

(Amiruzzanman and Shahjahan, 2003; Beghin, Diop, Matthey and Sewadah, 2003) is a

member of the genus Arachis in the family leguminosae (fabacaea). Other members of

this family include (cowpea), (soybean), (pigeon pea) and (melon).

Groundnut originated from Latin America and the Portuguese were responsible

for its introduction into West Africa from Brazil in the 16th century (Abalu & Etuk, 1986;

Hamidu, Kudi & Mohammed, 2006). The crop is now widely cultivated throughout the

tropical, sub-tropical and warm temperate areas (Hamidu et al., 2006). According to

Ntare, Waliyar, Ramouch, Masters & Ndejunga, (2005), the production of groundnut in

Nigeria started around 1912. This was in response to the high world prices, hence, made

Nigeria to be prominent among the exporter of groundnut and took the lead as the largest

producer and exporter of groundnut in the sixties. Nigeria reached a peak production of

1.6 million metric tons in 1973, but production fell by almost half of the 1973 figures in

less than a decade due to a combination of two important factors (Ntare et al., 2005).

First the drought of 1974/75 growing season accompanied by aphid infestation which

wiped out more than 750,000 hectares of groundnut fields. Secondly, the coincidence of

oil boom in Nigeria about the same time (Ntare et al., 2005).

Groundnut is grown in nearly 100 countries in the world. Major groundnut

producers are China, India, Nigeria, USA, Indonesia and Sudan. Developing countries

account for 96% (26 million ha) of the global groundnut area with 92% global

production (Food and Agricultural Organisation [FAO], 2004). Ashley (1993) revealed

that Nigeria unshelled nut is estimated about 2.6 metric tons annually.

Rainfall of 500mm-1000mm with temperature range of 250C to 300C will allow

for commercial productivity (Weiss, 2000; Department of Agriculture [DOA], 2008).

The productivity of groundnut is higher in well drained soils with pH between 6.0 – 6.5

particularly sandy loam soil, as it is light, thus, helps for easy penetration of pegs and

their development, hence, their harvesting (Gibbon & Pains, 1985; Simonds, 1976;

Yayock, 1984; Ambrose et al., 1986; Larinde, 1999).

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Groundnut is indeed one of the commercial crops in Nigeria which accounted for

70 percent of the total Nigeria’s export earning between 1956 and 1967, but declined

between 1968 and 1980’s (National Planning Commission/Raw Material Research

Development Council) (NPC/RMRDC, 2002). Despite the availability of abundant land

and human resources in Nigeria, yield per hectare from groundnut production has been

declining over the years and there is a shortfall of over 90 percent of groundnut

requirement by the companies involved in processing as revealed by (RMRDC, 2004).

The trend could be as result of either the small-scale groundnut farmers are

resource poor or are inefficient in resource (inputs) allocation and utilization, since the

output of groundnut in the study area did not commiserate with total hecterages put

under cultivation as can be seen in Table 1.1 (Trend in groundnut output in Taraba State,

Nigeria between 2002-2012) Taraba Agricultural Development Programme (TADP,

2013).

Table 1.1: Trend in Groundnut Output in Taraba State, Nigeria (2002 – 2012)

Year Area cultivated by small holder farmers in (’000HA) Production in Mt 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

28 31.92 102.00 107,01 114.80 117.80 115.60 117.70 114.80 117.80 117.70

48 53.76 178.00 161.50 181.50 182.06 181.80 182.20 181.70 183.20 182.20

Source: Project Monitoring and Evaluation Unit. TADP Jalingo, 2013

Nigeria agriculture is dominated by the small-scale farmers who are low income

earners and provide 2/3 (two-thirds) of the total food production in the country (Usman,

2006) but productivity of food crops output remained low (Nweze, 2002). As a result the

rural income is lower today than it was two decades ago and agricultural exports are

almost non-existent, also production efficiency techniques have remained rudimentary

for the main cropping system despite years of works on technology generation (Federal

Ministry of Agricultural and Rural Development [FMARD], 2001). This wide food

deficit has been attributed to resource productivity and efficiency (Onyenwaku, 1987;

Okuneye, 1988). The aftermath of this trend has always been gross inability to attain self

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sufficiency in food production as the sector becomes dormant and neglected (Argbokan,

2001).

Ntare (2005) stated that small-scale farmers’ access to new crop varieties has

long been recognized as a critical step for increasing agricultural productivity in sub-

saharan Africa. He opined that, adoption of improved varieties that resist pests, diseases

and drought can often vary in yield even when farmers are unable to adopt more costly

inputs such as agrochemicals.

Freeman et al., (1999) asserted that small-holders groundnut farmers are faced

with lack of resources or access to currently available technology, as a result, the authors

observed that, low producers’ prices and limited modeling opportunities reduced

incentives for small-scale groundnut farmers to invest in productivity enhancing

technologies such as improved seeds, fertilizer and pesticides.

Awoke (2003) identified lack of improved capital inputs, collaterals and high

interest rates as some of the major obstacles to groundnut production efficiency.

Effective planning should aim at imparting into farmers certain knowledge to match the

technical aspect of production, so that they would minimize the input of scare and

expensive resources consistent with obtaining the level of output beyond which no

further profit is possible, this would involve the efficient management of productive

resources aside from the target crop (Awoke, 2003).

The predominant reliance in traditional methods of farming by Nigeria small-

scale farmers for substantial part of our agricultural production activities has been partly

responsible for present low level production as against our increasing population

(Ohikere, 2010). The author said that the need for improved food production stemmed

from the fact that, it is, the first way to match conspicuous consumption with

conspicuous production in view of the ever increasing population with rising food

demand.

Food crops production efficiency is vital to improvement of the agricultural

sector productivity if resources available are judiciously used. Many resources are

employed by the small-scale farmers at the farm-level with attendant low output. Since

increased productivity is directly related to production efficiency. It is therefore

important to know how productivity of the small-scale groundnut farmers will be raised

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in order to help them reduce inefficiency. Efficiency measurement is very important for

monitoring productivity growth. Thus, it ascertains the extent to which if possible to

increase productivity using present resource base and available technology and this can

help in policy formulation on the reduction of inefficiencies visa-vis groundnut

production efficiency.

1.2 Statement of Problem

Groundnut is the 13th most important food crop, 4th in oil seed crop and also 3rd

most important of the world source of vegetable protein after soybean, rapeseed and

cotton seed (FAO, 2006; Foreign Agricultural Service [FAS], 2010). The seed (kernel)

contains 40-50% fats, 20-50% protein and 10-20% carbohydrates (FAO, 2006). About

80% of edible groundnuts are roasted for further processing into snacks food, and peanut

butter (GSP NEWS, 2004). It can be crushed for oil and groundnut cake (animals feeds)

(Beghin et al., 2003). Groundnut is also good source of minerals such as phosphorus (P),

calcium (Ca), magnesium (Mg), and potassium (K), as well as vitamins E, K and B

(RMRDC, 2004). Production in Africa has been estimated at about 2.6 metric tons

annually from a land area of approximately 2.5 million hectares with Nigeria inclusive. It

is estimated also, that, 78 percent of land area sown to groundnut is in various crop

association (Okigbo & Greenland, 1976; Nnadi & Haque, 2003).

Although food security remains a major concern due to the subsistence nature of

the country’s agriculture as asserted by Nwafor (2008), the sector employs more than

70 percent of the labour force, accounts for over 70 percent of the non-oil export and

most importantly provides 80 percent of the food needs of the country (Faburso and

Agbonlahor, 2007; CNB, 2009).

Nigeria population which grows at about 3.2 percent per annum with food

production at about 2.0 percent is not keeping pace with its population (FAO, 2005;

NBS, 2011). Food production process requires resources which when used judiciously

could lead to high productivity and profitability. These resources could be natural or

manmade: man-made resources include: labour, capital or entrepreneurship, which are

supplied and influenced by man (Olayide & Heady, 1982; Oyekele, Bolaji and Olowa,

2009).

In order to ameliorate the dwindling and not too impressive performance of

agricultural sector in terms of the gap between food supply and demand owing to

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population growth rates, successive governments in the past had come up with different

programmes and policies. Among them are: National Fadama Development Project

(NFDP), Root & Tubers Expansion, Directorate of Food and Rural Infrastructure

(DFRRI), National Agricultural Land Development Authority (NALDA) etc (Oredipe

and Akinwumi, 2002). These programmes and policies were aimed at raising the

productivity and efficiency of agricultural production, but many of these programmes

and policies have not yielded tangible result. The decline in groundnut production in the

country is worrisome and a real challenge to Government with a population of over 160

million to feed.

Previous studies on efficiencies of small scale farmers provide a variety of

results. Lau and Yutopoulous (1971) using the profit function equation found that small

scale farms attained higher productivity than larger farms in India. Sahidu (1974)

adopted Lau Yutopoulous model to sample wheat farms in India and came up with a

contrary conclusion showing large and small farms exhibiting equal level of

productivity. Khan and Maki (1979) using Lau-Yutopoulous model in Pakistan observed,

however that large farms were more efficient than small farms using a normalized profit

function. Ajibefun, Battse & Darammola (2002) and Mbata (1988) showed that large

farm size enhanced productivity among farmers in the dry savannah and hummid forest

agro-ecological zones of Nigeria. Other studies point to the socio-economic

characteristics of small scale farmers themselves as the major determinants of their

technical and resource use efficiency (Ajibefun, 2006; Darammola and Falusi, 2006;

Shehu and Mshlia, 2007; Otitoju and Arene, 2010, and Mamman, Agbo and Ebe, 2014).

Other studies in Nigeria which isolated farm specific characteristics as major

determinants of efficiency include Giroh and Adebayo (2009), Micheal (2011), and

Omolahim and Ibrahim (2011). Some studies focused specifically on the technical

efficiency of groundnut farmers (Tashakalma, 2010) considered resource poverty as the

main reason for technical inefficiency. Whether these findings are true of groundnut

farmers in Taraba State, Nigeria is yet to be confirmed.

In view of the strategic importance of the crop, groundnut in Nigeria as a major

source of vegetable oil and protein, there is the need to investigate the resource

productivity and efficiency of groundnut farmers. In doing so, the socio-economic

characteristics of these farmers need to be investigated with respect to their effects on the

efficiencies of the farmers. Constraints to the achievement of optimal technical,

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allocative, economic and profit efficiencies of groundnut farmers need also to be

investigated and documented. Taraba State with 75% of her 54,475 square kilometers of

arable land suitable for groundnut production (NRD, 2007; TADP, 2013) and favourable

whether with average annual rainfall above 500mm and abundant sunshine provides a

suitable environment for this study.

1.3 Objectives of the Study

The broad objective of this study was to investigate the resource productivity and

efficiency of small-scale groundnut farmers in Taraba State, Nigeria. The specific

objectives were to:

i. describe the socio-economic characteristics of small-scale groundnut

farmers;

ii. determine the effects of socio-economic characteristics on efficiencies of

groundnut farmers;

iii. determine the technical, allocative and economic efficiencies of

groundnut farmers;

iv. determine the costs and returns of groundnut production in the study area;

v. analyse the influence of production costs on profit in groundnut

production;

vi. identify constraints associated with groundnut production; and

vii. make recommendations for improving groundnut production efficiency.

1.4 Hypotheses of the Study

The following null hypotheses were tested

i. Socio-economic characteristics of small-scale groundnut farmers have no

influence on their efficiency.

ii. Groundnut farmers in the study area are efficient.

iii. The production costs of groundnut have no influence in profit realised.

1.5 Significance of the Study

Despite the dominance of the oil sector in government revenues and foreign

exchange earnings, agriculture (crops, livestock, fisheries and forestry) constituted the

largest single share of the national output, incomes and employment, suffice to say that,

it does not only account for the share GDP, but also is the backbone of the rural

livelihoods (Eboh, 2011). It is a fact, that, Nigerian mineral oil though contributed about

7

90% of the total revenue generation, but in terms of GDP, its contribution stands at 10%

(Federal Government of Nigeria [FGN], 2012).

Kuye et al., (2004) asserted that, the most difficult problem in Nigeria today is

how to provide good livelihood for the rural people through increased productivity. The

need for agricultural growth through productivity improvement as stated by (Xinshen et

al., 2009) is paramount in the updated strategy, because of the weak performance of

agriculture since 1960. Nweze (2002), Panwal et al., (2006) and Adinya et al., (2008b)

asserted that low output realized by small-scale holders (farmers) is an indication that

resources needed in the production of crops are not at optimal levels. Spencer (2002) in

his contribution stated that resource-poor farmers must be assisted to rise beyond

subsistence to increase their income through more efficient use of resources in order to

achieve optimum production level. Alimi, (2000) opined that productive resources must

be available and efficiently used.

Although successive government in Nigeria have come up with various

programmes and policies to achieve food self-sufficiency in order to bridge the gap

between the demand for and supply of food to meet the ever increasing population, but

the outcomes are not encouraging, thus, the productivity of the food crops by the small-

scale farmers remained low.

The need to investigate the resource productivity and efficiency of small-scale

groundnut farmers in Taraba State, Nigeria becomes necessary owing to the demand by

the local companies involved in the processing of groundnut and in addition to foreign

exchange earnings (RMRDC, 2004). The study becomes necessary too, because, inspite,

of the potentialities of the State, in terms of the soil fertility and rainfall patterns which

favour the growth of the crop, the yield per hectare of the farmers are low (Taraba

Agricultural Development Programme [TADP], 2013). Also, the efficiency with which

available resources and technology used by the small-scale farmers becomes a matter of

concern for investigation. An underlying premise is that, if farmers, most especially the

small-scale farmers are not efficient in the use of the existing technology, then, efforts

designed to improve efficiency would be more cost effective than introducing new

technologies as a way of increasing output (Shapiro, 1983; Belbase & Grabowski, 1985).

It is expected that the findings of this study will provide useful information and

technical advice to the small-scale groundnut farmers in the study area. It will also help

8

in identifying their production constraints and proffer recommendations with a view to

increasing their productivity visa-vis their income level as well improving their standard

of living. The study will thus assist in establishing the efficiency with which the small-

scale groundnut farmers allocate their resources in groundnut production.

Moreso, the results of this study are expected to give direction to policy makers

in designing appropriate public policies to increase groundnut productivity in Taraba

State and indeed Nigeria in general. The results of this study will help agricultural

planners in Agricultural Development Programmes (ADPs) and Ministry of Agriculture

in the State and Nigeria as a whole in their planning activities as regards efficiency of

available resources for increased productivity. Researchers will also benefit immensely

from the results of the study as a good resource base.

1.6 Limitation of the Study

This study focuses on resource productivity and efficiency of small-scale

groundnut farmers. Thus, it is limited in scope and resources. Data were collected on 270

small-scale groundnut farmers representing the three dominant zones (Zing, Wukari and

Takum) in Taraba State, Nigeria that were selected for the study. In terms of scope, the

study focused on only small-scale groundnut farmers and the data collected were for one

cropping season.

Limitation on data collection was faced most especially on issue of questionnaire

administration. The respondents were interviewed all through because of the importance

of every information stated in the questionnaire. Also the information provided by the

farmers used in this study was mainly from memory recall as many of them do not keep

records of operation. This made the collection of data to take more time than necessary.

Inspite of these limitations, the data were error free due to omission of relevant

information for the study.

9

CHAPTER TWO

LITERATURE REVIEW

2.1 The Concept of Resource Productivity

Agricultural productivity is the index of the ratio of total output to input used in

the production process which is synonymous with resource use productivity (Olayide and

Heady, 1982). The computation of this important productivity statistics can be achieved

from analysis of production functions. Such productivity statistics include the Average

Product (AP), Marginal Product (MP), Marginal Rate of Substitution (MRS), Elasticity

of Production (EP), and Return to Scale (RTS). The analyses have made the delineation

of three economic stages of production function that enable us to know the point of

efficient utilization of resources in the production process possible. Thus, the study of

productive efficiency started with the pioneering work of Farrell (1957) in Thiam et al.,

(2001).

2.2 Concept of Efficiency

Efficiency analysis is an issue of interest given that the overall productivity of an

economic system is directly related to the efficiency of production of the component

within the system. Thus, the concept of efficiency becomes a central issue in production

economics. The analysis of efficiency is generally associated with the possibility of farm

producing a certain optimal level of output from a given bundle of resources or certain

level of output at a least cost. The analysis falls into two categories: Parametric and Non-

parametric. The parametric approach relies on a parametric specification of the

production function while the non-parametric imposes a non-parametric restriction to the

underlying technology (Adewumi and Okunmadewa, 2001).

