GENDER AND THE TECHNICAL EFFICIENCY OF CASSAVA PRODUCTION

IN OLUYOLE AND AKINYELE LOCAL GOVERNMENT AREAS OF OYO STATE,

NIGERIA.

CHAPTER ONE

1.0 Introduction1.1 Historical Background and Importance of Cassava in Nigeria1.2 Statement of the Problem 1.3 Objective of the study1.4 Hypotheses of the study1.5 The Significance of the Study

CHAPTER TWO

2.0 LITERATURE REVIEW2.1 Cassava Production and the Nigerian Agricultural Sector2.1.1 Contribution of Cassava to Household Food Security2.2 Marketing of Fresh Cassava Tubers2.3 Gender and Cassava Production in Nigeria2.4 Agronomic and Economic challenges of Cassava Production in Nigeria 2.5 Roles of Women in the Nigerian Agricultural Economy2.5.1 Gender and Productivity Differential among Cassava Farmers.2.5.2 Women’s Access to Agricultural Production Resources in Nigeria2.6 Concept of Efficiency2.6.1 Techniques of Efficiency Measurement

2.6.2 Review of Production Frontier Models

2.7 Stochastic Frontier Production Function: Technical Efficiency

2.7.1 Technical Efficiency

2.7.2 Inferential Statistical Analysis

2.8 The Empirical Frameworks on Gender and Technical Efficiency

CHAPTER THREE

3.0 RESEARCH METHODOLOGY

3.1 The Study Area

3.2 Sources and Type of Data

3.3 Sampling Technique

3.4.0 Methods of Data Analysis

3.4.1 Descriptive Statistics

3.4.2 Budgeting Technique

3.4.3.0 Efficiency Determination

3.4.3.1 Models Specification

3.4.3.2 The Inefficiency Model

CHAPTER FOUR

4.0 RESULTS AND DISCUSSION

4.1 Socio-Economic Characteristics of the Male and Female Cassava Farmers in Oluyole and Akinyele Local Government Areas of Oyo State.

4.1.1 Distribution of Respondents by Age

4.1.2 Distribution of Respondents by Level of Education

4.1.3 Distribution of the Respondents according to their Farming Experience

4.1.4 Distribution of the Respondents by Sex

4.1.5 Distribution of Respondents by Marital Status

4.1.6 Distribution of Respondents by Household size

4.1.7 Distribution of Respondents According to their Occupation

4.1.8 Distribution of Respondents According to Farm Size

4.1.9 Distribution of Mode of Land Acquisition for Cassava Cultivation

4.1.10 Distribution of Respondents by Access to Extension Services

4.1.11 Distribution of Respondents by the Quantity of Fertilizer Used.

4.1.12 Distribution of Respondent by the Quantity of Pesticide Used.

4.1.13 Distribution of Respondents by the Quantity of Pesticide Used.

4.1.14 Distribution of Respondents According to Sources of Credit

4.1.15 Distribution of Respondents by the Amount of Credit Obtained

4.2 Gross Margin Analysis

4.3.0 The Stochastic Frontier Production Function Analysis

4.3.1 Signs and Significance of Estimates of Stochastic Frontier Production Function(i.e. Cobb-Douglas Frontier Function Type)

4.3.2 Goodness of Fit

4.3.3 The estimated Gamma () Parameter

4.4 Inefficiency Model

4.5.0 Productivity Analysis

4.5.1 Elasticities (εP) and Returns To Scale (RTS) of Cassava Production of the Male and Female Farmers in Oluyole and Akinyele Local Government of Oyo State.

4.5.2 Elasticities of Production (εP)

4.5.3 Returns To Scale (RTS)

4.6 Efficiency Analysis

4.6.1 Technical Efficiency Analysis of Male and Female Cassava Farmers in the Study Area

4.7.0 Test of Hypotheses

4.7.1 Test of Hypothesis for the Absence of Inefficiency Effects

4.7.2 Test of the Significance of Coefficients of the Socio-Economic Variables of the Inefficiency Model

4.7.3 Test of Hypothesis on the Significant Difference of Mean Technical Efficiency of the Male and Female Farmers in the Study Area.

CHAPTER FIVE

5.0 Summary, Conclusions and Recommendation

5.1 Summary of Findings

5.2 Conclusions

5.3 Policy Implications and Recommendations

5.4 Suggestions for Further Studies

REFERENCESAppendix

List Of Tables

Table 1: Age Distribution of Respondents

Table 2: Educational Level Distribution of Respondents

Table 3: Distribution of Respondents According to their Years of Farming Experience

Table 4: Sex Distribution of Respondents According To L.G.A

Table 5: Distribution of Respondents by Marital Status

Table 6: Distribution of Respondents According to Household Size

Table 7: Distribution of the Male and Female Cassava Farmers According to their Occupation Type.

Table 8: Farm Size Distribution of Respondents

Table 9: Distribution of Respondents by Mode of Land Acquisition

Table 10: Distribution of Respondents’ Access to Extension Services

Table 11: Distribution of Respondents by the Quantity of Fertilizer Used.

Table 12: Distribution of Respondents by the Quantity of Herbicide Used.

Table 13: Distribution of Respondents by the Quantity of Pesticide Used.

Table 14: Distribution of Respondents by their Sources of Credit Facilities

Table 15: Distribution of Respondents by the Amount of Credit Obtained.

Table 16: Costs and Returns per Hectare of the Respondents in Oluyole and

Akinyele Local Government Areas of Oyo State

Table 17: Maximum Likelihood Estimates for the Parameters of the Stochastic Frontier

Production Function for Male Cassava Farmers in the Study Area.

Table 18: Maximum Likelihood Estimates for the Parameters of the Stochastic Frontier

Production Function for Female Cassava Farmers in the Study Area

Table 19: Maximum Likelihood Estimates for the Parameters of the Stochastic Frontier

Production Function for Pooled Cassava Farmers in the Study Area.

Table 20: Expected Signs for Variables Influencing Technical Inefficiency

Table 21: Elasticities (εP) and Returns-to-Scale (RTS) of the Male and Female Cassava

Farmers in Oluyole and Akinyele Local Government Areas of Oyo State.

Table 22: Decile Range of Frequency Distribution of Technical Efficiencies of the Male

Cassava Farmers in Oluyole and Akinyele Local Government Areas of Oyo State.

Table 23: Decile Range of Frequency Distribution of Technical Efficiencies of the

Female Cassava Farmers in Oluyole and Akinyele Local Government Areas of Oyo

State.

Table 24: Summary of Cost Savings According to Efficiency Indicator by Male Cassava

Farmers in Oluyole and Akinyele Local Government Areas of Oyo State.

Table 25: Summary of Cost Savings According to Efficiency Indicator by Female Cassava

Farmers in Oluyole and Akinyele Local Government Areas of Oyo State.

Table 26: Test of Hypotheses on Technical Efficiency

Table 27: T-Ratio Test for the Significance of Coefficients of the Socio-Economic

Variables of the Inefficiency Models of the Male and Female Cassava Farmers.

Table 28: Test of Significant Differences of Mean Technical Efficiencies between the

Male and Female Cassava Farmers in the study area

ABSTRACT

This research work broadly examined gender and the technical efficiency of cassava

production in Oluyole and Akinyele Local Government Areas of Oyo State, Nigeria. The study

employed the use of cross-sectional data from farm survey conducted on a sample of 245

farmers (124 male and 121 female cassava farmers) from eight villages in the study areas. The

data were collected with the aid of structured questionnaire and were later analyzed. The study

employed the following analytical tools in order to analyze the data collected from the field:

Descriptive Statistics; Budgeting technique; Econometric analytical models.

The mean ages of the male and female cassava farmers in the study area were 50 years

respectively. The average farm sizes for the male and female cassava farmers were 3.69 and

3.4 hectares respectively. The mean production costs for the male and female cassava farmers

were N 108,000 and N109, 250 respectively in the study area. The mean output of cassava

tuber production of male and female farmers were 9.35t/ha and 9.33t/ha of farmland

respectively. The average revenue was about N137,700 among the male and female cassava

farmers respectively in the study area. This study revealed that about 68 % of the male and 78

% female cassava farmers were married. Most of the male (65%) and female (55%) cassava

farmers in the study area were fully engaged in their cassava production enterprises

respectively. Many of the male (34.4%) and female (14.88%) cassava farmers gained access to

their land by inheritance. Many of the male and female cassava farmer used chemical inputs in

the study area. Average gross margin per hectare for farmers in the male and female cassava

farmers was about N 29,700 and N28,250 respectively. Among the male cassava farmers, the

significant variables included pesticide quantity used and hired labour employed. Among the

female cassava farmers, the significant variables were fertilizer quantity (at 1%), herbicide

quantity and pesticide quantity.

The estimated sigma square ( ) for the male and female cassava farmers were 0.1819

and 0.4211 while for the pooled it was 0.2613. The estimated gamma () parameter of male,

female and pooled cassava farms revealed that 99%, 42% and 97% of the variations in the

cassava output among the male, female and pooled cassava farmers in the study area are due to

the differences in their technical efficiencies. The study also revealed the existence of

inefficiency effects among the male and female cassava farmers in the study area as the

farmers whether male or female were not fully technically efficient. The RTS for the male and

female cassava farmers were 1.03 and -1.15 in the study areas respectively.

CHAPTER ONE

1.0 Introduction

Nigeria is predominantly an agrarian country with over 70.0% of its population engaged

in farming (CBN, 1996). Agriculture provides the bulk of employment, income and food for

the populace. Also, it provides raw materials for the agro-allied as well as market for

industrial goods. Nigeria has substantial economic potential in its agricultural sector.

However, despite the importance of agriculture in terms of employment creation, its potential

for contributing to economic growth is far from being fully exploited. The sector’s

importance has fluctuated with the rise and fall in oil revenue. Over the past 10 years, the

Nigerian agricultural sector has remained stagnant while the contribution of the

manufacturing sector to the GDP has declined over the same period. Inappropriate

macroeconomic and sector policies perpetuated by the 15 years of military rule and

mismanagement have had a negative impact not only on agriculture, but also on the entire

economy. Consequently, per capita incomes have declined from approximately US$1200 in

the 1980s to about US$300 in 1999 (World Bank, 2000). In addition, Nigeria’s social

indicators have fallen well below the average for all developing countries. For instance, 70%

of the population is below the US$1/day poverty line (World Bank, 2000). Life expectancy is

only 53 years and infant mortality rate is as high as 74 per 1000 live births, with adult

literacy also low at only 43% (ADB, 1999).

Data from the Federal Office of Statistics (FOS, 1999) indicated that poverty levels in

the country have been on the increase since 1986. Detailed analysis of the poverty situation

in Nigeria revealed that most of the poor people work in the agricultural sector and most of

them reside in the rural areas. Studies in Nigeria (D’Situ and Bysmouth, 1994) and elsewhere

(World Bank, 2000) have traced an evident linkage between poverty and agricultural sector

performance. Therefore, improvements in performance of the agricultural sector can have

far-reaching and beneficial implications for food security, income generation, as well as

poverty.

Nigeria has continued to be the largest producer of cassava since the beginning of the

1990s with an estimated contribution of 40 million metric tonnes per annum with an average

yield of 10.2 tonnes per hectare. In recent years, the demand for Nigeria cassava has

increased appreciably due to increased awareness on cassava utilization. The presidential

initiative move by the Federal Government of Nigeria in 2002 was geared towards raising the

production level of cassava to 150 million metric tonnes by the end of year 2010 and realized

an income of US $5.0 billion per annum from the export of 37.6 million tonnes of dry

cassava products. (Nigerian National Report, 2006).

Cassava is Africa's second most important food staple, after maize in terms of calories

consumed. In the early 1960s, Africa accounted for 42% of world cassava production. Thirty

years later, in the early 1990s, Africa produced half of world cassava output; primarily

because Nigeria and Ghana increased their production four fold. In the process, Nigeria replaced

Brazil as the world's leading cassava producer (Nweke, 2004).

In Nigeria, traditionally, cassava is produced on small-scale family farms. As noted by

Nweke (2004) the roots are processed and prepared as a subsistence crop for home

consumption and for sale in village markets and transported to urban centers. In Congo,

Madagascar, Sierra Leone, Tanzania and Zambia, Cassava leaves are consumed as vegetable

(Jones, 1959; Fresco, 1986; Dostie et al, 1999; Haggblade and Zulu, 2003). In Nigeria, cassava is

primarily a food crop. In the year 2000, 90 % of total production in Nigeria was used as food and

the balance as livestock feed (Nweke, 2004).

The pivotal role of the efficiency in accelerating agricultural productivity and output has

been applauded and investigated by numerous researchers and policy-makers within Africa

and outside alike. It is no surprise, therefore, that considerable efforts have been devoted to

the analysis of farm level efficiency in developing countries. An underlying premise behind

much of the work on efficiency is that if farmers are not making efficient use of existing

technology, then efforts designed to improve efficiency would be more cost-effective than

introducing new technologies as a means of increasing agricultural output (Belbase and

Grabowski, 1985; Shapiro, 1983; Bravo-Ureta and Evenson, 1994).

Effective economic development strategy depends critically on promoting productivity

and output growth in the agricultural sector, particularly among small-scale producers.

Moreover, if farmers were not making efficient use of existing technology, then efforts

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

technologies as a means of increasing agricultural output (Belbase and Grabowski, 1985).

Adekanye (1985) declared that over the past ten years, women’s contributions to family

income have been well documented and that official agencies are beginning to recognize

women as producers of goods, not just consumers or reservists. Also there is a growing

realization that in many cases, development programmes have not only failed to benefit

women, but also have hurt them.

The U.N. decade for women (1975-1985) showed that legitimized women’s status has

contributed immensely to the awareness of women’s major contribution to their societies.

Studies by women researchers, which revealed the true circumstances of rural women’s lives,

have made some impacts on development policies of government and donor agencies and a

major impact on women’s programmes in most third world countries. As a result, how best to

integrate women into the development process has been consistently and systematically

questioned by both researchers and practitioners from the beginning of this century (Aishatu,

2002).

In Nigeria, it seems myths about rural women’s roles and contribution still persist, while

cultural constraints persist. In many Nigerian communities, economic roles of rural women

continue to be invisible or at best, viewed as an extension of their domestic roles until very

recently. Few definite efforts were made to evolve policies that will increase rural women’s

access to the education, training, credit or land resources, necessary for incorporating them

into the real mainstream of rural development (Aishatu, 2002).

Adeyeye (1986) stated that the female members of rural households belong to different

socio-economic strata and perform different roles. Whatever the differences, their roles are

vital to the sustenance of their families, communities and society at large. In many areas, they

have the roles of working in the fields and farms to produce food and or tend animals, market

farm produce in addition to bearing and rearing children and man large households with very

scanty or no amenities including such basic necessity as potable water and fuel.

In the last two decade a lot of attention has been drawn to the important role of rural

women in agricultural production in developing countries. However, prior to the realization

that those rural women constitute “economically active population”; they were largely not

considered productive because they usually work as unpaid family labour (Olawoye, 1994).

Many authors as indicated by Adeyeye (1986) have investigated the extent of rural

women’s contribution in terms of labour to agricultural production. Rural women are

involved in many food production activities. Some are farmers in their own right, producing

food crops for family consumption and sale; some work on their husband’s farms carrying

out varieties of operation while some women are traders of food crops, selling processed and

unprocessed forms of agricultural products.