Efficiency of a production system or unit connotes a comparison between

observed and optimal values of its output and inputs (Ogunjobi, 1999). The comparison

can be in the form of the ratio of observed to maximum potential output obtainable from

the given input or the ratio of minimum potential to observed input required to produce

the given output or the combination of the two. In these two comparisons, the optimum

is defined in terms of production possibilities, while efficiency is technical. It is also

possible to define the optimum in terms of the behavioral goal of the production unit. In

this case efficiency is economic and is measured by comparing observed and optimum

10

cost, revenue, profit or whatever the production unit assumes to pursue, subject to the

appropriate constraints on qualities and prices (Schmidt & Lovell, 1979; Battesse, 1992).

2.2.1 Technical Efficiency (TE)

Technical Efficiency (TE) is the achievement of maximum potential output from

a given quantity of input under a given technology. Thus, it is the attainment of

production goals without wastage as stated by (Jandrow et al., 1982; and Amaza &

Olayemi, 1999). Technical efficiency is defined as the ability to achieve a higher level of

output given similar level of production inputs.

Russel and Young (1983) stated that technical efficiency arises when less than

maximum output is obtained from a given combination of factors. The authors further

stressed two measures of technical efficiency; these are Timmer measures of technical

efficiency as the ratio of actual output to potential, given the level of input used on farm

and Kopp measures the technical efficiency, compares the actual output of farm to the

given ratio of the same input usage. Both measures yield substantial similar results.

2.2.2 Allocative Efficiency (AE)

Allocative efficiency has to do with the extent to which farmers make efficient

decision by using inputs up to the level at which their marginal contribution value in

equal to the factor cost (MVP = MFC).

2.2.3 Economic Efficiency (EE)

Economic efficiency combines both the technical and allocative efficiency. It

occurs when a firm chooses resources and enterprises in such a way to attain economic

optimum (Adesina and Djato, 1997).

2.3 Conceptual Framework

The crucial role of efficiency in increasing agricultural output has been widely

recognized by researchers and policy makers alike (Amaza ad Olayemi, 2002: Adesina

and Djato, 1997; Coelli et al, 2002). Since there are alternatives means of attaining

production goals and objectives, the theory of production presents the theoretical and

empirical framework that facilitates a proper selection among alternatives so that any one

or a combination of farmers’ objectives can be attained (Olayide & Heady, 1982).

11

Olayemi (2004) summarized the production decisions that confront any producer or

entrepreneur into the following:

(i) What and how much to produce?

(ii) What and how much input to use?

(iii) How much to combine input to maximize profit? and

(iv) How much to combine enterprises to maximize profit?

Also since one of the main objectives of any society is the attainment of an

optimal standard of living with a given amount of effort, any increase in productivity of

resources employed in farm production amounts to an increase in the standard of living.

Increase in agricultural productivity will therefore contribute to the well-being of the

economy as well. The ultimate objective and the interest of economist in productivity

should be to find ways of increasing output per unit of input and of attaining desirable

inter-firm, intra-firm and inter-sector transfers of production resources, thereby,

providing the means of raising the standard of living. The input – output process of

production according to (Olayide and Heady, 1982) is important in at least four major

problems areas

1) The distribution of income,

2) The allocation of resources,

3) The relation between stocks and flows, and

4) The measurement of efficiency or productivity.

A meaningful assessment of productivity depends upon a clear and precise

definition of inputs and output in such a way that their movements over time are not

equal. Determining which inputs and output are constant with the particular concept of

production in question is important. Sometimes, one is faced with separate and distinct

conditions when measuring labour, capital or land productivity. In other words, resource

productivity, productive efficiency, therefore, determines the extent to which it is

possible to raise productivity by improving the neglected resources (Tadesse &

Krishnamourthy, 1997). Using this definition as a benchmark, a change in productivity

over time will depend upon change in the types and quantities of inputs. Thus, an

increase in farm output will result from one of the these forces;

12

1) An increase quantity of inputs with no change in output per unit of input.

2) An increased productivity of input with no change or a decrease in quantity of

inputs and

3) A combination of changes in inputs and productivity. Therefore this situation

makes the concept of efficiency (productivity) a central issue in production

Economics.

4) The production frontier serves as a standard against which to measure the

efficiency of production. It should contain only the efficient observations

(Kebede, 2001).

The level of technical efficiency of a particular farmer is characterized by the

relationship between observed production and some ideal or potential production

(Greene, 1997, 2000, 2003). The measurement of firm-specific or farmers-specific

efficiency is based upon derivation of observed output from the best production or

efficient production frontier. If a farmer’s actual production point lies on the frontier, it is

perfectly efficient. If it lies below the frontier then it is technically inefficient, with the

ratio of the actual to the potential production defining the level of efficiency of the

individual farmer (Fig. 1), for example, Oa, Ob is a comparison of output at points Co and

Cb, each with the same level of input but Cb lying on the best practices frontier function

Qb (passing through 100% - efficient sample point) whilst Co lies on Qo which represents

a level that is a neutral-shift of the frontier Qb and passes through the Co in figure 2.1.

The concept could be measured relative to other frontier, for example the

absolute frontier Qa lying above all sample points. Here, the ratio will be Oo/Oa or a

comparison of output at points Ca on Qa and Ca. The potential absolute frontier is also

presented by Qp, which is the maximum output obtained from all conceivable

observations embodying the current technology (including over all periods that take

place) and it lies above Qa. Over time, there would be sequence of absolute frontier

functions Qa (and associated levels of technical efficiency) moving up to the potential

absolute frontier function Qp (Ogundele & Okoruwa, 2006; Okoruwa & Ogundele,

2006).

The production frontier has a property of economies of scale: Constant Returns to

Scale (CRS), Decreasing Returns to Scale (DRS) and Increasing Returns to Scale (IRS)

(Kebede, 2001). Farrell’s definition of technical efficiency led to the development of

13

methods of estimating the relative technical efficiencies of farmers. The common feature

of these estimation techniques is that, information extracted from extreme observations

from a body of data is by determining the best practice production frontier (Lewin &

Lovell, 1990). From this, the relative measure of technical efficiency for individual

farmer can be derived.

Fig. 2.1: Best practices, potential absolute frontier and measure of

inefficiency.

Source: Okoruwa and Ogundele (2006).

2.4 Groundnut Production and Its Trend

Groundnut (Arachris hypogeal I) is an annual legume which is also known as

monkey nut, peanut etc. (Beghin et al; 2003). Cultivated groundnut originated from

South America (Weiss, 2000). It is one of the most popular and universal crops

cultivated in more than 100 countries in the sixth continents (Nwokolo, 1996). Also

(Hill, 1979; Purseglove, 1982; Pompeu, 1980; Hammon, 1994; Larinde, 1999; Freeman

et al, 1999) held the same view. It is believed to be native of Brazil, Peru, Argentina and

Ghana, from where it was introduced into Jamaica Cuba and other West Indies Islands.

The Portuguese introduced the crop into Africa from where it was introduced into North

America. Groundnut was introduced into India during the first half of the 16th century

from one of the pacific Islands of China where it was introduced earlier from either

central or South America. Agboola (1979) as cited by Tashikalma (1988) stated that

groundnut was introduced into Nigeria after the 16th century. The major countries

producing groundnut according to Mahmoud et al, (1992) include India, China, USA,

Nigeria, Indonesia, Senegal, Sudan, and Burma. FAO (2006) revealed that groundnut is

grown in 25.2 million hectares with the total production of 35.9 million metric tons. It is

Oa

Ob

Oo

O

Output

Cb

Co

Qb = absolute frontier

Op = potential absolute frontier

Qb = best practice

Qo = inefficiency

Inputs

14

also reported by (FAO, 2006), that, India produced (26%), China (19%), and Nigeria

(11%). The cultivation of groundnut according to the author report was confined to the

tropical countries ranging from 400N to 400S. FAO (1993) as cited by Itzge et al, (2000)

submitted that about 1.0 million hectare of groundnut is grown in Nigeria with an

average yield of 1.1 metric tons per hectares and an average total production of

1,340,000 metric tons form 1999 – 2001 periods.

According to (RMRDC, 2004) the total hectares put to groundnut production for

twenty four states out of the 36 states and Abuja amounted to 708,319 hectares with a

range of between 50,000 hectares in Edo state to 84,712 hectares in Bauchi state with a

range of 47.00 to 73.00 tons and a mean output to 5.70 tons.

Table 2.1: Trend in Groundnut output in Nigeria (1961 to 2005) as modified

Year Average Harvested Area (m) Average yield (kg/ha) Average production (mt) 1961-1963 1964-1966 1967-1969 1970-1972 1973-1975 1976-1978 1979-1981 1982-1984 1985-1987 1988-1990 1991-1998 1994-1996 1997-1999 2000-2002 2003-2005

1660,333.333 2,161,000 2,010,000

1899,333.333 1769,333.333

719,000 572,333.333

2,376,333.333 661,333.333

738,000 1,088,000 1,868,000

2,505,666.667 2,729,333.333

2806,000

1108,667 854.333 877.333 759.667

604 816 886

913,667 1,109

1,452.333 1,209.333 941.333

1,061.333 1,012.333 973.333

1,821,666.667 1,840,000 1,739,000

1,437,333.333 1,090,333.333 587,666,667 502,666.667 518,333.333 734,666.667

1,066,333.333 1,327,000 1,770,000 2,653,000 2,761,000

2,844,666.667

Sources: FAO, 2004, FMA, 2005, Taphee, 2008

Agboola (1979) asserted that cultivation for export was gingered by increased

demand coupled with the extension of railway line in Kano in 1912 since then groundnut

became an important cash crop of the guinea Savannah Zone and have contributed

increasingly in foreign exchange. Also Olayide and Olatunbosun (1976) revealed that the

high demand for groundnut in Europe export market during the 50th developed the crop

into a main crop in Northern Nigeria making its production to be doubled.

Diop et al., (2004) reported that between 1980 and 1982, 2000 and 2002, Nigeria

recorded 1,026 and 412 metric tons respectively of shelled groundnut. The authors stated

15

that, China is the world’s largest exporter of groundnut accounting for about 32 percent

of the world’s export, earning currently more than $ 250 million annually, the United

States is the second largest exporter with 19% of world market earning currently more

than $ 180 million annually, followed by Argentina at 10-50 percent, Sub-Sahara Africa

(the Senegal, Gambia, Nigeria, Malawi, Sudan and South Africa) accounted for only 5

percent of the world trade.

2.5 Cost Efficiency

We assume that producers face input prices WERN …. and seek to minimize the

cost WTx they incur in producing the outputs YERM….. they choose to produce. The

standard against which their performance is evaluated shifts from the production frontier

to the cost frontier. We will see that the achievement of input oriented technical

efficiency is necessary, but not sufficient, for the achievement of cost efficiency. This is

because a technically efficient producer could use an inappropriate input mix, given the

input prices it faces.

A measure of cost efficiency is a function CE (y, x, w) = c(y:w)/wTx. The

measure of cost efficiency is given by the ratio of minimum cost to observed cost. Thus,

the measure of cost efficiency is bounded between zero and unity and achieve it upper

bound if and only if, a producer uses cost minimizing input vector. The measure is

homogenous of degree – 1 in inputs (eg cost efficiency), increasing in outputs and

homogeneous of degree 0 in input prices (eg. a doubling of all input prices has no effect

on cost efficiency).

It is apparent that not all cost inefficiency is necessarily attributable to technical

inefficiency. Using the definition, the input-oriented technical efficiency of the producer

being examined is given by TE, (yA. xA) = ϴA = WAT (ϴA XA)/WAT XA. Thus, cost

efficiency is given by the ratio of expenditure at xE (which is equal to expenditure at xB)

to expenditure XA, whereas input-oriented technical efficiency is given by the ratio of

expenditure at ϴA XA to expenditure at XA. The remaining portion of cost inefficiency is

given by the ratio of expenditure at XE to expenditure at ϴAXA, and is attributable to a

misallocation of inputs in light of their relative prices.

A measure of input allocative efficiency (AE) is a function, (y.x.w) =

CE(y.x.w)/TE, (y.x). Thus, a measure of input allocative efficiency is provided by the

16

ratio of cost efficiency to input-oriented technical efficiency. AE,(y.x.w) is bounded

between zero and unity, and attains its upper bound, if and only if, the input vector can

be radically contracted to the cost minimizing input vector. AE,(y.x.w) is also

homogenous of degree 0 in input quantities and input prices, being dependent on the

input mix on relative input prices.

2.6 Theoretical Framework

The theory reviewed in this study is the theory of production. Production thus, is

the process of transformation of a set of inputs to output. The economic theory of

production provides the analytical framework for most empirical research on

productivity and efficiency. Production efficiency means the attainment of production

goals without waste. Beginning with this basic idea of “no waste”, economists have built

up a variety of theories of efficiency. The fundamental idea underlying all efficiency

measures, however, is that of the quantity of goods and services per unit of input.

Consequently, a production unit is said to be technically inefficient if too little output is

being obtained or produced from a given bundle of inputs. There are basically two

methods of measuring efficiency: the classical approach and the frontier approach. The

classical approach is based on the ratio of output to a particular input, and is termed

partial productivity measure. Dissatisfied with the short comings of this approach led

economists to develop advanced econometric and linear programming methods for

analyzing productivity and efficiency. The frontier measure of efficiency implies that

efficient firms are those operating on the production frontier. The amount by which a

firm lies below it production frontier is regarded as the measure of inefficiency.

The earliest work on the frontier approach dates back to (Farrel, 1957). The

production function stipulates the technical relationship between resources (inputs) and

output in any production scheme or processes (Olayide and Heady, 1982). In their own

opinion, Adegeye and Dittoh (1985) defined production function as the relationship

between factors of production or resources (inputs) and product (or output). They also

asserted that production function can be studied under the following main headings viz:

factor-product relationship, factor-factor relationship and product-product relationship.

In his view, Oji (2002) explained the production function to mean, the technical

relationship, which connects (factors inputs) with outputs. Thus, he said, production

function describes the way in which the quantity of a particular product depends upon

17

the quantities of particular inputs used. The author further stressed that production

function could be represented in a mathematical, tabular, or graphical forms. The

theoretical definition of production function has been based on expressing the maximum

input bundles with fixed technology. This is regarded as estimating average production.

This definition assumes that technical inefficiency is absent from the production. This is

exactly what Ordinary Least Square (OLS) model assumes; that is, any deviation of

output from actual (frontier) output is due to traditional errors (such as measurement

error and error resulting from the inability of the model to capture all variables) for this,

OLS assumes that producing units are fully efficient in the use of production resources.

The stochastic frontier production function assumes that there are inefficiencies in

production.

2.7 Analytical Framework

The type of analytical tools to be used in research studies depend to a greater

extent on the purpose for which the model is being estimated, nature of the study,

available data, types of data (cross-section, time series and panel), convenience of the

analysis, other economic underpins and indeed advantages derived from the tools.

Hence, stochastic frontier production and cost of productions were used to analyse the

technical, allocative and economic efficiencies respectively of the farmers. While the

farmers’ economic efficiencies were estimated as the product of TE and AE. Thus, the

model used in this work is based on the one proposed by Battese and Coelli (1995) and

Battese et al; (1996) in which the stochastic frontier specification incorporates models

for the technical inefficiencies effects and simultaneously estimate all the parameter

involved in the production and cost function model.

Model specification – the stochastic frontier production function model of Cobb-

Douglas functional form is employed to estimate the firm level technical and allocative

efficiencies of the farmers’ in the study areas. The Cobb-Douglas functional form was

used because, the functional form has been widely used in farm efficiency for the

developing and developed countries, the functional form meets the requirement for being

self-dual, allowing an examination of economic efficiency, and lastly, Kopp and Smith

(1980) suggested that functional form has a limited effects on efficiency measurement.

The Cobb-Douglas production functional form which specifies the production

technology of the farmers is expressed as follows:

18

Yi = f(xi:β) exp Vi – Ui ………………………………………….…………….. 2.1

Where: Yi – represents the value of output which is measured in (N); Vi –

represents the quantity of input used in the production. The Vi’s are assumed to be

independent and identically distributed random error, having normal N(O,δy2)

distribution and independent of the Ui’s which are technical inefficiency effects, which

are assumed to be non-negative truncation of the half-normal distribution N(μ, δi2).

The technical efficiency of individual farmers is defined in terms of the ratio of

observed output to the corresponding frontiers output, conditional on the level of input

used by the farmers. Hence, the technical efficiency of the farmers is expressed as:

TE, = Yi/Y j* = f(xii β) exp (Vi – Ui)/f(xi;β) exp Vi = exp (-Ui) ………………….… 2.2

Where:

Yi is the observed output and Yi* is the frontier’s output. The TE ranges between

0 and 1 that is Od “Ted” 1.

The corresponding cost of frontier of Cobb-Douglas functional form which is the

basis of estimating the allocative efficiencies of the farmers is specified as follows:

Ci = g(Pj;α) exp (Vi+Ui) = 1, 2, …, n …………………………………………. 2.3

Where:

Ci represent the total input cost of the i-th farmers, g is a suitable function such as

the Cobb-Douglas function, Pi represents input prices employed by the i-th farm in

groundnut production measured in naira; α is the parameter to be estimated, Vi’s and Ui’s

are random error and assumed to be independent and identically distributed truncations

(at zero) of N(μi, δ2) distribution. Ui provides information on the level of allocative

efficiency of individual farmers defined in terms of the ratio of the predicted minimum

cost (Ci*) to observed cost (Ci).

That is, AE=Ci*/C i exp (Ui) ………………………………………………… 2.4

Hence, allocative efficiency ranges between zero and one (0 & 1)

19

2.8 The stochastic frontier production function

The theoretical definition of a production function has been based on the

expression of the maximum amount of output obtainable from the given bundles of

production resources with fixed technology. This is regarded as estimating average

production function (Olayide and Heady, 1982). This definition assumes that technical

inefficiency is absent from the production frontier. Farrel (1957) suggested a method of

measuring technical efficiency of a firm in an industry by estimating the production

function of firms which are fully efficient (i.e. frontier production function).

Farrel’s measure of efficiency depends on the existence of an efficiency

production function with which observed performance of firm can be compared. A

production function based on the ‘best’ practical results could have to be used as a

reference for measuring individual performance. However, due to the problem of

complication, Farrell considered it better to compare performance with the ‘best’

obtained than to set up some unrealizable ideal. He then obtained from scattered

diagrams of several firms an isoquant showing the least exacting standard of efficiency

assumption of convexity to the origin and non – positive slope at any point.

As shown in figure 2, the graphical presentation of farrel’s definitions assume an

efficient” isoquant which is SS. Given the efficient isoquant and the isocost line CC, the

three efficiency measures of Farrel were given as;

TE = OQ ---------� Technical efficiency OP PE = OR -------� Price efficiency OQ OE = OQ . OR = OR ----------� Overall Economic efficiency OP OQ OP

20

X2

s p c R Q* S* c*

0 x1

Figure 2.2: Farrell’s Efficiency Measure

The modeling and estimation of production efficiency of a farm relative to other

farms or to the ‘best’ practice in an industry has become an important area of economic

study. Aigner et al., (1977) and Meeusen Van den Broeck (1977) independently

proposed a stochastic frontier production function and extended by Jondrow et al.,

(1982). It allows for the estimation of individual firm efficiency level with both time

variation and cross-sectional data. This is defined by:

Yi = f(X:;β)exp (Vi - ui) i = 1,2, - - - , n ……………………………….. (2.5)

Where:

Yi is quantity of output; Xi is a vector of inputs; β is a vector of performance; Vi

is a random error which is associated with random factor not under the control of the

farmer (example, weather, measurement error etc) having zero mean. It is assumed to be

independently and identically distributed as N(O,δy2) and having half normal

(exponential) distribution. The Ui is a non – negative random variable, which is the

inefficiency associated with a number of technical factors in production.

Technical efficiency of an individual farm is defined in terms of the ratio of the

observed output to the corresponding frontier output, given the available technology. It

therefore implies that the frontier output varies with the level of technology employed by

the farm. On the other hand technical inefficiency arises when less than maximum

21

outputs is obtained from a given bundle of factors (Russell and Young, 1983). In the

context of the stochastic frontier production function, technical efficiency is obtained by:

TE = Yi*/Y i = f(Xi ; β) exp (Vi - Ui)/f(X i ; β) Exp (Vi) - - (2.6)

= Exp(-Ui) - - - - - - - (2.7)

Where:

Yi* is the frontier output, and Yi is the observed output.

Technical efficient farms are those that operate on the production frontier, and the

level by which a farm lies below its production frontier is regarded as the measuring of

technical efficiency.

Battasse and Coelli (1988) suggested that the quantity exp (-Ui) can be predicted

by using its conditional expectation, given the composed random errors Vi – Ui”

evaluated at the maximum likelihood estimates of the parameters of the model be

obtained in terms of the parameterization δ2 = δu2 + δv2. Battesse and Corra (1977)

defined y as the total variation in output from the frontier and which is attributed to

technical efficiency and is bounded between Zero and one (0 < γ < 1) i.e

γ = δu2/ δ2 - - - - - - - - - (2.8)

The variance parameter y has two important characteristics.

1. When δv2 tends to Zero, the Ui is the predominant error term in equation 2.8, and γ

tends to 1 implying that the output of the sample farmers differs from the maximum

output mainly because of differences in technical efficiency

2. When δu2 tends to zero, then the symmetric error V is the predominant error in the

equation, and so y tends to zero.

Thus, based on the value of γ it is possible to identify whether the difference

between a famer’s output and the efficient output is principally due to random errors (γ

tends to zero) or the sample’s inefficient use of resources (γ tends to one) Kolirajan,

(1981).

Battesse and Coelli (1995) extended the stochastic production frontier model by

suggesting that the inefficiency effects can be expanded as a linear function of

explanatory variables, reflecting farm-specific characteristics. The advantage of this is

that, it allows estimate of the farm specific efficiency scores and the factors explaining

efficiency differentials among farmers in a single stage estimation procedure.

22

Allocative efficiency relates to the degree to which a farmer utilizes inputs in

optimal proportions, given the observed input prices (Coelli et al., 2002; Ogundari et al.,

2006). Russell and Young (1983) looked at allocative efficiency (AE) as a condition that

exists when resources are allocated within the firm according to market prices. In a

materialistic society according to them, this will represent a desirable characteristic when

market prices are a true measure of relative scarcity. This will be the case when prices

are determined in perfectly competitive markets, but when prices are distorted by

monopolistic influences or where some good remain outside the market system the role

of prices in resource allocation is greatly impaired. Lau and Yotopoulos (1979) reported

that a farm is said to be allocatively efficient if it maximizes profit, that is, it equates its

marginal product of every variable input to its corresponding opportunity cost. A farm

which fails to do so is said to be allocatively inefficient.

Following Kopp and Diewert (1982) allocative efficiency is measured as the ratio

of two costs: the variable cost when the cost – minimizing quantities of variable inputs

are estimated by analytically solving the cost –minimizing problem under the

assumptions that the frontier production function is as estimated, and that the target

output Y it is the output observed, adjusted for the statistical noise. On the other hand,

allocative inefficiency arises when factors are used in proportions which do not lead to

profit maximization (Russell and Young, 1983).

In Ferrell’s framework, Economic Efficiency (EE) is an overall performance and

is equal to the product of Technical Efficiency (TE) and Allocative Efficiency (AE) i.e.

EE = TE x AE. The simultaneous achievement of both efficient conditions according to

Heady (1952) occurs when the price relationships are employed to denote maximum

profit for the firm or when the choice indicators are employed to denote the

maximization of other economic objectives.

According to Adesina and Djato (1997) economic efficiency occurs when a firm

chooses resources and enterprises in such a way as to attain economic optimum. The

optimum implies that a given resource is considered to be most efficiently used when its

marginal value productivity is just sufficient to offset its marginal cost. Thus economic

efficiency refers to the choice of the best combination for a particular level of output

which is determined by both input and output prices. The basic structure of the stochastic

frontier model is depicted in figure 3, in which the production activities of two farms,

presented by i and j are considered as explained by Battesse (1992). Farm i uses inputs

with values given by the vector Xi, and obtained the output, Yi. The frontier output, Yi*

23

exceeds the value on the deterministic production function f(χi;β) because the productive

activity is associated with unfavaourable conditions for which the random error, Vi, is

negative. In both cases the observed production values are less than the corresponding

values.

There are two types of frontier models. These are deterministic and stochastic

model. Deterministic model is used to describe the group of method that assumed a

parametric form of production frontier along with strict one – sided error term (Coelli,

1995). According to him, deterministic frontier takes no account of the possible

influence of measurement errors and other noise upon the shape and position of the

estimated frontier since all observed deviations from the frontier are assumed to be the

result of technical inefficiency.

The stochastic model specification not only address the noise problem associated

with earlier deterministic function, but also permit the estimation of standard errors and

tests of hypotheses which were not possible with the early deterministic model because

of the violation of the maximum likelihood condition. However, the main criticism of

stochastic frontier is that there is no apriori justification for the selection of any particular

distributional form Ui.

Deterministic production Output function γ = F(X; B) γ F (X1 : β) x x observed observed output output

0 X; X; Input x

Fig. 2.3: Stochastic frontier production function

Frontier output

γ*, if V1 > 0

Frontier output

γ*, if V1 < 0

24

2.9 Stochastic Frontier Analytical Techniques of Efficiency Measurement

Measurement of efficiency is one of the very important topics of research in both

developing and developed nations. Applications vary in context because most studies in

developing countries are focused on agriculture, while in the developed countries the

interest on technical efficiencies has been confined to the industrial sector, or the

manufacturing sector, in general (Obwona, 2000, 2006).

The literature emphasis two broad approaches to production frontier estimation

and technical efficiency measurement;

• The non-parametric programming approach, and

• The statistical or econometric approach

The economic approach has been motivated to develop stochastic frontier models

based on the deterministic parameter frontier of Aigner & Chu (1968). The Stochastic

Frontier Analysis (SFA) makes a distinction between statistical noise and random noise

around the obtained production frontier and inefficiency (Kebede, 2001; Oren &

Alemdar, 2006), but the Data Development Analysis (DEA) do not make such

distinction in common terms. This points is very important for studies of farm level data

in developing economies like Nigeria, as data generally include measurement error

(Ogundari, 2006).

However, SFA is criticized for assuming a prior distributional form for the

inefficiency component and imposes an explicit functional form for the underlying

technology. This is the weakness of the SFA approach (Kebede, 2001; Oren & Alemdar,

2006). In a simple case of a single and multiple inputs, the approach predicts the output

from inputs by the functional relationships Vi=f(xi,β) + E: where i denotes the

production or economic unit being evaluated and β’s are the parameters to the estimated.

The residual ∑i is composed by random error, Vi and inefficiency component, Ui; when

we assume that Vi = 0, SFA is reduced to the Deterministic Frontier Analysis (DFA); if

we further let Ui = 0, SFA, will be reduced to central tendency analysis (Kebede, 2001).

In agricultural economics literature, the use of Stochastic Frontier Analysis (SFA)

is recommended because of the inherent nature of uncertainty/variability associated with

agricultural production due to weather, fires, pests, diseases, etc (Coelli & Battese, 1996;

Coelli et al., 1998).

25

2.10 Economic Approaches for Examining Factors Influencing Efficiency from

Stochastic Frontier Analysis (SFA)

There are several approaches to analyse the factors influencing efficiency

(technical, profit, allocative and economic). From Stochastic Frontier Production

Function (SFPF). Two of these approaches are discussed here: two-step approach and

one step approach. One set of authors followed a two-step procedure in which the

frontier production function is first estimated to determine efficiency indicators while the

indicator thus obtained are regressed against a set of explanatory variables that are

usually firm-specific characteristics. Researchers in this category include Pitt and Lee

(1981), Kalirajan (1981a); Kalirajan and Flinn (1983); Ogundele (2003); Asogwa et al.,

(2006) and Asogwa et al., (2011). While this approach is very simple to handle, the

major setback is that it violates the assumption of the error term. In the stochastic frontier

model, the error term (the inefficiency effects) is assumed to be identically independently

distributed with zero mean and constant variance ie Vis – iid N(O,O2) (Jondrow et al.,

1982). In the second step, however, the technical efficiency indicators obtained are

assumed to depend on certain numbers of factor specific to the firm, which implies that

the inefficiency effects are not identically distributed.

The major setback has led to the development of a more consistent approach that

modeled inefficiency effects as an explicit function of certain factors specific to the firm,

and all parameters are estimated in one-step using maximum likelihood procedure.

Researchers in this category include Reifschneider and Stevenson (1991), Richmon

(1974), Huang and Liu (1994), and Battese and Coelli (1995) who proposed a Stochastic

Frontier Production Function (SFPF) for panel data. Other researchers in recent times

include Ajibefun (2006; 2007), Ajibefun et al; (2002), Ajibefun et al; (2006), Coelli &

Battese (1996), Battese & Sarfaz (1998), Seyoum et al; (1998), Kurkalova & Jensen

(2000), Obwona (2006), Okoruwa & Ogundele (2006), Ogundele & Okoruwa (2006),

Otitoji (2008), Otitoju & Arene (2010), Adebayo, (2006); Maurice, (2004); Shehu &

Mshelia, (2007); Michael (2011), Shehu et al., (2007), Ayinde, Omolahim & Ibrahim,

(2011), Mesike et al., (2009), Okoruwa et al., (2006), Asumugba & Ujoku (2007),

Taphee & Jongur (2014).

26

2.11 Production Efficiencies and their Determinants: Emperical Evidence

Some studies that adopted the Stochastic Frontier approach for efficiency

analysis are hereby reviewed. Kalirajan (1981b) used a Cobb-Douglas stochastic frontier

approach to estimate the economic efficiency of farmers growing high yielding irrigated

rice in India. He compares the large and small farm groups and concluded that there was

equal relative efficiency in the cultivation of IR 20 in Rabi season between the groups.

Najafi and Abdullahi (1996) considered technical efficiency of pistachio farmers in

Rafsanjani area and the result showed that average technical efficiency at Noogh, Anar

and Kaboo Tarkhan fields of Rafsanjani area were 40%, 50% and 52% respectively.

Najafi and Zibadi (1995) have investigated on technical efficiency of wheat farmers at

far province and thus in the study; they applied maximum likelihood (ML) method in

estimation of stochastic frontier production analysis function (SFPF). The results of the

study showed that although technical efficiency at 1989-92 has increased from 67.6% to

79.7% yet there is possibility of increasing production of wheat by mapping technical

efficiency up to 20.3%.

In Ukraine, Kurkalova and Jensen (2000) estimated a stochastic frontier

production model with technical inefficiency effects on a representative and collective

grain-producing farms. The result indicated that technical efficiency declined from 1989-

1992. The mean efficiency in the sample were estimated at 0.82, 0.76, 0.68 and 0.60 for

the four years (1989-1992), respectively. They further found that, more experienced

managers were found to be more productive with the effect of experience diminishing

with age and on farm provision of production infrastructure were associated with high

efficiency.

Ali and Flinn (1987) examined farm-specific efficiency for 120 rice farmers in

Pakistan. A translog stochastic profit frontier was estimated by maximum likelihood. The

findings showed that education had a significant role in reducing profit inefficiency. In

addition, off-farm employment and difficulties in securing credit (credit inaccessibility)

to purchase fertilizer increased profit inefficiency. Abdullai and Huffman (1998) applied

a stochastic translog profit frontier to examine production efficiency for 256 rice farmers

in Northern Ghana in 1992-93. The result showed a negative and statistically significant

relationship between access to credit and profit inefficiency. It means that farmers

lacking credit to purchase fertilizer tended to experience higher profit inefficiency.

27

Kalirajan and Shand (1989) estimated a translog production frontier for paddy

using unbalanced panel data for 34 farm households for the three year, 1981-1983, in

south India. The results showed a positive relationship between technical efficiency and

farming experience, education, access to credit and extension services. Wiboonpongse

and Sriboonchilta (2004) estimated the effects of production inputs and technical

efficiency on Jasmine and non-jasmine rice for 489 farmers (i.e 282 jasmine and 207

non-jasmine raise in Chiang Mai province, Phistanubk province and Tung Gula Ronghai

in 1999). Factors affecting technical efficiency were also analysed contemporarily with

the production frontier using the Maximum Likelihood Method (MLM). Moreover, they

analysed factors, especially rust blast, affecting the jasmine and non-jasmine production

in Thailand.

Battese and Coelli (1995) defined a Stochastic Frontier Production Function

(SFPF) for panel data for India famers and the technical inefficiency were assumed to be

function of firm specific variables and time. The hypothesis that inefficiency effects are

not linear function of age, scarcity of farmers as well as year of observation was rejected.

In Uganda, Ubwona (2000) estimated a translog production function to determine

technical efficiency differentials among small and medium-scale tobacco farmers using a

stochastic frontier approach. The results showed the efficiency level of tobacco farmers

range between 44.5% and 98.1% on scale of 100% efficiency with mean technical

efficiency of 78.4%. He further estimated the factors influencing technical efficiency,

variables and institutional factors, the results indicated that family size, education, credit

accessibility and extension services contributed positively towards the improvement of

efficiency.

Ekanayake (1987) examined efficiency of 123 Sri-Lankan farmers. Cobb-

Douglas Production Frontier were estimated for farms that had either good or poor water

access. He found that literacy, experience and credit availability had a significant

positive impact on the technical efficiency level of the farms with poor water access.