1.1 Historical Background and Importance of Cassava in Nigeria

Cassava is one of the most important crops in Nigeria. It is widely cultivated in the

southern part of Nigeria compared with any other crops, in terms of area devoted to it and the

number of farmers growing it. In all places, cassava has become very popular as a food and

cash crop and it is fast replacing yam and other traditional staples of the area (FACU, 1993).

Historically, cassava (Manihot esculenta) Crantz was introduced into Central Africa

from South America in the 16th century by the early Portuguese explorers. It was probably the

emancipated slaves, who introduced the cassava crop into Southern Nigeria as they returned

to the country from South America via the Island of Sao Tome and Fernando Po (Ekandem,

1962).

Cassava (Manihot esculenta) Crantz is a perennial woody shrub of the family

Euphorbiacea, possessing tall, thin and straight stems with an edible root. It grows in the

tropical and subtropical areas of the world. Cassava can grow on marginal lands, where some

other crops like cereals do not grow. It is also tolerant to drought and can grow in low-

nutrient soils. Cassava provides a basic daily source of dietary energy. Almost all the cassava

produced is used for human and animal consumption and less than 5% is used in the

industries. As a food crop, cassava fits well into the farming systems of the smallholder

farmers in Nigeria, because it is available all food year round, thus providing household food

security. Cassava is important not just as a food crop, but even more as a major source of

cash income for the largest number of households, in comparison with other staples,

contributing positively to poverty alleviation (FACU, 1993).

Cassava food products are the most important staples of rural and urban households in

southern Nigeria. Current estimate shows that the dietary calorie equivalent of per capita

consumption of cassava in the country amounts to about 235kcal. This is derived from the

consumption of garri (roasted granules), chips/flour, fermented paste and/or fresh roots, the

principal cassava food forms (Cock, 1985).

1.2 Statement of the Problem

In Nigeria, food production has not increased at the rate that can meet the increasing

population. Food production increases at the rate of 2.5%, while the demand for food

increases at a rate more than 3.5% due to the high rate of population growth of 2.83% (FOS,

1996). The apparent disparity between the rate of food production and its demand has led to a

food demand-supply gap and there’s an increasing resort to food importation.

Many of the population of African countries including Nigeria, before independence

lived in the rural areas. This indicated that more than 70% of the rural population depended

more on smallholder agriculture for food and income. The labour force during those times is

of household consisting of men, women and children. As a result of this rural smallholder

agriculture remained the major power for rural growth and livelihood improvement. The

rural population provides about 90.0% of the food produced in Nigeria while the remaining

10.0% is assumed to be obtained through importation which means Nigeria is yet to be self-

sufficient in food production. Nearly all the farm tasks connected with food production or the

so-called agro-industry are performed by women, with the exception of tree cutting and other

heavy land preparation which men utmostly perform. Women carry out such farm activities

as planting, transplanting, storing, preserving, and marketing of produce and engage in

almost all food processing enterprises like palm oil and palm kernel processing, cassava and

yam processing (Okuneye, 1988).

Policy prescriptions that seek to fuel agricultural growth are often hindered by the fact

that women farmers do not have the access to the essential resources that are required for the

implementation and success of the policy. Several studies report that in many countries it is

more difficult for females to have access to human capital, land, and financial or other assets

that allow them to be entrepreneurs (Blackden and Bhanu, 1999). If disparities between men

and women’s access to resources, control of assets and decision-making powers persist, these

will undermine sustainable and equitable development (World Bank, 1995). An effective

economic development strategy depends critically on promoting productivity and output

growth in the agricultural sector, particularly among small-scale producers. Moreover, if

farmers were not making efficient use of existing technology, then efforts designed to

improve efficiency would be more cost-effective than introducing new technologies as a

means of increasing agricultural output (Belbase and Grabowski, 1985).

Bailey et al., (1987) noted that management ability, inventories, asset portfolio and

outside resources may all contribute to a farmer's ability to succeed financially, grow, or be

efficient, and would also aid policy makers in creating improved efficiency- enhancing

policies and in judging the efficiency of past efforts. Even though an extensive literature tries

and assesses the equity implications of gender inequality not much has been said about the

efficiency costs of this inequality particularly in Nigeria (Esteve-Volart, 2004). The empirical

measures of efficiency are necessary in order to determine the magnitude of the gains that

could be obtained by improving performance in agricultural production with a given

technology (Bravo-Ureta and Pinheiro, 1997).

Africa has a female farming per excellence. Women play pivotal roles in African

agriculture. They act as producers, processors, storers and marketers. Despite these activities,

women continue to have systematically poorer command over a range of productive

resources, including education, land, information, and financial resources (Staudt, 1982).

Recently in Nigeria, it is hard not to be encouraged by new and rapid changes in the

production and marketing environment of agricultural commodities, particularly cassava, due

to market liberalization, new technologies and government incentive policies on the

promotion of cassava products for export.

Based on the statement of the problem above, this research work was carried out, to

investigate and provide answers to the following questions:

- How do the cassava farmers organize and source for their inputs?

- Are the farmers using the right combination of inputs?

- How profitable is the cassava production to the male and female cassava farmers in the

study area?

- How technically efficient are the cassava farmers in the study area with respect to the

available resources of the farm?

- What are the major constraints to efficient cassava production enterprise in the study area?

1.3 Objective of the study

The general objective of this study is to examine the effect of gender on the technical

efficiency of cassava farmers in Oluyole and Akinyele local government areas of Oyo state.

Based on the general objective, the specific objectives are to:

(i) describe the socio-economic characteristics of the male and female cassava farmers in

the study area,

(ii) estimate the costs and returns to cassava production in the study area,

(iii) analyze the technical efficiency of the male and female cassava farmers in the study

area, and

(iv) identify the major constraints to cassava production in the study area.

1.4 Hypotheses of the study

(i) HO: δ = 0; Socio-economic characteristics of the male and female cassava farmers have no

significant relationship on their technical efficiencies.

(ii) Cassava production activities are more profitable in the men’s cassava farms than in the

women’s cassava farms in the study areas.

(iii) The male cassava farmers are more technically efficient than the female cassava farmers

in the study area.

(iv) HO: ; that is, there are no technical inefficiency effects in male and female cassava

crop production in the study area.

1.5 The Significance of the Study

Cassava, Manihot esculenta Crantz (Euphorbiacea) a perennial shrub, is often

characterized as a women’s crop (Adekanye, 1983), because they are often the principal

grower of cassava. According to Adeyemo (1991), women do 70 to 80 per cent of the

planting, weeding, and harvesting and 100 per cent of the processing of cassava, a root crop

critical in times of food scarcity. Famine is rare in areas where cassava is widely grown,

since it provides a stable base to the food production system and has the potential for

bridging the food gap. Cassava is usually the cheapest source of food energy available

especially, the processed forms.

Cassava production plays a very important role in household food security in Nigeria, and

it also serves as a reliable source of income to farmers in rural areas. This is because it can

grow on marginal land and it is consumable in various forms, thereby making it available in

large quantity. There is need for more production of cassava in the rural areas because of its

importance in alleviating rural poverty. This will help the ruralities to have a better, more

reliable source of living. Introduction of improved varieties of cassava and the use of

integrated pest management scheme are needed.

Recently, efforts are going on by the food technologists toward processing cassava into

different forms to reduce bulkiness, perishability and also to increase palatability of the

cassava crop. With a special look at the population rate in Nigeria, it is a matter of necessity

to improve food production quantitatively and qualitatively. Cassava production enterprise in

Nigeria is an enterprise that sustains both man and animals nutritionally and industrially, in

term of raw materials for production, profit maximization and reinforcement of the Nation’s

Gross Domestic Product (GDP).

A large number of past and recent empirical studies focused on estimating the economies

of scale of various agricultural enterprises, investigating the degree of responsiveness of the

farm operators/managers to product and input price changes as well as the measurement of

relative efficiency of usage of resources of the farms.