Taylor and Shonkwiler (1986) analysed the effect of agricultural credit programmes on

technical efficiency for a sample of 433 farmers in Brazil. The frontier parameters were

estimated by maximum likelihood method (MLM), assuring that the technical

inefficiency effects had half-normal distribution. It was found that the credit programme

had no impact on improving technical efficiency.

28

Karbasi et al; (2004) in their study, “technical efficiency analysis of Pistachio

production in Iran using Maximum Likelihood Method (MLM) to estimate the Cobb-

Douglas frontier production function on 163 farmers for two year of 2000-2001 and

2001-2002 from two districts direct of Kashmar and Bardescan, respectively. The

inefficiency effects model incorporated in the production frontier model showed that in

both districts and significant relationship existed between the technical efficiency and

factors like farm size, attending extension service and literacy level. However, this

relation of technical efficiency and farmer’s age was indirect and significant.

In Kenya, Marinda et al., (2006) applied Cobb-Douglas stochastic frontier

function in the analysis of farm level data of maize production. The empirical results

showed that out of the explanatory variable identified, the main factors that tended to

contribute significantly to technical efficiency were: education of the farmers, access to

credit, fertilizer use, and distance of the farm to the main road. Access to credit was a

constraint to female farmers and affected their technical efficiency; while in Thailand,

Chaovana Poonphol et al., (2005) estimated stochastic frontier production function

(SFPF) using the survey data collected from 656 rice farmers in 2004. The results

showed that average technical efficiency of rice farmers was 79% and also found that

factors affecting the technical inefficiency of the rice farmers were; land, amount of

loans used for major rice production, experience, formal education and age. The

estimated elasticity of mean rice output with respect to land is 0.801, estimated at mean

input levels.

Ojo (2003) examined the productivity and technical efficiency of poultry egg

production in Nigeria using the stochastic frontier production function analysis using

data from 200 farmers. The results indicated that poultry egg production was in the

rational stage of production (stage II) as depicted by the return-to-scale (RTS) of 0.771.

The technical efficiencies of the farms varied between 0.239 and 0.933 with a mean of

0.763. He further observed that only location of farm (nearness to urban centre)

positively affected technical efficiency while increase in the other socio-economic

variables: age, experiences and education led to decrease in technical efficiencies.

Fasoranti (2006) in her study examined the influence of socio-economic variables

of farmers in Cassava-based cropping systems in Ondo State, Nigeria using cross-

sectional data collected on 305 cassava farmers. The analysis was based on the three

29

cassava cropping system (cassava-sole, cassava plus maize and cassava with other crops)

identified in the study area. The results showed that farming experiences and the level of

education helped to reduce technical inefficiency among farmers that planted cassava-

sole crop; while farming experience helped to reduce technical inefficiency among

farmers within the cassava plus other crops cropping system. Cooperative membership

significantly reduced technical inefficiencies in all the three cropping systems. On the

other hand, land acquisition method increased technical inefficiency in the study area.

Results on the allocative inefficiency showed that the level of education and farming

experience reduced allocative inefficiencies in cassava-sole crop while land acquisition

method and cooperative membership reduced allocative inefficiency under cassava-

maize mixture cropping system. Farming experience and land acquisition method to

reduce allocative inefficiency under cassava and other crops cropping system.

Otitoju (2008) in his study on the determinants of technical efficiency in small

and medium scale soybean production in Benue State, Nigeria discovered that the mean

technical efficiency of small and medium-scale soybean farmers were 0.842 and 0.728

respectively. Family size, age and non-family labour were statistically significant and

decreases technical inefficiency among small-scale soybean farmers, while age and off-

farm income were statistically significant and reduces technical inefficiencies in

medium-scale soybean production.

Otitoju and Arene (2010) in the study “constraints and determinants of technical

efficiency in medium-scale soybean production in Benue state, Nigeria” observed that

the average technical efficiency was about 73%. The determinants of technical efficiency

which were statistically significant were sex, age and experience. Sex and age had an

inverse relationship with technical inefficiencies of the farmers while experience had a

direct relationship.

Ajibefun (2006) used the translog stochastic frontier production to analyse and

link the level of technical efficiency of Nigeria small-scale farmers to specific farmers’

socio-economic and policy variables. The results showed that while farmers’ socio-

economic and policy variables significantly influenced the level of technical efficiency;

education has the highest marginal effect, the highest mean technical efficiency of 0.77

occurs among group of farmers within 7-12 years of schooling (secondary school

education group) while the least mean technical efficiency (0.54) occurs within 1-6

30

years. It implies that technical efficiency has a direct relationship with years of

schooling.

Ajibejun and Abdulkadri (1999) estimated technical efficiency for food crops

farmers under the National Directorate of Employment in Ondo State, Nigeria. The

results of the analysis showed wide variation in the level of technical efficiency between

0.22 and 0.88 on a small 1.0 (indicating that the level of technical efficiency of the

farmers ranged between 22% and 88%). Ajibefun et al; (2002) used the translog

stochastic frontier production function methodology to estimate the level of technical

efficiency of small-holder food crops farmers in Oyo State of Nigeria. The results

revealed that the inefficiency effects of small-holder croppers were significant. The

technical efficiency varied widely, ranging from 19% to 95%, with a mean value of 82%

indicating that the farmers are 82% efficient in the use of their production inputs. Age of

farmers, farming experiences, level of education, size of farm holdings as well as the

ratio of hired labour to total labour used, were factors that significantly influenced the

level of technical efficiency. The results showed that the technical inefficiency of

farmers increase with age, farm size and the ratio of hired labour to total labour, while

the level of technical inefficiency tends to declined with years of experience and level of

education. The results also indicated an increasing return-to-scale parameter 1.17, (i.e

significantly different from 1).

Ogundele and Okoruwa (2006) in their study “technical efficiency differentials in

rice production technologies in Nigeria” estimated technical efficiency following the

maximum likelihood estimation using data from 302 farmers. The findings indicated that

there was no absolute differential between the two groups (local and improve) of

farmers, the two groups were correspondingly high (>0.90), which showed that there is

little opportunity for increased efficiency, about 10%, given the present state of

technology. The variables that tend to contribute to technical efficiency are hired labour,

herbicides and seeds.

Ehirim and Oyeka (2002) in their study “a stochastic frontier approach to

technical efficiency in acquaculture in Oyo State, Nigeria. The study revealed that an

average relative inefficiency index of 24% was found using Cobb-Douglas functional

model and a total return to scale of 1.12 was recorded, which shows an increasing return-

to-scale (IRS). It implies that an additional increasing of 0.12% of output will be

31

recorded if there is an increasing in 1% use of all these input resources like capital,

labour and chemical.

Ogundari (2006) applied stochastic Cobb-Douglas profit frontier model to

examine the determinants of profit efficiency among the small-scale paddy rice farmers

in Nigeria. The results revealed that the profit efficiency of the paddy rice farmers were

positively influenced by (age, educational level, farming experiences and household size)

but negatively influenced by the price of fertilizer/kg. The average profit efficiency

estimated was 0.060 on 1.0 scale.

In their study, Abdulai and Huffman (2000) showed that the mean profit

efficiency was 27.4 and the results showed that the level of education (human capital) of

the household head tends to have a highly significant impact on profit inefficiency. The

negative sign indicates that higher levels of education reduce profit inefficiency. The

coefficient of the interaction term was also negative, but significant at the 10% level,

suggesting that more educated farmers without credit constraints were more efficient

than their counterparts who face credits constraints. The positive and significant

coefficient of the non-farm employment variable indicates that the farmers engaged in

non-farm activities tend to exhibit higher levels of inefficiency. A negative and

statistically significant relationship was also found between access to credit and profit

inefficiency.

The production function approach, however, it is not able to capture

inefficiencies associated with different factor endowment and different input and output

prices across farms (Abdulahi & Huffman, 1998). Therefore, either the profit function

approach or the cost function approach have been used in the analysis of efficiency, it is

because the dual relationships provide the flexibility in problem-solving when data are

limited or are of a specific type. Nevertheless, the quality of the estimated dual

relationship may not be too good if price variability is small, thus firms will have market

power or measurement error to have occurred (Lusk et al., 1999).

Tadesse and Krishnamorthy (1997) estimated technical efficiency in paddy farms

in Tamil Nada using stochastic frontier model. The study shows that 90% of the variation

in output among farms was due to difference in technical efficiency. Land, animal power

and fertilizer were found to have significant influence on the technical efficiency levels

of the farmers. Bravo-Ureta and Evenson (1994) in their study of agricultural production

32

of peasant farmers derived technical, allocative and economic efficiency separately. They

found that the peasant farmers could increase output or household income, through better

use of available resources given the state of technology.

Yao and Liu (1998) applied the stochastic Cobb-Douglas production function to

analyze the determinants of grain production and technical efficiency in China using a

set of panel data on 30 provinces from 1987-1992. Their result showed an estimated

national average level of technical efficiency of 0.64, with all the efficiency variables

been highly significant. Amaza and Olayemi (2002) used the stochastic frontier

production function to determine the resource use efficiency in food crop production in

Gombe State, Nigeria and found that land, hired labour and fertilizer were underutilized

while family labour was excessively utilized in food crop production. According to them

these four factors were relevant to be major factors that influence the output of food

crops. Oiakhina (2005) used the stochastic frontier approach in a study on land

productivity and resources use efficiency by food crop farmers in Niger Delta Area of

Nigeria. According to him, the estimated coefficient were positive for age, sex, family

size and years of schooling implying that these socioeconomic factors affected the food

production efficiency negatively.

In another study conducted by Amaza et al., (2000) on the factors that influence

technical efficiency of cotton formers in Nigeria showed the status of cotton farmer such

as credit and education positively correlated with technical efficiency. Tran et al., (1993)

in the study on an analysis of technical efficiency of state of farmer in Vietnam

discovered increasing returns to scale of the farms and operational difference in technical

efficiency levels. The reason advanced for the variations were that of management and

different field of husbandry methods adopted in the farms. Ojo and Imoudu (2000)

conducted a comparative study on productivity and technical efficiency of oil palm farms

in Ondo state of Nigeria and found out that training of farm settlers increase their

technical efficiency than those that are not trained and concluded that technical

efficiency positively correlated with the training.

Maurice, (2004) examined the resource use productivity in cereal crops

production among fadama farmers in Adamawa state, the result indicated that the

technical efficiency among the sample farmer ranged from 0.29 to 0.97 with mean

Technical Efficiency (TE) of 0.80. Maurice et al., (2005) analyzed the technical

33

efficiency on rice based on cropping pattern among dry season fadama famers in

Adamawa State. The result of the inefficiency model indicated that farming experience

and level of education increased the TE of farmers. Adebayo (2006) conducted a study

on resource use efficiency and multiple objectives of dairy pastoralist in Adamawa state

using the stochastic frontier production function. The result showed that labour potential

milking cows, veterinary inputs and feed supplement have significant influence in

output. The result also indicated that pastoralist had mean TE of 0.87 with possibility of

increasing milk output by 13 %. Moreover, the inefficiency analysis revealed that age,

experience have positive impact on pastoralists’ efficiency while household size had no

significant relationship with efficiency. Similarly in a study conducted by Shehu and

Mshelia (2007) on productivity and technical efficiency of small scale rice farmers in

Adamawa state, Nigeria, the result showed that, the average TE of the farmers was 0.957

and more 90 % are between 90 and 100 % technically efficient. Thus, they concluded

that efficiency of the rice farmers could be increased by more than 4 %.

Oluwatayo et al., (2008) in a study on resource use efficiency of maize farmers in

rural Nigeria; Evidence from Ekiti state found out that the technical efficiency index

shows that the farmers were 68 % efficient in their use of resource. They concluded, this

calls for improving the efficiency of maize farmers in the study area. Shehu et al., (2010)

in study on determinants of yam production and technical efficiency among yam farmers

in Benue state Nigeria revealed that the yam farmers appeared somewhat inefficient in

their use of inputs. Thus, they stated that the attainment of an average technical

efficiency of 95% indicated that efficiency of farmers could be increased by about 5% to

attain maximum possible output.

Ayinde et al., (2011) revealed in a study on efficiency of resources use in Hybrid

and open-pollinated maize production in Giwa local government area of Kaduna state,

Nigeria, that all the resources used in the production of hybrid and open – pollinated

maize were not efficiently utilized.

Umoh (2006) conducted a study on resource use efficiency on urban farming: An

application of stochastic frontier production function. The result showed that, the overall

urban farmers performed at an average technical efficiency of 72 %, only one farmer was

between 90 and 100 % efficient. The findings of the study according to them have the

implications for increased food production in the study area. For almost three decades,

34

the modeling estimation and application of stochastic frontier production function

assumed prominence in econometrics and applied economics analyses.

2.12 Costs and Returns in Agricultural production

Cost refers to the monetary values of the inputs used in production while profits

or returns are gains from production (Adegeye and Dittoh, 1985; Olukosi and Erhabo,

2005). Heady (1952) defined the cost of producing any good or services as the value of

the resource used in producing them in their best alternative way; since there are other

alternative means of attaining these production goals; the theory of production presents

theoretical and empirical framework to facilitate a proper selection among alternatives

such as any one or a combination of the farmers’ objective can be attained. Production

naturally is aimed at either maximizing output, maximizing profit, minimize cost or a

combination of some of or all these (Olayide and Heady, 1982).

Olukosi and Isitor (1990) and Olukosi and Ogungbile (1989) have examined two

major categories of cost involved in crop production. These are variable cost (VC) and

fixed costs (FC). Variable costs (VC) referred to those costs that vary with the level of

production (output). Examples are cost of seeds, fertilizers, expenses on hired labour etc.

Spurlock and Gills (1997) stated that variable costs are those that a manager controls in

the short – run and that will increase as total planned production is increased. Fixed costs

(FC) on the contrary referred to those cost that do not vary with level of production or

output examples are rent on land, interest on capital, degradation cost, costs of

machinery etc. the summation of VC and FC gives rise to total costs (TC).

As a measure of farmers’ net returns from the farm enterprise Olukosi and

Erhabor (2005) defined gross margin (GM) as the difference between the gross farm

income (GFT) and the total variable cost (TVC) i.e. GM = GFI – TVC. It is used as a

primary measure of profitability under the assumption that fixed cost is negligible as

what obtained under traditional farming system and that the analysis is for a short term.

Idama (2000) reported that revenue generation is perhaps the most important

responsibility of modern government. As the welfare needs of the people increased

sources of generating revenue to meet the needs must be found. He is of the view that

one must keep on investing heavy on such wealth generating activities as groundnut and

other agricultural business, the lots of farmers would have been freak or improved.

Iyalla (2004) observed that the need to maximize yield per unit area of cultivated

hectares of land is of great importance in farming particularly in wet land farming,

35

considering the cost of input in terms of plant nutrient and man-power. He also noted

that large scale and export (revenue generation) oriented agriculture are now not

commonly practiced in the country any longer. But with the quest of the third tiers

government, emphasis is now shifted to large scale agriculture and mechanized farming

to resuscitate groundnut production.

Eyo (2004) observed that in the Nigerian agricultural sector, the small operators

face pure competition both at production and marketing stages. Because of this structure,

output is sold at industry – determined price and profit are maximized at the level of

output where marginal cost equals marginal revenue. However, the size of profit depends

on how large the per unit output prices compared to the unit cost of production. If the per

unit output is large, the operators earn pure profit in the short – run. Invariably, the

outcome of the pattern of structure and conduct of the performance is interpreted by the

profit or marketing margins among other things.

Yayock et al., (1988) Olukosi and Isitor (1990), revealed that the improvement in

the marketing structure of groundnut and desire for certain products of groundnut such as

groundnut oil, cakes, animal feeds, confectionaries generally influence marketing of

groundnut in the country. They further stated that the unit in which groundnuts are

marketed are in bags and retail quantities sold in measures called mudus that varies from

one location to another. This implies that there is no standardization in the units of

measurement, which is a common problem with agricultural marketing in developing

countries, therefore is a setback to returns in agricultural production.

Awoke (2003) in a study of production analysis of groundnut in Ezeagu local

government area of Enugu state found out that farmers employed traditional method of

farming and use of family labour. He also found out that groundnut production was a

profitable enterprise with a gross margin of N8,466.00/ha and a sizeable project of

N6,067.00/ha.

According to Langyinto (1999), that transport cost, infrastructural facilities like

road network, proximity to market location, price of the commodity and activities of

commercial agents affect profitability of groundnut production in Nigeria. He further

stated that groundnut is marketed in the forms of fresh dry pods, dry grains and other

processed forms.

Hammawa (2001) in a study on the profitability of locally processed groundnut

oil and cake discovered that it was a profitable venture, though with a low profit margin.