An in-depth analysis of technical efficiency on farms operating different enterprises will

help in identifying the feasibility of an increased output with/without increasing the resource

inputs base. It is evident that the study of technical efficiency in cassava production at the

farmers’ level will (i) in empirical terms reveal the constraints and conditions confronting the

male and female cassava farmers’ productivity as well as efficiency of resource combination

and usage. (ii) serve as the foundation for predicting the consequences of

distortions/fluctuations in the economic conditions of producing various crops and animals

(iii) reflect in the aggregate of outputs of crops and animals available for human consumption

and industrial uses.

There exists an inextricable link between women’s well being and the overall health of a

society. Africa is the only region of the world where per capita food production is falling.

Over the past 25years, Africa’s per capita food production has declined by 23 percent,

because the economic backbones (women) were subjugated, marginalized, and unrecognized

(Joan, 2000).

Furthermore, women are the primary labour force on small-scale farm holdings in

African. The extent and variation of their involvement have not received much research focus

at the micro-level. Yet, the findings of such research can help sharpen policy directions and

implementation bringing different groups of women farmers to the mainstream of

development.

This study will benefit the Nigerian agricultural sector by estimating the extent to which

it can possibly raise its cassava output based on the existing resources and the prevailing

technology, thereby developing its export capacity, as a way of conserving foreign exchange.

Thus, this study was focused on the male and female cassava farmers in the study area as

independent and interdependent stakeholders in the economy of the study area.

CHAPTER TWO

2.0 LITERATURE REVIEW

2.1 Cassava Production and the Nigerian Agricultural Sector

Nigeria has six major Agricultural Ecological Zones (AEZ) that run transversely from

west to east based on the IITA classification system. In a south-north axis, these zones are the

humid forest (mainly in the south), the derived/coastal savanna (part of the south), the

southern Guinea savanna (the entire Middle Belt), and the northern Guinea savanna, the

midaltitude savanna, and the dry Sudan/Sahel savanna, all in the northern parts of the

country. The Guinea savanna AEZ is noted for the following major crops: cotton, groundnut,

maize, millet, sorghum, soybean, yam, cassava and vegetables (tomato, carrot, lettuce, onion,

and pepper). The humid forest and the derived/coastal AEZ is noted for producing tree-crops

(cocoa, oil palm, rubber, and timber), and food crops (cassava, yam, maize, pineapple,

banana, plantain, papaya, mango, orange, yam beans, and vegetables (fluted pumpkin, okra,

tomato, and pepper. Among the crops grown in the south, cassava is the most widely

cultivated, both as a food and a cash crop (IITA et al., 2003).

Nigeria is the largest producer of cassava in the world. Its production is currently put at

about 33.8 million tonnes a year (FAO, 2002). Total area harvested of the crop in 2001 was

3.1 million ha with an average yield of about 11 t/ha. Cassava plays a vital role in the food

security of the rural economy because of its capacity to yield under marginal soil conditions

and its tolerance to drought. It is the most widely cultivated crop in the country; it is

predominantly grown by smallholder farmers and dependent on seasonal rainfall. Rural and

urban communities use cassava mainly as food in both fresh and processed forms. The meals

most frequently eaten in the rural areas are cassava-based. Data from the Collaborative Study

of Cassava in Africa (COSCA) showed that 80% of Nigerians in the rural areas eat a cassava

meal at least once weekly (Nweke et al., 2002). Per capita consumption of cassava of 88

kg/person/year between 1961 and 1965 increased to 120 kg/person/year between 1994 and

1998 (Nweke et al., 2002).

The contribution of cassava production by geopolitical zones in Nigeria showed that the

southern states account for 64% of the cassava produced in Nigeria, but the crop has also

increased in importance in the Middle Belt (north-central zone) in recent years and is

expanding into the dry savannas bordering the Sahel. It provides the livelihood for over 30

million farmers and countless processors and traders.

Figure 1: Cassava production (%) by zone in Nigeria, 2001

(Based on CAYS data from FMARD, 2002).

South-East 29%

South-South 20%

South-West 24%

Middle belt 20%

North 7%

Nweke et al., (2002) maintained that cassava performs five main roles: famine reserve

crop, rural food staple, cash crop for urban consumption, industrial raw material, and foreign

exchange earner, also that Nigeria is the most advanced of the African countries poised to

diversify the use of cassava as a primary industrial raw material and livestock feed. They

attested that the two factors that provided Nigeria with this comparative advantage in Africa

include: the rapid adoption of improved cassava varieties and the development of small-scale

processing technologies including the cassava grater.

According to FMANR (2000), among the crops widely cultivated in southern Nigeria,

research efforts have made the greatest impact on cassava. Production has increased

substantially in the country over the last 20 years principally owing to an increase in the area

cultivated and improvements in production efficiency through the introduction of high-

yielding, disease-and pest-resistant cultivars. Despite this development, the demand for

cassava is mainly for food; and opportunities for commercial development remain largely

undeveloped, in contrast to the other major regions of cassava cultivation in Asia and South

America. The absence of agro-industrial markets remains the major constraint to further

development of the crop. Cassava production exhibits high levels of variability and cyclical

gluts, due mainly to the inability of markets to absorb supplies. As a result, prices of storage

roots decline sharply and production levels are reduced in succeeding years before picking up

again. Such factors cause price instability over the years, which significantly increase the

income risk to producers. Insufficient processing options for the storage roots, inadequate

marketing channels, and a lack of linkages between producers and the end-users are major

factors preventing greater profitability for producers and processors. There is a potential to

generate from one crop multiple economic benefits through improved post-harvest handling

and processing of fresh storage roots.

In Africa including Nigeria, cassava is primarily produced for food in its various forms.

Nigeria has been recognized as the largest producer of the crop in the world. According to

Food and Agricultural Organization (FAO), estimates of its years 1986-2004, production

levels range from about 34 million tonnes to 37.9 million tonnes. However, the Central Bank

of Nigeria (CBN) had indicated that by the year 2003, Nigeria would produce 41.8 million

metric tonnes of cassava (FAO, 1995).

Nweke (1996) attested that cassava is grown in virtually all the parts of Nigeria, with

rainfall greater than 100mm and accounts for over 70% of the total production of the tuber

crops in West Africa. This achievement has been attributed to the improved high yielding,

pest and disease resistant cassava varieties produced and released to farmers through research

collaboration of International Institute of Tropical Agriculture (IITA), Ibadan and National

Root Crops Research Institute (NRCRI), Umudike. He also emphasized that cassava has

continuously played three vital roles, which are: as cash earners for the growers, low cost

food source for both urban and rural dwellers as well as household food security.

In Nigeria, cassava production spread most rapidly during the 20th century to a large

extent, this was as a result of governmental encouragement, due to crop resistance to locust

attacks, drought and its consequent value as a famine reserve. The replacements coupled with

market demand also contribute to the diffusion of cassava in Nigeria (Cater, 1995).

According to Fresco (1993), cassava’s wide adaptation to a range of climate and edaphic

conditions, gives it comparative advantages over crops in those situation where

extensification of land use takes place where the ratio of land decrease, cassava will be

grown increasingly on expanding land area. The replacement of more demanding crops in

terms of labour and soil fertility such as yam, millet and guinea corn will result. The result of

this scenario will be an expansion of cassava production.

Cassava yields in Africa are low, averaging 6.1 tonnes per hectare under farmers’

traditional farming compared with a potential yield of 30.5-51 tonnes per hectare. The major

factor limiting higher yield is damaged caused by insects and diseases. For instance, Mosaic

Virus that is widely spread and economically important. The yield losses of up to 95% and

80% from Cassava Mosaic Virus are common (Hann, 1998).