Obiasie and Chuke (1984) suggested raising the price of groundnut by the government to

36

stimulate products in order to increase their revenue, they also opined that lowering the

price of inputs would reduce cost of production thereby increasing the gross margin. A

similar view was shared by Abalu (1986) who said that groundnut producers responded

to good prices.

In a study conducted by Adinya et al., (2010) on exploring profitability potentials

in groundnut production through agro forestry practices in Bekwarra Local Government

Area of Cross River State, Nigeria showed that groundnut-base alley cropping system of

production by small-scale farmers was profitable.

2.13 Socioeconomic Characteristics of farmers

The socioeconomic demographics of farmers play an important role in creating

awareness and knowledge as they influence decision and level of use of modern input

and technology (Mohammed et al., 2005). Some of the socioeconomic features of the

farmers which may affect their technical efficiency (productivity) level include: age,

gender, education, land ownership, farm size, types of labour, access to farm inputs,

access to credit, access to extension services, farming experience and household size.

i. Age

The age of farming household heads was observed to have an inverse relationship

with productivity of farmers in studies from Adeoti (2002), Ajibefun and Abdulkadiri

(1999, 2004) Adebayo and Onu (1999), Olaf et al., (2003). Ajibefun and Daramola

(1999), Ajibefun et al., (1996, 2002), Coelli and Battesse (1996), Idjesa (2007), and

Ogundele (2003). All of these studies were carried out in the humid forest, dry savannah,

and most savannah regions of Nigeria, except Coelli and Battesse study, which was

carried out in India. This was understandable since it is expected that as a farm

household head become older his or her productivity will decline.

ii. Farming experience

Years of farming experience is another factor that enhances productivity among

farming households. Years of farming experience in Nigeria increases as age of farmers

increases. Age in farming experience is therefore positively correlated with the

productivity of the farmers. Older farmers have also been observed to have higher

productivity than younger farmers. For example, Ajani (2000), Ajibefun and Abdulkadri

(1999, 2004), Ajibefun et al., (1996, 2002), and Idjesa (2007) observed that productivity

in the humid forest and moist savannah agro-ecological zones of Nigeria was positively

associated with more experience in farming. Also Adewumi and Okunmadewa (2001)

37

reported that the economic efficiency level of farmers was significantly affected by

farming experience.

iii. Education

Education is one of the key assets needed to foster productivity in any profession.

Findings of Adetiba (2005), Adeoti (2002), Ajani (2000), Ajibefun and Abdulkadri

(1999, 2004), Ajibefun et al., (1996, 2002), Amaza (2002), Bravo-Ureta and Riegner

(1991), Idjesa (2007), Idumah (2006), and Kehinde (2005) confirmed that education was

key to enhanced productivity among farming households in the humid forest, dry

savannah and moist savannah agro ecological zones of Nigeria and in New England.

According to (Alabi and Aruna, 2006) education was found to be a vital component in

technology adoption in Agriculture. Their works confirmed with the work of Arnon

(1987) and Awoola (1995) who observed that education attainment of farmers does not

only raise his productivity but also increase his ability to understand, evaluate

information on innovation and adopt it for production. In the same vein Renato and Euan

(2004) stated that education was found to be one of the significant factors associated

with technical efficiency of farmers implying that human capital is an important factor in

carrying out production and managerial tasks on groundnut farm. This was likely

because good education propels heads of farming households to adopt new innovations

and technologies that are vital to enhancing farm productivity.

iv. Land ownership

Abdullahi (1981) noted that, although the country is endowed with agricultural

land, the right to ownership of land and ethnic boundaries make it difficult for farmers to

easily acquire land for agricultural purpose outside their cultural location, thus,

contributes or mare their productivity. Adekanye (1988), Ajani (2000), Akinseinde

(2006), Babalola (1988) and Olawoye (1988) showed that farmers that owned parcels of

land on which they farmed were proactive than non landowning farming households.

Land ownership according to Adebayo and Onu (1999) is one of the socioeconomic

characteristics that affect farmers’ level of productivity. This was understandable since

farmers that owned land on which they farm were ready to make huge investment on

such land through the adoption of new technological package which enhance

productivity level. Adekanye (1988) provided empirical evidence showing that women

had a lower level of productivity than men because they had far less access to land and

other productive inputs.

38

v. Household Size

Family size is an important source of family labour especially in traditional

agriculture where farming is highly labour intensive (Aye and Oboh 2006), Owa et al

(2007), Adelwumi and Adebayo (2008), Ya’ashe et al., (2010), Gwandi et al., (2010)

and Jude et al (2011). Most of these authors’ results revealed that the mean of the

household size falls between the range of 5 and 9. The result also showed that people

within these study areas appreciate large families ostensibly because of the labour they

stand to benefit.

vi. Types of labour

Labour is a limiting factor of production in the vast majority of West African

farming systems. Technology and labour productivity hold the key to the development of

agricultural economics (Chianu et al., 2001). Adebayo (2006), Ajibefun and Abdulkadri

(2004) Ajibefun et al., (2002) Amaza and Olayemi (2002), Dittoh (1991), Ogundele and

Okoruwa (2006), and Tella (2006) all assessed how labour affected farm productivity in

the dry savannah and humid forest agro-ecological zones of Nigeria. Using analytical

tools such as the Cobb – Douglas production function, the normalized profit function

approach, and the stochastic frontier model (Amaza and Olayemi, 2002). (Dittoh, 1991)

and (Tella, 2006) observed that the use of hired labour reduces productivity when not

properly utilized. Adebayo (2006), Ajibefun and Abdulkadri (2004), Ajibefun et al,

(2002), and Ogundele and Okoruwa (2006), however showed that hired labour

contributed positively to farm productivity. Outside Nigeria, Mochebele and Winter –

Nelson (2002) investigated the impact of labour migration on technical efficiency

performance in Lesotho. Using stochastic frontier production, the study found that

households that sent migrant labour to South African miners were more efficient than

households that did not, with a mean technical efficiency of 0.36 and 0.24 respectively.

Similarly, Nkonya et al., (2005) revealed that pre-harvest labour positively affected crop

production in Uganda. Adegeye and Dittoh (1985) stressed the important of family

labour in small holder famers’ production in Nigeria. Abdulrahman (1983) reported that

farmers hire labour when they assume that increment in cost will result in higher

earnings and usually when scale of operation is large. Ntare (2005) attributed groundnut

production problem to high labour cost.

39

vii. Gender

The connection between agricultural productivity and gender were well

dominated in the studies of Adekanye (1988), Babalola (1988) Odii (1992) and Olawoye

(1988). Odii (1992) observed that the contribution of female farmers to agricultural

productivity was highly significant. Adekanye (1988) offered evidence of gender

differentials in agricultural productivity in Nigeria with women’s productivity arising

from their weak bargaining position within the family and in the labour market. Further

support for this gender bias in Africa derives from the fact that women have far less

access to land and other productive inputs (Babalola, 1988, Olawoye, 1988).

viii. Access to farm inputs (Fertilizer, herbicide)

Access to fertilizer, agro-chemicals and improved seeds/planting materials has

been proved as an important driver of agricultural production and productivity among

farmers in sub-Saharan Africa. Using stochastic frontier model, Mbata (1988) and

Ogundele and Okoruwa (2006) stated that the use of fertilizer increased agricultural

productivity of crop farming in the dry savannah and humid forest agro-ecological zones

of Nigeria. Nkonya et al., (2005) also alluded to the positive impact of fertilizer. The use

of herbicides by famers according to Mbata (1988), Ogundele and Okoruwa (2006) had a

positive correlation with technical efficiency or productivity of farmers. However, Tella

(2006), using the Timmer and KOPP indices, revealed that the use of chemical

contributed to productivity negatively if not properly utilized.

ix. Access to credit

Another important factor that has been empirically proven to influence

productivity is credit. Akinseinde (2006), using data envelopment and the Tobit model,

showed that having access to credit facilities contributed positively to a household’s

production efficiency in the humid forest agro-ecological zone of Nigeria. Similarly,

Obwona (2000), using the Translog production function, showed that access to credit

contributed positively towards the improvement of efficiency among tobacco farmers in

Uganda.

x. Farm size

The effect of farm size on farm productivity is very vital. Lau and Yotopolous

(1971) using the profit function equation found that small farms attained higher

productivity levels than larger farms in India.

Sahidu (1974) adopted the Lau – Yotopolous model to sample India wheat farms

and came up with a contrary conclusion showing large and small farms exhibiting equal

40

level of productivity. Khan and Maki (1979) using the Lau – Yotoopoulous model in

Pakistan observed, however, that large farms were more efficient than small farms.

Using a normalized profit function and stochastic frontier function, Ajibefun et al.,

(2002) and Mbata (1988) showed that large farm size enhance productivity among

farmers in the dry savannah and humid forest agro-ecological zones of Nigeria.

xi. Access to extension services

Access to extension services has been identified as key to farm productivity in a

series of studies. Obwona (2000), using the Translog production function, demonstrated

that access to extension services by tobacco farmers improved their productivity in

Uganda. In contrast, Bravo-Ureta and Reigner (1991) using the stochastic efficiency

decomposition model based on Kopp and Diewert’s deterministic methodology,

concluded that extension service did not markedly affect productivity of farmers in New

England. However, the studies of Adewuyi (2002), Ajani (2000), Amaza (2002) and

Awotide (2004) all reported that extension services enhanced farmers’ productivity in the

humid and dry savannah agro-ecological zones of Nigeria. Also Adewumi and

Okunmadewa (2001) revealed that the economic efficiency level of farmers was

significantly affected by extension services.

2.14 Factors Affecting Groundnut Production

According to Diop et al., (2004), groundnut production conditions vary

considerably across continents, reflecting differences in technological practices. Yields

are highest in the united state and China and lowest in Sub-Saharan Africa (Except South

Africa) and India. The low yields in Africa and India are due to limited use of modern

inputs including high yield seeds varieties and heavy dependence on rainfall.

Mahmmoud et al., (1992) reported that groundnut production in Africa suffered

downward Trend. He asserted that, low yield in Eastern Africa has been attributed to the

unreliable rains associated with drought, lack of high yielding cultivars, pest and diseases

as well as low inputs used in groundnut cultivation.

Ntare, (2005) stated that small holder farmers’ access to new crop varieties has

long been recognized as a critical step for increasing agricultural productivity in Sub –

Saharan Africa. They authors opined that adoption of improved varieties that resist pest,

disease and drought can often vary yield even when farmers are unable to adopt more

costly inputs such as chemical and fertilizers.

41

Ntare (2005) and Misari et al., (1980) attributed groundnut production problem to

inadequate supply of improved seed, pesticides and high labour costs. They also stated

that late planting and heavier soils have been responsible for low yield. They further

stressed that drought and rosette epidemic have tremendous effect on yield loss of

groundnut. Awoke (2003) identified lack of improved capital inputs, lack of collaterals

and high interest rate as some of the major problems of groundnut production.

According to Freeman et al., (1999) small – scale groundnut farmers are the ones

mostly affected by this stress, lack of resources or access to currently available

technology. They authors also observed that low producers prices and limited modeling

opportunities reduced incentives for small holder groundnut farmers to invest in

productivity enhancing technologies such as improved seed, fertilizer and pesticides.

Sashidar (1993) revealed that the incidence of Aftotoxin associated with

groundnut makes it high – risk agricultural commodity. Toxicity of groundnut from

aflatoxin endangers the health of humans and animal and lower market value Abdalla et

al., (2005). Hence it is a serious problem of groundnut producers as well as consumers.

In a study in Niger, Crauford et al., (2006) confirmed that infection of aflatoxin in

concentration in peanut can be related to the occurrence of soil moisture stress during

pod filling when soil temperatures are near optima.

Early and Late leaf spots commonly called Ticks Disease as reported by Garba et

al., (2005) and Lokhande & Newaskar, (2000) caused huge loss in groundnut due to

several defoliation. Rust has now become a disease of major economic importance in

almost all the groundnut growing areas of the world. It is a devastating factor both under

high rainfall and high humidity (Siddaramaiah et al., 1980; Mayee, 1989; Lokhande et

al., 1988).

Major insect pest of groundnut such as Hobner (Heliothis amigera) has become a

serious pest in recent years. The study on the relationship between seasonal incidence of

heliothis and weather parameters by (Upadhyay et al., 1989) showed that holistic

population was positively associated with maximum and minimum temperature.

ICRISAT (1989) reported that aphids distribution across a drought-stress gradient

created by a long line source over-head irrigation showed that aphid density was much

higher where most irrigation water has been applied and lowest at a point farthest from

the water source, where plants are experiencing a drought stress.

42

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 The Study Area

The study area is Taraba State, Nigeria. The State is situated in the North Eastern

part of Nigeria. It occupies 54, 473 square kilometers, with a population size of 2,

300,736 people (NPCo, 2006). It has sixteen (16) local government areas. Taraba State is

bounded in the west by Plateau, Nassarawa and Benue States, on the eastern border by

Adamawa State and the Republic of Cameroon, on the Northern border by Gombe State

and to the Southern parts it shares boundary with Benue and Akwa Ibom States (Bashir,

2000).

Taraba State lies in the Guinea savannah and consists of undulating landscape

dotted with a few mountains features. These include the scenic and prominent Mambilla

Plateau. It lies largely within the tropical zone and has a vegetation of low forest in the

southern part and grassland in the northern part. The Mambilla Plateau with an altitude

of 1, 800 meters (6000ft) above sea level has a temperate climate all year round. River

Benue, Donga, Taraba and Ibi are the main rivers and rise from the Cameroon mountain,

straining almost the entire length of the State in the north and south direction to link up

with the River Niger.

The major occupation of the people is agriculture. Cash crops produced in the

State include coffee, tea, groundnut and cotton. Crops such as maize, rice, sorghum,

millet, cassava, and yam are also produced in commercial quantities. In addition, cattle,

sheep and goats are reared in large numbers, especially on the Mambilla Plateau, and

along the Benue and Taraba valleys. Finally, people undertake other livestock production

activities like poultry production, rabbit breeding and pig farming in fairly large scale

(Community For Social Development Project, [CSDP], 2012; Natural Resources and

Development [NRD], 2007).

The state is heterogeneous in ethnic composition. The annual average rainfall

varies from 500mm in the northern part of the state to 1000mm in the southern part of

the state. Rainy season in the state starts from April and ends in October with an annual

precipitation of 1500mm (TADP, 2013). This study focused on groundnut as one of the

major cash and food crop grown in the study area.

43

3.2 Sampling procedure

Taraba State is divided into four (4) agricultural zones by the Taraba State

Agricultural Development Programme (TADP) for administrative convenience. The

study employed multi-stage sampling techniques in the selection of the respondents. The

first stage was random selection of three out of the four agricultural zones. Zone 1, 2 and

3 were therefore selected. In the second stage, three local government areas were

randomly selected from each of the three zones giving a total of nine (9) local

government areas. In the third stage, three major groundnut producing villages were

purposively chosen from each of the selected LGAs giving a total of 27 villages (TADP,

2013). In the fourth stage, a list of major groundnut farmers in the 27 villages was

compiled and from the list, a total of 270 groundnut farmers were randomly selected for

the study (TADP, 2013).

Table 3.1: Study Location and Sample Chosen

TADP zones Local government areas selected Samples of groundnut farmers used

Zone I

Zone II

Zone III

Total 3

Ardo-kola, Yorro & Zing

Gassol , Donga &Wukari

Bali , Kurmi &Takum

9

105

90

75

270

Source: TADP Headquarters, Jalingo

3.3 Data collection

Data collected for the study were mainly cross sectional data by survey. The data

were collected for 2012 cropping season using structured questionnaire which were

facilitated by trained enumerators of zones 1, 2 and 3 of the Taraba State Agricultural

Development Programme (TADP).

3.4 Data analysis techniques

Both descriptive and inferential statistics were used in the analyses of data.

Descriptive statistics involving the use of means, frequencies distribution and

percentages were used to achieve objective 1 and 6, stochastic frontier production

function was used to achieve objective 2 and a component of objective 3 (technical

efficiency), stochastic frontier cost function model was used to achieve the allocative

component objective 3 while the remaining component of objective 3 (economic

44

efficiency) was achieved by stochastic frontier production and stochastic cost function.

Thus, gross margin analysis was used to achieve objective 4 and profit function analysis

was used to achieve 5.

3.4.1 The Empirical Stochastic Frontier Production Model

The stochastic frontier production model was independently proposed by Aigner

et al., (1977) and Meeusen and Van den Broeck (1977). It employs a Cobb – Douglas

production function to simultaneously estimate the random disturbance term (Vi) which

is outside the control of the production unit and the inefficiency effect (Ui) as proposed

by Battese et al., (1996)

The farm frontier production function can be written as:

Y i = f(Xi;β) exp (Vi - Ui) - - - - - - - (3.1)

Where:

Y is the quantity of agricultural output Xi, is a vector of input quantities and β is a

vector of parameters.