Among the important factors resulting in low root yield of cassava in Africa are late

planting, untimely and inadequate weed control and high incidence of diseases and pests the

enormous drudgery involved in land preparation, lack of ready and sure market for the fresh

roots and the transportation and processing problem based on the report of a joint research

(Ezumal et al., 1980).

Hann (1995) showed that cassava has the potential to produce more food calories per unit

area than other crops in its class. Both roots and leaves are valuable as human food and

livestock feed and the roots are widely used for industrial production of starch and alcohol.

According to Olayide (1982), it was estimated that up to 55million out of 90 million tonnes

of global cassava production are consumed by man and it has also been predicted that there is

every possibility that consumption will rise in the nearest future.

According to Ugwu (1995), whole cassava meal and cassava peel have been developed as

carbohydrate base for poultry feed. These can substitute up to 75% for maize depending on

the class of poultry and method of production. According to Hann (1998), cassava flour can

only replace 20% of wheat flours.

The IITA 1980 workshop in its finding indicated that cassava products are not income

sensitive even over a large income range. This means that cassava is not an inferior good as

would be expected. Price analysis carried out in 1999 on cassava tuber revealed that

processed forms such as: Garri, Lafun, Fufu, and Starch among others, improve value-added

and increase income accruing to the farmers and processors and improve quality of life of

consumers who are provided with better quantity food at varying prices they can afford

(Hann, 1995).

2.1.1 Contribution of Cassava to Household Food Security

Cock (1985) declared that cassava products are the most important staples of rural and

urban households in Southern Nigeria. Current estimates showed that the dietary calorie

equivalent of per capita consumption in the country amounts to about 238kcal. This is

derived from the consumption of garri (roasted granules), chips/flour, fermented pastes and /

or fresh roots, the principal cassava food forms.

Odurukwe et al., (1997) attested that, in the south, cassava is followed by yam as the

staple food. Yam consumption in most of the south is seasonal, being highest in the month of

November to January, the period of harvest. Thereafter, cassava products and other

supplementary foods take over. In all locations, cassava has become a very popular crop and

is fast replacing yam and other traditional staples of the area, gaining ground increasingly as

an insurance crop against hunger. Cassava is also a major cash crop.

A large proportion of cassava, probably large than those from most other staples, is

planted purposively for sale. In comparison with other staples, cassava generates income for

the largest number of households. The planting of high yielding varieties has resulted in

higher cash income. Considerable income is generated from cassava; it provides them with

an income –earning opportunity, enabling them to purchase commodities, which can

contribute to household food security (Odurukwe et al, 1997).

2.2 Marketing of Fresh Cassava Tubers

Carter (1995) affirmed that the profitability of any product depends among other factors

on marketing of the products themselves. Apart from after sales maintenance services usually

carried out by firms, marketing can be defined as the process of moving the product from the

producer to the consumer in the proper form and amount and at appropriate time and place.

Nweke et al., (1997) declared that, marketing is a necessity, so that as a nation develops,

it is able to meet the demand and supply of commodities. They attested that the marketing of

agricultural product can be considered as a tool for developing policy as well as instrument

for regulating and executing development process. In all they viewed, marketing as a setting

as well as an expanding within a set of dynamic environment forces.

According to Berry (1993), in the more prosperous rural economy of southwestern

Nigeria, sales ranged from two-third to 90% of women’s cassava output. Fresco (1982) stated

that even very poor farmers often sell a significant proportion of their crop. Women farmers

in Southern Zaire sold 20-40% of their cassava.

2.3 Gender and Cassava Production in Nigeria

Cassava provides different opportunities for both men and women farmers and

processors. A study by Nweke et al., (2002) identified five important gender relevant issues

related to cassava. For instance, first, men and women make significant contributions of their

labor to the cassava industry, with each specializing in different tasks; men work

predominantly on land clearing, plowing, and planting, while women specialize in weeding,

harvesting, transporting, and processing. Secondly, men and women play strategic but

changing roles in the cassava transformation process. Thirdly, as cassava becomes a cash

crop, men increase their labor contribution to each of the production and processing tasks.

The introduction of laborsaving technologies in cassava production and processing has led to

a redefinition of gender roles in the cassava food systems. Finally, women who want to plant

cassava are usually constrained by the lack of access to new cassava production technologies

and other resources. A recent study on gender and cassava commercialization in Nigeria

showed that as cassava is commercialized, households in cassava producing areas invest

more on the education of their children (Kormawa and Asumugha 2003).

While the sexes are equally represented in trading, women, and to a lesser extent

children, dominate in processing. As opportunities for commercialization increase (arising

from favorable market opportunities for cassava and its products), the number of women

involved in processing increases. Growth in cassava production is therefore likely to provide

increased employment opportunities for women. However, there is a tendency, as

mechanized processing equipment (such as graters and mills) are acquired, for men’s

involvement in cassava processing to increase, as they often control and operate these

machines (Spencer and Associates,1997). Women may therefore lose some of the benefits of

increased employment, as they lose control of some of the income. Steps need to be taken to

ensure that this does not happen, e.g., by assisting women to get organized into groups that

can effectively carry out the commercialization of the commodity, increasing the access of

such organized women’s groups to credit for the acquisition of post-harvest machinery, and

training them to operate the equipment properly, and enhance their post-harvest and micro-

enterprise skills. This means that the needs of women should be kept in mind even at the

project design and implementation stages to prevent any possible negative impacts of

increased commercialization in the sector, e.g., the equipment design and dissemination

stages.

2.4 Agronomic and Economic challenges of Cassava Production in Nigeria

Fresco (1993) revealed that the constraints in cassava production include a wide range of

technical, institutional and socio-economic factors. These constraints are: pests and diseases,

agronomic problems, land degradation, shortage of planting materials, food policy changes,

access to markets limited processing options and inefficient extension delivery systems.

According to FMARD (2003), the challenges include:

(i) Lack of a well-organized planting material multiplication and distribution system for

improved cassava varieties: Despite the development of high yielding and pest- and disease-

resistant varieties in Nigeria, many recommended varieties are yet to be released, and many

released varieties are yet to be multiplied on a large scale and made available to farmers.

Shortage of planting materials is also compounded by farmers’ inability to preserve planting

materials. The lack of a well-organized planting material multiplication and distribution

system is one of the major constraints to the adoption of improved cassava varieties. The

system of multiplication and distribution is often inefficient either because strategically

located national seed production schemes do not exist or because cassava is given a lower

priority. The very low multiplication rate, bulkiness, and high perishability of cassava

planting materials make their multiplication and distribution more expensive than

conventional (grain-based) seed services. The private sector has not participated in the

multiplication and supply of cassava for these reasons (FMARD 2003).

(ii) Lack of access to improved cassava planting materials, appropriate crop and soil

management practices: Although improved varieties with a potential yield of more than 40

t/ha have been developed for cultivation in Nigeria (FMARD 2003), the national average on-

farm yields are estimated to be 11 t/ha. The low yields are attributed to poor agronomic

practices, low soil fertility, and poor input delivery mechanisms (FMANR, 2000). Cassava

root yields are poor because of the low usage of modern inputs, (e.g., improved varieties,

fertilizer, and lime), labor shortages that force farmers to plant late, and a lack of improved

crop husbandry practices, (e.g., optimum planting densities, appropriate crop mixtures for

sustained soil fertility management, etc).

(iii) Lack of improved post-harvest processing, storage, and utilization technologies: Freshly

harvested cassava roots are bulky and the shelf life rarely exceeds 2 days after harvesting due

to enzymatic reactions. Cassava also contains varying amounts of cyanogenic glucosides

which break down to hydrocyanic acid, a toxic compound. The bulkiness and high

perishability of harvested roots and the presence of cyanogenic glucosides call for immediate

processing of the storage roots. Simple processing—pounding, grating or chipping —is

essential for detoxifying the tuberous roots, and allowing farmers/processors to convert the

highly perishable cassava roots into dry, stable, and safer products. Processing also adds

value to cassava and extends the shelf life by converting freshly harvested roots into a freely

traded commodity. The present cassava processing methods are highly labor-intensive and

expensive (FMARD, 2003). Among other principal constraints to cassava processing is the

absence of efficient dryers, peeling machines and pelletizers. Drying is a key process for

making virtually all cassava products. This is because the major cassava producing zones are

also the zone with relatively more rain and have a longer period of rain fall. Solar radiation is

relatively low, justifying the need to use dryers extensively for cassava commercialization in

southern Nigeria. Thus, to make cassava competitive, both for the domestic and export

markets, investments in cassava processing machines among others must be a prerequisite.

Improved storage and packaging technologies to extend shelf life will contribute to

increasing cassava root availability and reliability, stabilizing prices, and facilitating export

(FMARD 2003).

(iv) Labour shortage due to migration to urban centers and poor health: According to

FMARD (2003), shortage of labor is a major impediment to agricultural growth and the

problem is mainly attributed to high levels of urbanization in the country. The quality of

labor available is low because it is mainly provided by old people and children. Diseases

which are most prevalent during the rainy season, when demand for labor is high, affect the

quality of labor. Malaria and the HIV/AIDS epidemic exacerbate the existing labor

constraints in agriculture. Post-harvest processing of cassava is laborious and a source of

drudgery for women and children. New laborsaving and quality improving technologies

exist, but they are mostly located in urban and peri-urban areas. More efficient hand tools

and animal drawn/mechanical implements may increase labor productivity in cassava

production while improved post-harvest machines and hammer-mills would reduce the

drudgery in post-harvesting handling.

(v) Inadequate market information: Poor linkages between producers and buyers exist

because of poor access to market information. Sustainable and timely dissemination of

national market information for cassava is essential to enable the producers, processors,

distributors, etc., to take advantage of new or high value market opportunities. Currently,

there is no well-established market information system for cassava in the country. Although

the Rural Sector Enhancement Program (RUSEP) pilot project funded by USAID/Nigeria

and executed by IITA has initiated an agricultural Marketing Information System (MIS), it is

still far from adequate. Hence, an effective market information system is needed to ensure

that operators within the cassava industry have access to relevant information with ease. The

system should capture information on product standardization (chips, flour, starch, etc.),

price and pricing, inventory levels, product range, utilization possibilities, alternative markets

for cassava products, and price profiles, etc. (FMARD, 2003).

(vi) Poor access to inputs and financial services: Farmers are unable to access essential inputs

(fertilizer) and financial services (credit), and are therefore unable to improve the

productivity of their land. Operators (farmers, processors) within the cassava industry

generally lack adequate capital for both upstream and downstream production activities.

Personal savings are low; disposable incomes are grossly inadequate to finance farm

activities, while the majority of the farmers lack access to formal credit links. Most small-

scale farmers do not borrow from commercial banks because of very high interest rates as

well as their own lack of collateral. The private sector provides credit in form of inputs only

for export crops such as cotton and cocoa. Financial agencies should provide short and

medium-term credit to target beneficiaries. Effective and long lasting links are needed

between the financial agencies and farmers and processors through group formation, savings

mobilization, the development of profitable on-farm and off-farm activities, and assistance in

supervising credits (FMARD, 2003).

(vii) Poor access to markets: Marketing can be a problem for poor farmers especially those

living in villages with poor feeder roads that may not have resources to transport their

commodities to the market. Typically, farmers transport cassava as head loads, on bicycles,

or in lorries. With poor market access, marketing cassava can be particularly problematic

because of its bulky nature, especially as unprocessed roots. Poor access also makes the

movement of goods and people difficult; particularly during the rainy season. The roads

linking the major towns are usually quite good. Though the market access road network is

better in Nigeria than in other West African countries, the rural feeder road networks are

poorly developed or absent in some places. This has significant implications for marketing,

cost of inputs, access to health facilities and other social services, and has adverse effects on

production and the rural standard of living. There are also problems of unreliable supply,

uneven quality of products, low producer prices, and costly marketing structure which affect

its use for agricultural transformation. However, cassava has unique characteristics and great

potential as a raw material for different end uses and product markets. The extent to which

the potential market for cassava may be expanded depends largely on the degree to which the

quality of various processed products can be improved to make them attractive to various

markets, local and foreign, without significant increases in processing costs (FMARD, 2003).

(viii) Market opportunities: A potential market for cassava is in the livestock feed industry.

However, only about 5% of amount produced is used as feed, indicating that the industry is

underdeveloped. The current demand for maize in the Nigerian livestock industry is put at

4.3 million tones /year. Cassava is unlikely to completely replace maize as the basic energy

source in livestock feed. Cassava storage roots are cheaper than maize in both rural and urban

markets but the additional processing to chips and pellets is prohibitive due to high

processing costs. Nigeria has no comparative advantage in the export of cassava chips and

pellets because of stiff competition from Thailand (which dominates the export market at the

moment), and the underdeveloped structures for commercialization (Ezedinma et al. 2002;

Nweke et al.2002). The favorable domestic prices for maize grain do not encourage the use

of cassava chips and pellets in livestock feed in the country.

The enterprises in which cassava is likely to make an impact are processing cassava flour

for bread and confectionery, processing sweeteners such as fructose and glucose for foods

and beverages, producing starch and adhesives (dextrin) for the paper, textile, wood and

crude oil production, producing crude ethanol for hospitals, distilleries, and pharmaceutical

industries, and developing multiplication centers for healthy planting materials to satisfy the

demand for improved high yielding varieties (FMARD, 2003). Interest in investments in the

Nigerian ethanol industry is growing but emphasis on small-scale cassava-based production

units using cassava as raw material will provide a rapid alternative market for the

commodity. This will definitely increase employment and income for farmers, processors,

and agro-industries along the value chain, thus diversifying the rural economy (FMARD,

2003).

The use of cassava starch as an industrial raw material in Nigeria is low and the market

structures are also underdeveloped. In the early 1990s, only about 700 t/year of cassava

starch was produced because Nigerian cassava starch is considered to be of low quality by

industries and none is exported (Nweke et al. 2002). Maize starch rather than cassava was

preferred, especially by the textile and confectionery industry. The harsh economic climate

during the military era also led to the near collapse of the textile industry in Nigeria and so

reduced the potential market for cassava starch. The positive steps taken by the present

democratically elected government to revive the textile industry will provide an incentive to

develop the starch industry.

The development of the starch industry in Nigeria would enable the soft drinks industry

to stop the importation of all its syrup concentrate because cassava starch derivatives

(hydrolysates, e.g., glucose, sucrose, fructose, maltose, and syrup) would have been

developed in Nigeria. The current annual use of starch hydrolysates in the pharmaceutical

industry is 1523 tonnes but 80% of the raw materials used by the pharmaceutical industry in

Nigeria which are presently imported will be produced locally (RMRDC, 1997). The 58,000

tonnes of adhesives, a major derivative of starch (dextrin), were imported for use in the

wood, cable, paper and printing, packaging, and footwear industries in Nigeria will now also

be produced locally. Developing the starch industry for use as adhesives for these industries

would put 60,000 tonnes of cassava into use for this industry alone in Nigeria (Nweke et al.

2002).

2.5 Roles of Women in the Nigerian Agricultural Economy

Nigeria is fundamentally an agricultural economy, having an estimated population of

66.0 million people living in rural areas with 30.0% of the GDP coming from Agriculture.

Over two third of the labour force still engage in agriculture practices and pursuits. Nigeria’s

population is rural, with 35.0% being urbanized. Concerning the overall sex ratio, the figure

stands at 102 men per 100 women on the average, but slightly lower for the rural areas

(Jibowo, 1994).