The corresponding cost frontier as used by Ogundari et al., (2006) can be derived

analytically as

C = g (Pi Yii γ) + Vi + Ui - - - - - - - (3.2)

Where:

C is the total production cost P is a vector variable of input prices, g is a suitable

functional form, Vi is the value of output in kg and γ is the parameter to be estimated.

By using Shephard’s Lemma (Bravo-Ureta and Reigner, 1991), the minimum

cost input demand equation is obtained i.e.

ӘC = Xi (Pi Yi ϕ) - - - - - - - - - - (3.3) ӘPi

Substituting equation (3.1) and equation (3.2) into equation (3.3) yields the

economically efficient input vector Xe. The technically efficient input vector can be used

to compute the cost of the technically efficient (Xt. P) and the economically efficient (Xe.

P) input combinations associated with the firm’s observed output. The cost of farm’s

actual operating input combination is given by Xa’.P.

45

These three cost measures are the basis for computing the following technical,

economic and allocative efficiency indices as explained by Bravo-Ureta and Reigner,

(1991).

And allocative efficiency respectively.

TE = Yi/Y* = f(χiβ) exp(Vi – Ui)f(χi β) exp(Vi) - - - (3.4)

TE = exp( - Ui) so that 0 ≤ TE ≤ 1 - - - - - (3.5)

Variance parameters δ2 = δ2+ δ2u - - - - - (3.6)

Y = δ2/δ2v so that 0 = y ≤ 1 - - - - - (3.7)

The empirical model for Taraba small scale groundnut farmers is given by:-

In Yij = βo+β1Ɩnχ ij+β2Ɩnχ2ij+β3Ɩnχ3ij+β4Ɩnχ4ij+β5Ɩnχ5ij+β6Ɩnχ6ij+β7Ɩn χ7ij+Vij – Uij - -

- (3.8)

Where:

Subscript ij refers to the jth observation of the ith farmers

Ɩn = logarithm to base e

Y = Output of the groundnut farmers (Kg grain equivalent) per/ha

χ1 = Farm size (in hectares)

χ2 = Quantity of seed used (in kg/ha)

χ3 = Family labour used in production (in man-days/ha)

χ4 = Hired labour used in production (in man-days/ha)

χ5= Quantity of other agrochemical used (in litres/ha)

χ 6 = Quantity of fertilizer used (in kg/ha)

χ7 = Expenses on ploughing (tractor and animals traction) in Naira per hectare.

It is assumed that the technical inefficiency effects are independently distributed

and Ui arises by function (at Zero) of the normal distribution with, Uij and Variance δ2,

where Uii is defined by:

μii = δo + δ1 Z1ij + δ2Z2ij + δ3Z3ij + δ4 Z4ij + δ5 Z5ij + δ6 Z6ij - - (3.9)

Where:

μij = Technical inefficiency of the ith farmer

Z1 = Farming experience (in years)

Z2 = Gender of the respondent

46

Z3 = Household size (number of person in farmers household)

Z4 = Extension contact (Number of meetings)

Z5 = Literacy level (in years)

Z6 = Age of the respondents (in years)

Z7 = Access to credit facilities (loan)

δ1 – δ7 = Are parameters to be estimated.

The maximum likelihood estimated of β and δ coefficients were estimated

simultaneously using the computer programme FRONTIER 4.1 in which the variance

parameters are expressed in terms of δ2s = δ2

v + δ2 and γ = δ/δ2 (Coelli, 1994; Ajibefun,

1998).

3.4.2 The Emperical Stochastic Frontier Cost Production Model

The dual cost frontier production function adopted in estimation of total cost of

production as applied by (Ogundari, 2008; Maurice, 2012) is specified as follows:-

LnCi =β0+β1ƖnF1+β2ƖnF2+β3ƖnF3+ ... β8ƖnF8+Yi+Vi+Ui - - - -

(3.10)

Where:

Ci = Total cost of production of the ith farmers (N)

F1= Cost of acquired land (N)

F2= Cost of fertilizer (N)

F3= Cost of groundnut seed (N)

F4= Cost of other agro-chemicals (N)

F5= Cost of family labour used (in-Man-days)

F6= Cost of hired labour used (in-Man-days)

F7= Cost of ploughing (Animal traction/tractor) (N)

F8= Cost of transport (N)

Y i = Is the output of ith farmer (kg)

V i and Ui = are as previously defined

47

The inefficiency model is defined by:-

Ui = δ0+δ1z1+δ2z2+δ3z3+δ4z4+δ5z5 - - - - - (3.11)

Where:

Ui = Cost of efficiency effect

Z1 = Age of farmer (in years)

Z2 = Farming experience (in years)

Z3 = Literacy level (measured in years spent in school)

Z4 = Family size (total number of persons in a household)

Z5 = Frequency of extension contact/number of visits

Given the functional and distributional assumption of maximum likelihood

estimate (MLE) for all parameters of the stochastic frontier production function defined

by equation (3.1) the farm frontier production function, the corresponding cost function

(3.2) and minimum cost of input (3.3) the technical efficiency (TE) is defined by

equation (3.4) and (3.5), the variance parameter is defined by equation (3.6) and (3.7),

the inefficiency model defined by equation (3.8) and (3.9) and the stochastic cost

function (3.10) and in the inefficiency model of cost function (3.11) is estimated using

the computer program, frontier 4.1 (Coelli, 1994; Ajibefun, 1998; Ogundari and Ojo,

2007).

3.4.3 Profitability Analysis Using Budgeting Techniques

The budgeting techniques employed for the analysis is gross margin. The model

was used to determine the profitability of groundnut production in the study area. Gross

margin is the differences between total revenue and total variable costs expressed on per

hectares basis (Adebayo, 2006; Bucket, 1988, Olukosi and Erhabor, 2005, Taphee et al.,

2013). Algebraically,

GM = ∑PiA i – ∑Kjχj - - - - - - - - (3.12)

Where:

GM = Farm gross margin (N/ha)

Pi = Unit price of output (kg/ha)

48

Qi = Quantity of output (Kg/ha)

K j = Unit cost of variable input j (Kg/ha)

χi = Quantity of variable input (Kg/ha)

PiQi = Total revenue (N/ha)

KjZj = Total cost associated with variable input j (N/ha)

∑ = Summation sign

3.4.4 Profit Function (⌅⌅⌅⌅)

Profit function relates maximize profit (or minimize cost) to the price of

product(s) and input(s), (Sankhayan, 1988). The function was used to determine the

influence of the production cost on the proceeds of the product (groundnut) realised.

Hence, the modal was used to achieve objective five (5). The generalised profit function

is given as:

⌅ = Pyf (χi ………. χn, Z) - ∑Piχi or ⌅ = R – C - - - (3.13)

i = 1 – 6

Where:

⌅ = Profit (N)

Py = Unit price of output (N)

Piχi= Cost of variable (N)

Pi = Unit price of ith variable input (N)

Zi = fixed price (N)

χi = Variable input (N)

Thus, the revenue equation is express as:

TC = Piχi+P2χ2+P3χ3+P4χ4+P5χ5+P6χ6 - - - - (3.14)

49

Where:

PyY = Total cost (N)

P1χi = Cost of groundnut seeds (N)

P2χ2 = Cost of labour used (in mandays/hours)

P3χ3= Cost of fertilizer used (Kg/ha)

P4χ4 = Cost of transportation (N)

P5χ5 = Cost of storage (N)

P6χ6 = Fixed capital asset

50

CHAPTER FOUR

RESULTS AND DISCUSSION

4.1 Socio-Economic Characteristics of Groundnut Farmers

4.1.1 Age of Groundnut Farmers

The result of this study showed that majority (66.67%) of the groundnut farmers

are between the age bracket of 31-40 years. The mean age of the respondents was 35

years. The result, therefore indicated that most of the farmers are young and energetic,

since they are in their active age. Thus, labour productivity of groundnut farmers is

expected to be high. The result, conforms with the work of Adebayo and Onu (1999) that

age is one of the socio-economic attributes which affects the level of farmers’

productivity. The result also agreed with the findings of Olaf et al., (2003) who indicated

that socio-economic factor such as age have significant effect on the technical efficiency

of farmers. In the same vein, the result is in conformity with work of Tashikalma (2010)

which found out that small and medium scale groundnut farmers in Adamawa state,

Nigeria had average age of about 33 and 39 years respectively.

4.1.2 Gender of the Groundnut Farmers

Majority (about 62.22%) of the respondents were female (Table 4.1). This

implies that groundnut production in Taraba State, Nigeria is mainly dominated by

female. The result agrees with the work of Odii (1992), who observed that female

farmers contributed significantly to agricultural production. Thus, the results may not be

unconnected with the fact that, the picking and drying of groundnut require much time

and labour which of course will be endured by female farmers.

4.1.3 Marital Status of Groundnut Farmers

The result revealed that 14.81% of the sampled groundnut farmers were single.

About 63.71% were married while 18.52% and 2.96% were widowed and divorced

respectively.

4.1.4 Household Size of the Respondents

Majority of the groundnut farmers (88.15%) have household size of 1-10 people

while 11.85% have 11 and above in their household. The mean of the family size is 5

people. From the result, it is realized that the respondents’ large family size is above the

51

recommended average size of four (4) per family in Nigeria. Family labour is recognized

as a source of labour supply in small holder food crop production in most part of Africa

with Nigeria inclusive. The result agrees with the finding of Otitoju and Arene (2010)

that majority of the respondents (medium-scale soybean farmers in Benue State, Nigeria)

had average family size of about 7 people. And this also agrees with the work of

Abdullai and Huffman (2000) that the rice farmers in Northern Ghana had average

household size of about 8. However, the result disagrees with the findings of Ntare

(2007) which found that the average family size of Arabica coffee producers in

Cameroon was 11. Also on the contrary, large household size of the family may mean

additional responsibility on the household heads according to Adebayo, (2006).

4.1.5 Level of Education of Groundnut Farmers

The result in Table 4.1 showed that 22.96% of the groundnut farmers in the area

never attended school, that is, they had no formal education, while about 77.04% of the

respondents had one form of formal education or the order. Out of the 77.04% of the

respondents that had formal education, about 35.56% attended primary school, 23.70%

attended secondary school while about 17.78% attended higher institution at various

levels. The mean years of schooling of the groundnut farmers in the study area was about

9 years. Education has been found to be a vital component in technology adoption in

agriculture (Alabi and Aruna, 2006). The result implies that most of the farmers have

attained education up to secondary school or its equivalents. The result agrees with the

findings of Nwaru and Onuoha (2010) that found out that a greater percentage of small

holder food crop farmers (both credit using farmers and non-credit farmers) in Imo State,

Nigeria only attended secondary school or its equivalent with average of 10 years of

schooling and also the findings of Ogundari (2008) who stated that rain fed rice farmers

in Nigeria had the average years of school of 10 years. The result also concurred with the

works of Amaza (2000), Adwuyi and Okunmadewa (2001) and Renato and Euan (2004)

that found out that education is a significant factor which has positive impact on farmers’

productivity and efficiency. Thus, the result suggests that majority of the groundnut

farmers in the study area could at least read and write.

4.1.6 Extension Contact of the Respondents

Majority (39.26%) of the respondents had contact with extension services in the

study area. About 29.62% of the respondents had contact with extension agent within the

52

range of 6 to 10 times in the cropping season, 25.56% of the groundnut farmers were

visited by extension agent within the range of 1 to 5 times during the cropping season

while 5.56% of the respondents had no contact with extension services during the

cropping season. On the average, respondents in the study area had contact with

extension personnel about 9 times. Adewumi and Okunmadewa reported that the

efficiency level of farmers was significantly affected by extension services. The result

therefore disagreed with the findings of Nchare (2007) who noted that the average

contacts with extension workers by Arabica coffee farmers in Cameroon was 3 times.

Access to extension services as established in the literature afford the farmers the

opportunity to be better informed about production techniques as well acquire basic

training and skills on how best to allocate resource to achieve higher efficiency and

productivity. Therefore, if farmers have frequent contact with extension services, they

are likely to be more efficient in the use of resources and invariably profitable in the

farming venture.

4.1.7 Farming Experience of the Respondents

The result showed that majority (80%) of the respondents have been cultivating

groundnut for about 6-11 years. Only 20% have been in the business of groundnut

production for a period of 1-5 years. The mean farming experience is 9 years. This

implies that farmers in the study area have acquired enough experience in groundnut

production, therefore, adoption of new innovations will pose no problem. The result is in

consonance with the study of Kwaghe (2006) that found out that farmers with many

years of experience in farming are more willing to change towards adopting current

recommended techniques. The result is also in line with the work of Adewuyi and

Okunmadewa (2001) who reported a positive relationship between farming experience

and technical efficiency. Tashikalma (2011) reported that farmers with more years of

farming experience in terms of farm operations, handle better, compared to farmers with

few years of farming experience. In the same vein, Asumugha and Ujoku (2007) asserted

that faming experience influence decision making in relation to risk aversion in farm

business.

53

4.1.8 Farm Size of the Groundnut Farmers

Majority (65.19%) of the respondents in Table 4.1 had farm sizes of between 1-

1.5 hectares while 24.81% have farm size of less than 1 hectare and 10% of the

respondents have farm size of up to 2 hectares. The result therefore implies that, majority

of the farmers in the study area are small-scale farmers. This result agrees with the

findings of Ibrahim (2004), who stated that small-scale farmers are those that cultivate

farm land not more than 2 hectares.

4.1.9 Labour Source of the Respondents

Labour is a limiting factor of production in the vast majority of West African

farming systems. Labour productivity holds the key to the development of agricultural

Economic (Chianu et al., 2001). Other who holds the same opinion include Adebayo

(2006), Ajibefun and Abdulkadri (2004), Ajibefun et al., (2002), Amaza and Olayemi

(2002), Ogundele and Okoruwa (2006) and Tella (2006) all stressed that labour affected

farm productivity. The result in Table 4.1 showed that majority (51.85%) of the

respondents used family labour which naturally characterized the peasant nature of

Nigerian farmers. The result is in conformity with the findings of Adegeye and Dittoh

(1985) that family labour is the most important component of labour in small holder

farmers’ production in Nigeria and indeed African countries. About 25.93% of the

respondents used hired labour while 22.22% used both the hired and family labour in

groundnut production in the study area.

On the contrary, Abdulrahman (1983) stated that, farmers hire labour when they

assume that increment in cost will result in higher earnings and usually when scale of

operation is large. Also Atare (2005) associated high labour cost to be one of the major

problems of groundnut production.

4.1.10 Source of Finance of the Groundnut Farmers

Access to credit has been empirically proven to be an important factor that

influence productivity. Akinseinde (2006) using Tobit model showed that access to

credit facilities contributed positively to a households’ production efficiency in the

humid forest agro-ecological zone of Nigeria. Similarly, Obwona (2000) reported that

access to credit contributed positively towards the improvement of efficiency among

tobacco farmers in Uganda. Majority (74.07%) of the respondents have personal savings

54

as their source of finance while 19.26% have their source of finance from borrowed

funds and 6.67% have their source from both personal savings and borrowed.

The result indicates that majority of the farmers in the study area have or no

access to credit facilities in the financial institutions. The inaccessibility of the farmers

may largely be due to either ignorance to existence of the facilities or to some extent the

stringent measures that are usually required for obtaining the loan from the banks. These

conditions may include demand for guarantor, collateral securities, high interest rate and

literacy level. Thus, the result agrees with the findings of Awoke (2003) that identified

lack of collateral and high interest rate as some of the major problems of groundnut

producers.

55

Table 4.1: Distribution of Respondents according to their Socio-economic Attributes (n = 270)

Variable Frequency Percentage Age 21-30 31-40 41-50 51 and above Mean age 35 years Gender Male Female Marital Status Single Married Widowed/widowered Divourced Household Size 1-5 6-10 11 and above Mean household size 5 Level of Education No formal education Primary school Secondary school Tertiary Mean literacy level 9 years Frequency of extension contact No Contact 1-5 6-10 11-15 Farming Experience 1-5 6-10 11 and above Mean farming experience 9 years Farm size <1 1-1.5 1.6-2.0 Mean farm size 1.60 hectares Sources of Labour Family Hired Both Sources of Finance (Access to Credit). Personal Savings Borrowed Both

34 180 50 6

102 168

40 172 50 8

178 60 32

62 96 64 48

15 69 80 106

54 170 46

67 176 27

140 70 60

200 52 18

12.59 66.67 18.52 2.22

37.78 62.22

14.81 63.71 18.53 2.96

65.93 22.22 11.85

22.96 35.56 23.70 17.78

5.56 25.56 29.62 39.26

20.00 62.96 17.4

24.81 65.19 10.00

51.85 25.93 22.22

74.07 19.26 6.67

Source: Field Survey, 2012

56

4.2 Effect of Socio-economic Characteristics of Groundnut Farmers Using

Stochastic Frontier Production Function

The maximum likelihood estimates (MLEs) of the stochastic frontier production

function for groundnut farmers are presented in Table 4.2. Four (4) out of the seven

inputs used in the model were statistically significant at varying degrees of probability.