FAO (1996) showed that the Nigerian women perform the following multiple roles:

(1) Child Bearing and Rearing: About 74.0% of the Nigerian women’s reproductive life is

spent in marriage since they marry very early. Women produce and nurture 6.3 children on

the average. The burden of reproduction must have limiting effect on their educational and

economic activities. Obviously, reducing the family size will definitely enhance Nigerian

women’s productivity. For example, Nigeria still had 70.0% of its adult females and 46.0%

of its adult male as illiterate. The involvement of the females (women) in educational

institution is relatively low, with 49.0% at the primary level and 20.0% at the university.

(2) Female-headed Household: About 15.0% of rural households and 18.0% of urban

households in Nigeria are headed by female. It varies from 23.0% in the Southeast to 19.0%

in the Northwest. The increasing effect of the female-head households has created socio-

economic crisis resulting in poverty, greater pressure on women’s time and greater

dependence on the labour of children. The overall negative effect will be on the family and

children’s welfare (Oluwasola, 1998).

(3) Family and Household Maintenance: Agricultural production and processing of farm

produce as well as rural small-scale industrial activities continue to be the major assignment

of rural women. However, care of the children and the household responsibilities absorb

more of a woman’s time, rather than her income-earning activities on a daily basis.

(4) Economic and Income Earning Activities: Greater numbers of the women are involved in

income-earning activities, for their own account and the distinct responsibilities assigned to

them. Women in farming activities are evidence of the existence of gender specific rights and

obligation in Nigeria. Nigerian women represent about 50.0% of the agricultural labour

force, and they produce most of the country’s food. Farmwomen undertake most of farm

operation themselves. Rural women spend between 15-20 work hours, on the average per day

on agricultural and household subsistence work, while men spend 15 hours (Adeyemo,

1991).

The specific constraints facing women farmers include:

(a) Limited access to farmland

(b) Difficulty in obtaining credit from institutional sources with aggravates their limited

ability to earn and control income on their own.

(c) Limited ability of women to own capital assets (Adeyeye, 1986).

Odurukwe et al., (1997) stated that in most part of rural Nigeria, division of labour within

the household is gender-specific and are according to age(s) of the household members.

Women play a prominent role in agricultural production. The extent of their involvement in

agricultural production, and their contribution to the household food basket vary from one

ethnic group to another. They also affirmed that women play an important role in cassava

production, processing and marketing. Until recently, the role of women was underestimated.

This misconception together with cultural prejudice limits the access of women to extension

services and other resources.

With the growing recognition of the role of women in agricultural production, a number

of programmes have been initiated recently: Women-In-Agriculture (WIA), Better Life

Programme (BLP), Family Economic Advancement Programme (FEAP), and Family

Support Programme (FSP). These serve as mechanism for giving women better, cheaper and

reliable access to land, credit, agricultural input, extension information and other resources.

The WIA units also attempt to secure and to make available to women groups improved

cassava- processing facilities (machines) to increase processing efficiency (Odurukwe et al,

1997).

Overall, women play a central role in cassava production, contributing about 58% of the

total agricultural labour in the South west and 67% in the Southeast and 58% in the Central

zones, with women involving in virtually all activities: hoeing, weeding, harvesting,

transporting, storing, processing, marketing and domestic chores (FACU, 1993).

Odurukwe et al., (1997) established that the women are almost entirely responsible for

the processing of agricultural commodities. They play a dominant role in marketing of

cassava produce and assist their husband in marketing cassava and other crops as well as

their own crops. In many cases, women buy the agricultural produce from their husbands and

other farmers, and market them at a profit. At times, they buy cassava in the soil, harvest,

process and market. Odurukwe et al., (1997) also attested that small-scale cassava processing

is the domain of women, although most of the mechanized equipment (grater and grinder) are

owned and operated by men. It was necessary to ensure that the shift from mechanical

processing does not put the women in a disadvantaged position in terms of employment and

income earning opportunities. They therefore recommended that gender issues should be

considered when designing mechanized processing facilities for women who play a major

role in cassava production and processing.

2.5.1 Gender and Productivity Differential among Cassava Farmers.

The term ‘Gender’ does not refer just to differences of sex, which are biological. Gender

refers to the social meanings constructed around sex differences and is an important stratifier

alongside class, caste, race and ethnicity. Gender refers to the differentiation between the

roles of men and women as constructed by society. While primarily, women are assigned the

responsibility of domestic and reproductive activities, they also engage in market oriented

activities in the agricultural sector (Olawoye, 1994).

Gender ascribes the roles, responsibilities and opportunities of men and women. Gender

roles are changeable and vary across culture and time being chiefly transmitted through the

socialization process. Gender is a “social relationship between women and men based on

perceived sex differences, an ideology regarding their roles, rights, and values as workers,

owners, citizen and parents” (NCEMA, 1990; Olawoye, 1996).

Gender being a socially constructed concept; vary from one context to the other, so what

men and women do in a particular culture may be different from what they do in another

culture. Gender relations refer to the social norms and practices that regulate the relationships

between men and women in a given society at a given time. Gender relation changes over

time and vary across different societies. One pervasive trait of gender relations across

different cultures is the power asymmetries between men and women (NCEMA, 1990).

In all societies, gender relations play a systematic role in the division of labour,

distribution of work, income, wealth, education, productive inputs, and so on. In most

societies, women are likely to work longer hours than men, have lower earnings, education,

wealth and access to credit, experience poverty differently (NCEMA, 1990). In Nigeria, it is

often the case that different members of this household simultaneously cultivate the same

crop on different plots. Pareto efficiency in production implies that yields should be the same

on all plots planted to the same crop within a household in a given year.

Traditional household models assume that a farm household function as single unit for

productivity and consumption that a consensus exists among household members on the

allocation of resources and benefits and that all household member’s interest and problems

are identified (Cloud, 1987).

Recent studies have suggested the use of family as a heterogeneous unit in a dynamic

context to study the intergenerational aspects of gender analysis.  The evidence shows that

the effects of income accruing to a household or more particularly a male headed household

are significantly different from the effects of income accruing to women as men and women

have different distributional priorities. Analyzing household data sets for four countries,

Quisumbing and Maluccio (2000) find that greater control of assets resources by women had

a beneficial impact on budget shares for education. Such distributional choices are made

possible by increased asset ownership by women. In the absence of an effort to raise

educational standards, the prospects for adoption of new technology in the next generation

would be less than satisfactory. Higher income and asset levels for women have also been

associated with better nutritional levels for children.

Ajao et al., (2004) affirmed that, on the average, the value of cassava output is smaller

for female farmers than for male farmers. In general, the women used smaller bundles of

physical inputs than their men counterparts. They also used less chemical fertilizers,

insecticide and are not likely to use tractor service. The labour is marginally higher on female

managed cassava farms than in male managed ones. They also concluded that the women

underutilized labour input devoted to cassava production relative to their male counterparts,

while the male farmers are more efficient in their use of fertilizer. Women’s low yield of

cassava output was due to fact that men select land first, and so would have selected the most

productive part, leaving women with land that have either been over-used or land that are

prone to erosion; so invariably the women are the victim of less productive land, while men

always use the most productive portion of the land.

The women farmer’s productivity is hindered by inferior educational status, inferior

access to resources like land and credit. Most research centers are crusading for the

improvement of women farmers’ productivity in Africa, most especially IITA (International

Institute of Tropical Agriculture) with two fundamental objectives, which are:

(a) to increase food production

(b) to promote social equity ( Omoregbe,1995; Fresco,1993)

Women in years past tried to cope with their multiple responsibilities, which vary in

degree with culture, income level, literacy, age and marital status, but have been confronting

a range of obstacles which affect them in all or some of their roles. The constraints affecting

women farmers in Africa and the global world have been broadly grouped into two:

(1) Constraints, which are of primary importance to the human capital development of

women, and

(2) Constraints, which are of importance to the economic productivity of women (Karl,

1983).