They include farm size, seed, family labour and other agro-chemical. Hired labour,

fertilizer and expenses on ploughing were not statistically significant but positively

related to the output of groundnut in the study area. The variance parameters of the

stochastic frontier production function were represented by sigma squared (δ2) and

gamma (γ). The sigma square (0.431) is statistically different from zero at 1% probability

level. This indicates a good fit and correctness of the specified distributional assumption

of the composite error terms. Also, the variance ratio defined by Gamma (γ) was

estimated at (0.721) and was statistically significant at 1% probability level. The Gamma

(γ) estimated shows the amount of variation arising from technical inefficiencies of the

groundnut farmers. Therefore, the existence of technical inefficiency among groundnut

farmers accounted to about 72% of the variation in the output level. In other words 72%

of the variation in groundnut farms output was attributed to differences in technical

efficiency. Thus, the result in Table 4.2 showed that seed was the most important factor

in groundnut production with an elasticity coefficient of 0.55 implying that a 10%

increase in the quantity of seed would increase output of groundnut by 5.5%. The result

therefore agrees with the findings of Tashikalma (2011) which found that agricultural

productivity can be increased through increase in seed as input. Farm size was the second

most significant factor in groundnut production with a positive elasticity coefficient of

0.25 which was statistically significant at 1% level of probability. The implication is that,

a 10% increase in hectare of land cultivated would increase output of groundnut by

2.5%. This is an indication that land as a factor of production is very vital in groundnut

production in the study area. This result is in conformity with the findings of Awotide

and Adejobi (2006), Ogundari and Ojo (2007), Mesike et al., (2009), and Shehu et al.,

(2007) which found out that farm size is one of the important factors in agricultural

production. Other agricultural (herbicides) was also a significant input in groundnut with

an elasticity coefficient of 0.13 which was statistically significant at 1% probability

level. This implies that a 10% increase in the use of other agrochemical (herbicides) in

groundnut production would increase output by 1.3%. Apart from minimizing

57

expenditure on weeding, the use herbicides reduces stress and fatigue associated with

groundnut production especially land clearing. By implication, the use of herbicides will

enable farmers to cultivate large hectares of land which results in increased output.

The estimated coefficient for family labour 0.11 is statistically significant at 10%

probability level and positively related to the total output. A 10% increase in family

labour would increase output of groundnut by 1.1%. This result is in consonance with the

findings of Adegeye and Dittoh (1985) which found out that, family labour in small

holder farmers’ in Nigeria is one of the important factor of production.

The analysis of the inefficiency parameters is very important as a basis for

informing agricultural policies on what need to be done to improve agricultural

production. The inefficiency parameters as specified are those that relate to farmers

specific socio-economic characteristics which appear to have significant roles in

determining the level of technical efficiency of the farmers. These were examined using

sigma (δ) coefficient. Note that a negative sigma (δ) coefficient implies that the

parameters has a positive effect on efficiency and vice versa.

The estimated coefficient of farming experience was found to be negative and

statistically significant at 1% probability level. This indicates that as the groundnut

farmers get more experience, they are likely to be technically efficient compared to

inexperienced farmers. This result agrees with the findings of Ajibefun and Abdulkadir

(2004), Ajibefun et al., (2002), Idjasa (2007) Ajani (2000) and Adewumi and

Okunmadewa (2001) that found out that efficiency level of farmers was significantly

affected by farming experience.

The coefficient of gender was found to be negative but not statistically significant

at any probability level. The possible explanation is that as more of groundnut farmers

were women and because of their inability to own land like their men counterpart may

likely become hindrance that may bring about technical efficiency. This result agrees

with the findings of Adekanye (1988), Babalola (1988) and Olawoye (1988) which found

out that women have far less access to land and other productive inputs.

Household size has a negative coefficient and statistically significant at 1%

significance level. This implies that farmers with relatively large family size have the

potential to increase farm output. This result is in consonance with the findings of

58

Ya’ashe et al., (2010), Gwandi et al., (2010) and Jude (2011) which found that the mean

household falls between the range of 5 and 9 persons. They also said that, the people

within these study areas appreciate large families ostensibly because of the labour they

stand to benefit.

The estimated coefficient of literacy level of the groundnut farmers was found to

be negative and statistically significant at 5% probability level. The possible explanation

is that farmers with formal education are more likely to be technically efficient compared

with the uneducated ones. The result can be further explained in terms of the enhanced

abilities of farmers with formal education to acquire technical knowledge and adopt new

innovations easily which makes them to be efficient in the use of productive inputs and

other simple machines used in agricultural production. This result conforms with the

findings of Renato and Euan (2004) who reported that education was found to be one of

the significant factors associated with technical efficiency of farmers implying that

human capital is an importance factor in carrying out production and managerial tasks on

groundnut farm.

The coefficient of extension contact was estimated to be negative and statistically

significant at 5% probability level. The possible expectation is that, since extension

contacts/services have influence on adoption of agricultural and farm technologies

among farmers, there is the tendency that farmers who were opportune to be visited or

contacted with the extension agent will readily adopt new innovations. This result agrees

with the findings of Ransom et al., (2003) which found out that contact with extension

significantly and positively affected adoption of improved varieties in hills of Nepal.

Also, in the studies of Adewuyi (2002), Ajani (2000), Amaza (2002) and Awotide (2004)

all reported that efficiency level of farmers was significantly affected by extension

services.

59

Table 4.2: Maximum Likelihood Estimate (MLE) of Stochastic Frontier Production Function for Small-Scale Groundnut Farmers (n = 270)

Variable Parameters Coefficient Std error T-value Constant

Farm size

Seed

Family labour

Hired labour

Fertilizer

Other agrochemical

Expenses on ploughing

Inefficiency effects

Farming experience

Gender

Household size

Extension contact

Literacy level

Age

Access to credit

Variance parameters

Sigma – Squared

Gamma

β0

β1

β 2

β 3

β 4

β 5

β 6

β 7

δ1

δ2

δ3

δ4

δ5

δ6

δ7

δ 2

γ

2.10***

0.253***

0.546**

0.109*

0.0013

0.0021

0.133***

0.0097

-0.29***

-0.17

-0.26***

-0.18**

-0.16**

-0.0044

-0.417

0.431***

0.721***

0.048

0.0084

0.0302

0.0053

0.0016

0.0028

0.0040

0.0015

0.0086

0.0497

0.0094

0.0086

0.0077

0.0055

0.024

0.0018

0.0093

4.41

3.02

1.80

2.03

0.080

0.074

3.29

0.067

3.29

0.033

2.76

2.13

2.05

0.081

1.68

7.32

7.75

Source: Computed from Field Data Computer Frontier 4.1C Version Print-Out

*** Significant at 1%, ** Significant at 5%, * Significant at 10%

4.3 Determination of Technical, Allocative and Economic Efficiencies of

Groundnut Farmers

Stochastic frontier production function and stochastic cost function were used to

determine technical, allocative and economic efficiencies of the groundnut farmers.

4.3.1 Technical Efficiency of Groundnut Farmers

Distribution of farmers’ technical efficiency indices derived from the analysis of

the stochastic frontier production function is presented in Table 4.3. The Table revealed

that, technical efficiency of the sampled farmers was less than one (1.00). This implies

60

that groundnut farmers in the study area are producing below the maximum frontier

output. The range of technical efficiency shows that the most efficient farmer has a

technical efficiency of 0.98, that is (98%) while the least efficient farmer has a technical

efficiency of 0.30, that is (30%) with a mean technical efficiency of 0.77 that is (77%).

The mean technical efficiency of 77% implies that on the average, the farmers were able

to achieve about 77% of optimal output from a given set of inputs under given

technology. The mean TE of 77% indicates a reasonable average level of technical

efficiency on the average farm. Also, the result shows that, the groundnut farmers were

not efficient as their observed output is 23% less than the maximum output. Thus, the

output of the groundnut farmers can be increased by 23% through improved resource

allocation with no additional cost.

The distribution of technical efficiency of farmers showed that about 41.88% had

technical efficiency of 80 percent and above while majority (54.08%) had technical

efficiency within the range of 50-79 percent. This study is in agreement with the findings

of Najafi and Zibadi (1995) who reported that, the mean technical efficiency of wheat

farmers at Far province was 79.7%. Also, the result is in conformity with the study of

Chaovanapoonphol et al., (2005) in Thailand who revealed that the average technical

efficiency of rice farmers was 79%.

Table 4.3: Technical Efficiency Distribution of Groundnut Farmers

Efficiency level Frequency Percentage 0.30 - 0.39

0.40 - 0. 49

0.50 - 0.59

0.60 - 0.69

0.70 - 0.79

0.80 - 0.89

0.90 – 1.00

Total

Minimum

Maximum

Mean

4

8

28

41

77

73

39

270

0.303

0.979

0.769

1.48

2.96

10.37

15.19

28.52

27.04

14.44

100

Source: Computed from Field Data Computer Frontier 4.1C Version Print-Out

61

4.3.2 Determination of Allocative Efficiency using Stochastic Cost Function for

Groundnut Farmers

The maximum likelihood estimates for the parameters of stochastic cost function

used in the contraction of allocative efficiency is presented in Table 4.4. Four (cost of

fertilizer, seed, farming labour and ploughing) estimates of the parameters carried the

expected sign and were statistically significant at varying degrees of probability, which

therefore implies that the factors are important determinants of total cost associated with

groundnut production in the study area, this also means that an increase in these inputs

will lead to increase in the total production cost.

Although four (rent on land, cost of agro-chemicals, hired labour and cost of

transportation) of the estimates carried the expected sign, they were not statistically

significant at any level of probability. The possible explanation is that, though they were

associated with the total cost of production, but, they were not determinants of the total

cost of production in the study area; therefore, an increase in these inputs may not affect

an increase in the total cost of production.

The estimate of sigma square (δ2) was 0.212 and statistically significant at 1%

probability level. This indicates a good fit and correctness of specified distributional

assumption of the composite error term. The estimate of Gamma (γ) on the other hand

was 0.771 and statistically significant at 1% probability level implying that variation in

production cost among the sampled farmers in the study area was distributed to

efficiency variables in the inefficiency cost model. Farming experience (Z2), literacy

level (education) (Z3), household size (Z4) were significant and positively related to cost

efficiency among the sampled farmers because negative coefficient indicates positive

effect on cost efficiency and vice-versa.

The estimated coefficient of farming experience carried negative sign and was

statistically significant at 5%. This indicates that experienced farmers are likely to take

cost decisions that will lead to allocative efficiency compared to farmers who have little

or no experience.

Similarly, the coefficient of education (literacy level) of the sampled farmers

carried the expected sign and was statistically significant at 1% probability level. This

implies that increase in the level of education of farmer’s may tend to decrease the

62

farmer’s cost inefficiency and thus increases his cost efficiency. This is because educated

farmers are more likely to take better decisions concerning input that will minimize cost

and maximize output or profit and/or both.

Also, the coefficient of family size was statistically significant at 5% probability

level. Respondents with relatively large household size are likely to use more of family

labour to reduce the high cost of hired labour thereby enhancing cost efficiency, which

will in turn increase farm output.

4.4 Maximum Likelihood Estimate (MLE) of Stochastic Cost Function for Groundnut Farmers (270)

Variables Parameters Coefficient Std error T-value

Constant

Rent on land

Cost of fertilizer

Cost of groundnut seed

Cost agrochemical

Cost of Family labour

Cost of Hired labour

Cost of ploughing

Cost of transportation

Inefficiency model

Age

Farming experience

Literacy level

Household size

Extension contact

Variance parameters

Sigma – Squared

Gamma

Likelihood function

β0

β1

β 2

β 3

β 4

β 5

β 6

β 7

β 8

δ1

δ2

δ3

δ4

δ5

δ2

γ

3.47***

0.0028

0.405**

0.208**

0.000021

0.0085***

0.0063

0.149***

0.153

-0.0075

-0.237**

-0.316***

-0.162**

-0.0043

0.212***

0.771***

137.16

0.440

0.0034

0.0174

0.0083

0.0056

0.0027

0.364

0.0041

0.0108

0.0085

0.106

0.0081

0.0070

0.0057

0.00019

0.189

7.90

0.83

2.33

2.52

0.0039

3.15

0.17

3.67

1.42

-0.895

-2.241

-3.874

-2.313

-0.754

11.429

4.075

Source: Computed from Field Data Computer Frontier 4.1C Version Print-Out

*** Significant at 1%, ** Significant at 5%, * Significant at 10%

63

4.3.3: Frequency Distribution of Allocative Efficiency

The allocative indices derived from the analysis of the stochastic cost function is

presented in Table 4.5. The minimum and maximum farmers’ allocative efficiency of

0.506 and 0.883 showed that there was high variation between the least allocatively

efficient groundnut farmer and the best allocatively efficient farmer. The least

allocatively efficient farmer would require about 49%, (that is, 1.0 – 0.51) to achieve

allocative efficient gain while the best allocatively efficient farmer would require just

about 12% to attain maximum allocative efficiency level.

The inference drawn shows that 14.07% of the sampled farmers had allocative

efficiency of more than 50%, while 29.63% had allocative efficiency of 60–69%.

However, majority of the respondents (56.30%) had allocative efficiency of 70% and

above. This result indicates that although the sampled farmers in the study area were

somehow allocatively efficient in producing a given level of output, there are still

considerable potentials for farmers to improve in the allocation of resources so as to

minimize resource wastage associated with production process and consequently

reducing production cost.

Table 4.5: Allocative Efficiency Distribution of Groundnut Farmers

Efficiency level Frequency Percentage 0.50 – 0.59

0.60 – 0.69

0.70 – 0.79

0.80 – 0.89

0.90 – 1.00

Total

Minimum

Maximum

Mean

38

80

103

49

00

270

0.506

0.883

0.695

14.07

29.63

38.15

18.15

00.00

100

Source: Computed from Field Data Computer Frontier 4.1C Version Print-Out

4.3.4 Frequency Distribution of Economic Efficiency of Groundnut Farmers

Result in Table 4.6 shows that economic efficiency of groundnut farmers in the

study area ranged from 0.220 – 0.861 with a mean of 0.54. Majority of sampled farmers

64

(55.55%), had economic efficiency of 50 – 69%, while 33.33% had economic efficiency

of less than 50%. However, only 11.12% of the respondents had economic efficiency of

0.70 – 1.00 (70 – 100%). The mean of 0.54 implies that groundnut farmers in the study

area were not economically efficient in the use of productive resources. There is a high

magnitude of variation between the least economically efficient farmer and the best

economically efficient farmer which may perhaps be due to misallocation and/or under

utilization of productive resources. The resultant effect is high cost per unit of output and

hence the inability to maximize profit.

The inference drawn, shows that for average groundnut farmers in the study area

to attain maximum economic efficiency level, the farmers must experience efficiency

gain of 46% (1 – 0.54), this means that the overall economic efficiency of farmers in the

study area could be increased by 46% through reduction in production cost; this would

maximize profit from groundnut production activities. The least economically efficient

farmers will require efficiency gain of about 78% (1.00 – 0.22) to be able to attain the

maximum efficiency level while it will take just about 14% for the best economically

efficient farmers to attain maximum economic efficiency level. This implies that there

exist potentials for farmers in the study area to be economically efficient in groundnut

production.

Table 4.6: Economic Efficiency Distribution of Groundnut Farmers

Efficiency level Frequency Percentage 0.20 – 0.29 0.30 – 0.39 0.40 – 0.49 0.50 – 0.59 0.60 – 0.69 0.70 – 0.79 0.80 – 0.89 0.90 – 1.00 Total Minimum Maximum Mean

06 26 58 88 62 28 02 00 270

0.220 0.861 0.541

2.22 9.63 21.48 32.59 22.96 10.37 0.75 0.00 100

Source: Computed from Field Data Computer Frontier 4.1C Version Print-Out

65

4.4 Profitability Analysis of the groundnut farmers

The result of the average cost and returns associated with groundnut production

in the study area as presented in Table 4.4 showed that the total variable cost was

N162,771.84 and a gross margin of N47, 265.16 per hectare respectively. Labour

constituted the highest production cost with 70.67%. This implies that labour was a

serious requirement for groundnut production in the study area. Inputs cost constitutes

about 9.37% while 19.96% is made up of cost of storage and other operating expenses,

such as cost of transportation, cost of rent land, cost of bags etc. The total variable cost

forms the total cost of production as there is no fixed cost. The none-inclusion of fixed

cost is attributed to the negligible proportion of fixed capital used in subsistence

agriculture.

The return on investment, indicated that for every one naira invested in groundnut

production, the farmer gains N0.29. The implication is that groundnut production in the

study area is profitable. This result is in agreement with the findings of Awoke (2003) in

a study of production analysis of groundnut in Ezeagu Local Government Area of Enugu

State, Nigeria; that found that groundnut production was a profitable enterprise with a

gross margin of N8, 466.00 per hectare.

66

Table 4.7: Average Costs and Returns of Groundnut Farmers per hectare (n = 270)

Variable Unit Quantity Price (N) Value Percentage

Gross output

Operating Capital

Groundnut seed

Fertilizer

Herbicide

Storage

Other operating expense

Total Operating Capital

Labour Input

Land clearing

Planting

Weeding

Fertilizer application

Harvesting

Farm gate processing

Total labour Cost

TVC (Total Variable Costs)

GM = TR – TVC

GM ROI

Kg

Kg

Kg

Litres

Mandays

Mandays

Mandays

Mandays

Mandays

Mandays

840.15

30

50

4

33.12

32.76

26.60

24.75

45.63

11.41

250

300

0.35

1.125

750.75

577

920.13

460.40

670.12

420.18

210, 037

9000

1750

4500

600

26500

47, 750

24,864.84

18,910.71

24,475.45

11,394.90

30,581.68

4,794.25

115,021.84

162,771.84

47,265.16

0.29

5.53

1.08

2.76

3.68

16.28

15.28

11.62

15.04

7.00

18.78

2.95

Source: Computed from field data, 2012

4.5 Profit (⌅⌅⌅⌅) and Cost Relationship in Groundnut Production

Profit (⌅) function was used to analyse the influence of groundnut production

cost on the profit realized. Four functional forms (linear, exponential, semi-logarithm

and double logarithm) were tried for the analysis of the relationship between farmers’

profit and cost of production inputs.

The result of the analysis indicated that of the four functional form tried, double

logarithm function gave the best result based on economic, econometric and statistical

criteria, hence was chosen as the lead equation. The coefficient of multiple determination

(R2) shows that 78.3% of the variation in groundnut profit among the respondents is

explained by the variables included in the model, therefore, the variables used fitted well

67

into the model. Furthermore, the overall model as measured by the F-statistics was

significant at 1% level of probability.

Of the six independent variables used in the analysis, four were significant at 1%

level of probability implying that increase in the use of these variables would affect

groundnut profit. Costs of seed and transport were positively related to groundnut profit,

while labour cost and storage cost were inversely related to groundnut profit.

An inverse in the quantity of seed used in groundnut production is expected to

bring about increase in the cost of seed. Theoretically, all things being equal, there is an

inverse relationship between profit and cost, but in the study area, seed is underutilized

vis-a-viz, the area of land put under cultivation, therefore, increasing seed quantity (by

implication increasing seed cost) would result in increasing groundnut density per unit

area which ultimately would increase groundnut yield per hectare and in turn increase

profit. In double logarithm, the coefficients are direct elasticities, hence, a 1% increase in

the cost of seed would bring about 1.36% increase in profit.

In a related development, the positive coefficient of transportation cost implies

that an increase in cost of transportation would bring about increase in profit, thus, is true

because the transportation cost burden is systematically transferred to the final

consumers in the prices paid by them per every unit of the commodity bought. A 1%

increase in transportation cost will bring about a 0.75% increase in profit.

However, labour cost and storage cost were inversely related to groundnut profit,

implying that increasing cost of these variables would bring about decrease in profit and

vice versa. Labour cost was measured as the sum of both cost of hired labour and inputed

cost of family labour used in production. In the study area, most of the respondents

resorted to the use of family labour probably due to the high cost of hired labour. In the

maximum likelihood estimate (MLE) (see Table 4.2) family labour measured in

Mandays was underutilized, while hired labour also measured in Mandays was not

significant, valuing and lumping labour could result into over utilization of labour, and

additional increase in the use of this variable would decrease groundnut profit.

A 1% increase in cost of labour would bring about a 0.62% decrease in groundnut

profit. Consequently, a 1% increase in storage cost would bring about a 0.03% decrease

in groundnut profit. The equation of the regression model is presented thus:

68

LnY = 0.123 + 1.359 lnχ1 – 0.617 lnχ2

(0.294) (10.153)*** (-9.120)***

- 0.002 lnχ3 + 0.752 lnχ4 – 0.030 lnχ5 + 0.042 lnχ6

(-0.667) (7.165)*** (-3.099)*** (1.558)

*** = Significant at 1% level of probability.

Figures in parentheses are the corresponding t-values.

69

Table 4.8: Summary of Regression Analysis (n = 270)

Explanatory Variables

Functional Forms Constant χ1 χ 2 χ 3 χ 4 χ 5 χ 6 R2 R-2 F

Linear

Exponential

Semi-logarithm

+ Double-logarithm

-9933.799

(-0.946)

4.199

(96.336)***

-460609.0

(-4.094)***

0.123

(0.294)

19.539

(17.372)***

0.000

(23.208)***

81346.518

(2.252)**

1.369

(10.153)***

-1.189

(-10.715)***

-4.260

(-9.240)***

-146321

(-8.006)***

-0.617

(-9.120)***

5.948

(5.169)***

5.320

(1.114)

549.886

(0.576)

-0.002

(-0.667)

10.330

(4.238)***

1.090

(1.080)

801408.26

(10.632)***

0.752

(7.165)***

-0.661

(-1.426)

-4.077

(0.035)**

-4134.968

(-1.581)

-0.030

(-3.099)***

0.977

(0.349)

1.830

(0.875)

3945.173

(0.536)

0.042

(1.558)

0.747

0.779

0.694

0.788

0.741

0.774

0.687

0.783***

129.58

154.76

99.59

162.62***

Source: Regression Output *** = Significant at 1% ** = Significant at 5% * = Significant at 10% Figures in parenthesis are corresponding t – statistics + = Lead equation

70

4.6 Constraints Associated with Groundnut Product

The major constraints confronting the groundnut farmers in the study area as

indicated in Table 4.6 were pests and diseases infestation, lack of storage facilities,

inadequate research and extension services, low price, inadequate credit facilities, lack of

improved varieties, land tenure system, shortage of labour, inaccessibility of farm inputs and

inadequate rainfall.

Pests and diseases infestation (about 18.10 percent) had been identified as the most

serious problem facing groundnut farmers and ranked first among the list of problem faced

by the respondents. This conforms with the finding of Craufrd et al., (2006), Garba et al.,

(2005), Lokhande & Newaskar, (2000), Upadhyay et al., (1989) and ICRISAT (1989),

which stated that early and late leaf spots commonly called Tick Disease caused huge yield

loss in groundnut due to defoliation and insect pests on groundnut such as Hobber (Heliothis

amigera) and thus, aphid have become a serious problem in groundnut in recent years. Lack

of storage facilities (13.57%) ranked second . lack of storage facilities may be attributed to

the reason why famers dispose off most of their groundnut at the farm gate and probably the

reason why output is sold at industry-determined price and profit as noted by Eyo (2004).

The inadequate research and extension services which ranked third among the

respondents is prevalent in most communities in Nigeria today. Nowadays, one hardly see

change agents in the villages as it is used to be. Even the Small Plot Adoption Technologies

(SPATS) which are used for practical teachings on farmers’ farms are invisible these days.

Instead, one sees or notices extension agents in the towns and cities where they stayed to

enjoy social amenities. This may not be unconnected with the fact that the changed agents

are no longer empowered to be motivated to carry out their civic duties. This is a serious

threat to agricultural growth and development as most farmers cannot apply if at all there are

innovations on their farms which hitherto required the attention of the extension agents. The

situation according to Adewumi and Okunmadewa (2001) have devastating effect on the

overall economic efficiency level of farmers. Low price (10.77%) of groundnut as a result of

no ready market revealed fourth among the farmers. This, Freeman et al., (1999) observed

that low produce prices and limited modeling opportunities reduced incentives for small

holders to invest in productivity enhancing technologies.

71

Inadequate credit facilities (9.50%) is another major problem among the respondents

in the study area. This may be the reason why farmers could not afford to use modern farm

inputs such as high yielding seed varieties and improved farm practice such as irrigation and

farm management practices as identified by Diop et al., (2004). The study also revealed that

land tenure system (7.87%) is another constraint to groundnut production in the study area.

The land tenure system may be the reason why farm sizes of the groundnut farmers’ are

small. The constraint thus agreed with the works of Abdullahi (1981) and Olukosi et al.,

(2007), which stated that, although the country is endowed with agricultural land, but, the

right to ownership of the land and ethnic boundaries make it difficult for farmers to easily

acquire land for agricultural purposes outside their cultural location. The shortage of labour

(7.61%) in the study area may be attributed to peasant farming nature of the respondents.

The findings agrees with the studies of Ntare (2005) and Misari et al., (1988) which

observed that groundnut production is associated with high labour cost.

On the inaccessibility of farm inputs (7.24%) by famers in the study area, may

probably be the reason why, the groundnut production is suffered from production trend as

in the findings of Ntare, (2005). Also Misari et al., (1988) associated groundnut production

problems to inadequate farm inputs. Also inadequate rainfall (5.61%) was identified as one

of the major constraints of groundnut production in the study area. The result is in

conformity with the findings of Mahmoud et al., (1992) who reported that low yield of

groundnut has been attributed to unreliable rains.

72

Table 4.9: Distribution of Respondents Based on Constraints Associated with

Groundnut Production (n = 270)

Major constraints Frequency Percentage (%) Rank **

- Pest and diseases infestation

- Lack of storage facilities

- Inadequate research and extension

services

- Low price

- Inadequate credit facilities

- Lack f improve varieties

- Land tenure system

- Shortage of labour

- Inaccessibility of farm inputs

- Inadequate rainfall

200

150

120

119

105

95

87

84

80

62

18.10

13.57

10.86

10.77

9.50

8.87

7.87

7.61

7.24

5.61

1

2

3

4

5

6

7

8

9

10

Total 1105* 100

Source: Field Survey, 2012

** Rank in descending order

* Multiple responses

73

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

5.1 Summary of Major Findings

The study was conducted to analysed resource productivity and efficiency of small-

scale groundnut farmers in Taraba State Nigeria. A multi-stage random sampling technique

was used to select 270 small-scale groundnut farmers in the three (3) TADP zones based on

their prominence in groundnut production. Structured interview scheduled was used to elicit

the required information from the selected respondents.

Both descriptive and relevant inferential statistics such as frequency, percentages,

mean, gross margin analysis, profit function, stochastic frontier production and stochastic

cost function were used for data analyses. Socio-economic, farm specific and institutional

characteristics and resources used constituted the explanatory variables for the study. Major

possible constraints confronting the small-scale groundnut farmers were also identified.

Analysis of the socio-economic characteristics of the small-scale groundnut farmers

in the study area revealed that 66.66% of the respondents fall between age ranged of 31 – 41

years with the mean age of 35 years. Females (about 66.22%) dominated groundnut

production in the study area, 63.71% were married, 88.15% had family sizes of 1-10 people

in their households, 77.04% had one form of education and the other. The result further

shows that majority 39.26% were visited by extension agent in the study area, 80% had 6 –

11 years of farming experience, 65.19% had farm sizes of between 1 – 1.5 hectares, 51.85%

used family labour while 74.07% had personal savings as source of finance.

The Maximum Likelihood Estimate (MLE) of the parameters of the stochastic

frontier production function indicated that the coefficients of the production factors had

expected signs. Farm size, seed, family labour and other agrochemicals have significant

relationship with groundnut production at various probability levels. The mean technical

efficiency was 0.769 with minimum and maximum efficiencies of 0.303 and 0.979. The

inefficiency model showed that farming experience, household size, extension contact and

education were the variables that increased the technical efficiency of the respondents. The

maximum likelihood estimate of the stochastic cost function indicated that the coefficients

74

of cost of fertilizer, seed, family labour and ploughing carried the expected signs and were

related with cost of groundnut production. The mean allocative efficiency was 0.695 with

minimum and maximum allocative efficiencies of 0.506 and 0.883 respectively. Farming

experience, literacy level, family size were the significant factors influencing the allocative

efficiency of groundnut farmers.

The economic efficiency of groundnut farmers in the study area ranged from 0.220 –

0.861 with a mean of 0.54. The mean economic efficiency of 0.54 implies that groundnut

farmers in the study area are economically inefficient in the use of productive resources. The

study identified pests and diseases infestation, lack of storage facilities, inadequate research

and extension services, low price, inadequate credit facilities, lack of improved varieties,

land tenure system, shortage of labour, inaccessibility to farm inputs and inadequate rainfall

as the major constraints of groundnut farmers.

The Gross Margin analysis shows that a Total of Variable Cost (TVC) was N162,

771.84 per hectare with a gross margin of N47, 265.16. Returns on investment was N0.29

implying N47, 265.16 that for every Naira spent there was a gain of 29 kobo which means,

groundnut production in the study area is a profitable venture.

The result of profit function analysis revealed that Double Logarithm functional form

with R2 of 0.788 (78.8%) was chosen as the best fit equation for the analysis. The study

therefore, established that costs of seed, labour, transport and storage were positively related

to groundnut profit and significant at 1% level of probability. It also shows that, the

variables used fitted well into the model with F-statistics significant at 1% level of

probability, thus, increase in the use of these variables would affect groundnut profit.

5.2 Conclusion

Analysis of the result revealed that groundnut production is dominated by female

who are married and within the age bracket of 31 – 40 years. Most of them have attended

one form of education or the other, with farming experience of 6 – 11 years. Groundnut

production in the study area is a profitable venture. Seed, transport, storage and labour costs

are related to groundnut profit in the study area. Farm size, seed, family labor, and other

agrochemicals have relationship with groundnut output at various probability levels.

75

Farmers were not fully efficient, thus, education, extension contact, family size and family

labour were the variables that increase technical efficiency of the respondents. However,

farming experience, family size and education were significant factors influencing allocative

efficiency of groundnut farmers. Also on the overall, the farmers in the study area were not

economically efficient. Pests and disease infestation, lack of storage facilities, extension

services, inadequate credit facilities, low price, inaccessibility, land tenure system were the

major constraints of groundnut farmers. The gross margin was N47,265.16 with return on

naira invested of N0.29. Seeds, transportation, storage and labour costs were the factors that

influenced the profitability of groundnut production. Pests and diseases infestation, lack of

storage facilities, extension services, inadequate credit facilities, low price, inaccessibility,

land tenure system were the major constraints of groundnut farmers.

5.3 Recommendations

• Based on the results of socio-economic characteristics of groundnut farmers in the

area which found extension contact to be relevant, thus, there is a need of

government support in term of revitalization and prioritizing funding of extension

delivery system of the state owned agricultural development programme. Also

literacy level of the farmers was found to be on important factor that increases

groundnut out in the area, there is need for the farmer to be given proper orientation

and/or basic trainings in major farm management technjques, this will help increase

their productivity, hence be more efficient.

• From the results obtained on the technical, allocative and economic efficiencies

which showed that the groundnut farmers are not efficient, could be as a result of

lack of input and other production facilities. Therefore, efforts geared towards

ensuring incentives such as loans and other credit facilities should be made available

so as to improve their efficiency.

• Since production of groundnut is profitable in the area, it means if government and

non-governmental agencies will encourage farmers, it will go a long way to help

them produce more and generate more incomes for their wellbeing.

• Results obtained from the profit function indicated that costs of seeds, transportation,

storage and labour were factors that increased profit on groundnut product ion in the

76

area. This implied that improvement on these inputs if made by the government and

non-government agencies will make the groundnut farmers to stay on the business.

Also constraints such as pests and diseases, inadequate research and extension

services, lack of storage facilities should be taken care of by government and non-

governmental agencies so as to improve groundnut production.

5.4 Contribution to Knowledge

The results obtained from the study created awareness that women and adults can be

part of policy reformation in groundnut production in the state. The study added idea on how

effective resources were allocated for maximum production of groundnut which helps in

maximum utilization of scarce resources available. Knowing the profitability level of

groundnut production in the state, will encourage more investors to invest and participate in

groundnut production in the state. This study has also contributed to knowledge by

proffering solution to major problems identified as policy makers may use this result as a

guide in policy reformation. Finally, it has added to literature, relevant information on

groundnut production in Taraba State, Nigeria.

5.5 Areas for Future Research:

This study focused mainly on resource productivity and efficiency of groundnut

farmers in Taraba State. Other areas needed future research include: trend of groundnut

production in the state, resource productivity and efficiency of groundnut farmers in

northern states of Nigeria can be carried out to know whether there will be variations among

the states.

77

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