The concept of Gender productivity when embraced and appreciated will bring about

sustainable rural communities in the world especially Africa. A sustainable rural community

has been as one, which is economically viable, socially active and environmentally adequate

(Dykeman, 1998). Other characteristics also included are a sense of belonging to the

community, and the existence of interactions between the members (male and female) of the

community (Ramsey, 1995). Gender productivity is the bedrock of all sustainable rural

communities that ever existed.

According to Kline (1994): “The sustainability of a community can be defined as the

ability of a community to utilize its resources, in such a way as to ensure that all the

members (male and female) of that community, both present and future may attain a high

level of health and economic security, a place in the configuration of the future, whilst at the

same time maintaining the integrity of the ecological systems, on which depend both

production and life itself”.

The gender yield differential apparently is caused by the difference in the intensity with

which measured inputs of labour, manure, fertilizer are applied on plots controlled by men

and women, rather than by differences in the efficiency with which these inputs are used. In

production function estimates for all crops (cereals and vegetable crop in whish women

specialized); it was found that except in the case of sorghum (among cereals), the coefficient

of the gender variable was not significant (Fresco, 1993).

One of the major reasons, for the neglect of women in cassava development project in

West Africa is the pervasive assumption that the female farmers are less efficient than the

male farmers. Thus even in regions of West Africa, where women are the traditional maize

growers together with some crops ( vegetable, cassava ) which are considered as women’s

crops, development projects choose to focus on men and not on women (Ekandem, 1962).

2.5.2 Women’s Access to Agricultural Production Resources in Nigeria

Women play a dominant role in agricultural production in developing economies

including Nigeria. They are involved in practically all aspects of agricultural production.

Chiebowska, (1990) reported that women living in rural areas represented 60% of the world’s

female population with as much as 70% of them in the developing countries. In Nigeria,

women constitute 49.7% of the national population and majority of them reside in the rural

areas, where they live mainly by exploiting the resources of nature (CBN, 1994; NPC, 1998).

They are involved in agriculture as suppliers of labour, food crops and livestock producers,

processors of food and fish products, marketers of peasant farm surplus and transporters of

farm supplies and farm products between the farm and the home.

According to the World Bank (1989), women in Sub-Saharan Africa, Nigeria inclusive,

are responsible for the production of about 70% of the total staple food supply in the region.

This contribution is higher than that of the women in other regions of the world. The

National Center for Economic Management and Administration (NCEMA, 1990) quoting the

Food and Agricultural Organization, stated that women’s contribution was 50-60% in Asia,

46% in the Caribbean, 31% in North Africa, and the Middle East and slightly more than 30%

in Latin America.

2.5.3 Constraints to the Activities of Rural Women in Agricultural/Rural

Development in Nigeria

Women engage in both domestic chores and farm tasks. The domestic chores include

bearing and rearing children, water and firewood fetching and food preparation, while their

farm tasks are land clearing, land tilling, planting, weeding, fertilizer or manure application,

harvesting, food processing, threshing, winnowing, milling, transportation and marketing as

well as rearing of livestock such as chicken, goats, pigs, ducks and sheep (Adeyeye, 1986).

It is often the lack of crucial productive resources such as land, labour and capital, which

render the image of the women farmers, as being marginal and inefficient producers. Some of

the constraints faced by women in the discharged of their roles in agriculture are as follows:

(1) Women’s Access to Land: Land is the most essential resource in agriculture. The

ownership, use and the control of land determine to a large extent the benefit from

agricultural production. It has been posted and demonstrated, that women do not readily have

access to land (Adekanye, 1985; Famoriyo, 1985; Adeyemo, 1991; Fabiyi and Adegboye,

1978; Mortimore and Fabiyi, 2003). These constraint, adversely affect the productivity of

women and hence their well being.

The Nigeria land problem as reported by Mortimore and Fabiyi (2003) presents a

complex mosaic derived from past history, colonial legacy, current economic pressures and

opportunities, as well as from its natural, ecological and political characteristics. With the

existing over 250 ethnic groups and abundant land and other natural resources, conflicts are

often generated in the exploitation of the competitive growth of the country’s economy.

According to Fabiyi and Adegboye (1978), land is the Nigerian context takes on

fundamental significance as a commodity in daily lives of Nigerians as expressed in social,

economic, and political organization of various communities in Nigeria. They affirmed that

in most parts of Oyo State, individuals derive the rights of ownership and use from the group

to which they were born or adopted. The group exercises the right of ownership and

individuals exercise the right of use. “The acquisition of usufructuary rights by cultivating

persons follows the principles of property enunciated by John Locke: a person makes

property in land his own by mixing his labour with the soil and appropriates it from a state of

nature (Parson, 1977). Fabiyi and Adegboye (1978) also attested that the rights of the

individual to use the land are heritable as long as he does not neglect his holdings or permit

its usufruct to lapse through inactivity.

The central issue in the analysis and discussion of any land tenure systems is the

relationship of man in the occupancy and use of land. This relationship Bohannan (1963) has

called the ‘man-thin-unit’. However, Max Gluckman (1945) has pointed out that the word

‘right’ comes into the discussion of social relationships so that we also have a man-man unit.

Thus, land tenure defines the relationship among men in the use and control of land

resources.

Rights to land and natural resources underpin all investment by poor in subsistence-

oriented farming, smallholder cash crops, animal husbandry and the use of forest products. In

addition, the development of rural markets, village - based industries and service activities

are central to better livelihoods for poor people. The need for farming families to feed

themselves, produce quality goods for sale, and compete on global market, while sustaining

the productive capacity of the land, necessitates a purposive review of existing land law in

Nigeria (Mortimore and Fabiyi, 2003).

According to Mortimore and Fabiyi (2003), the economic function of land cannot be

separated from spiritual, social, cultural and political patronage. They affirmed that women

usually inherit little or no land, and their rights of usufruct usually derive from their

husbands, although they are not precluded from owning land by most national laws or by

Sharia law which, where applicable, allows them to inherit their rights are not defined or

protected in statutory laws, and may be threatened where market production or irrigation

(controlled by men) appropriately their land or men obtain title, convert CPRs ( Crop

Production Systems) ( where fuel, e.t.c. is gathered) to cultivation.

The three principal components of land tenure are ownership, transfer and use. It is

ownership which creates access to use, occupation, lease and redemption of a piece of land

(Adegboye, 1976). In feudalistic societies, ownership of land carries with its control of

government namely the right to tax, the right to judge, and the power of enforce police

regulation (Penn, 1963). According to Karl (1983), the tenure systems deny the women land

ownership titles and rights. This constitutes a complex network of problems, which affect

women farmers directly. In countries like Nigeria, where rights to occupy land are

determined by tribal chiefs and village authorities who decide on land use by members,

especially in Northern Nigeria where Islamic culture predominates, women are entitled to

inherit half the parcel of land given to men (Oluwasola, 1998). This denies women the use of

land as collateral, to obtain loans from credit agencies. In general, women’s right to land

have been marginalized.

(2) Women’s Access to Credit: Credit is a source of capital that is needed to acquire and

develop farm enterprise. Lack of credit is seen as a key factor limiting the ability of women,

to expand their operation, raise productivity, hire more labour and improve their own income.

According to Berger (1985), the most obvious factor limiting women’s access to credit

appears to be outright discrimination. For instance, sexual stereotype of women’s roles may

be so pervasive that money lender see women, only as dependent home makers, not as heads

of households, micro –entrepreneur or responsible subject of credit.

According to Oluwasola (1998), for a woman to obtain a loan at all from the Nigeria

Agricultural and Cooperative Bank (NACB), a bank set up to promote agriculture, her

husband will have to guarantee her. Without adequate cash income and access to invertible

credit, women are unable to take advantage of productive resources like irrigation water,

fertilizer, herbicides and machinery.

(3) Women’s Access to Agricultural Technology: The modernization of agricultural sector

entails the use of improved technologies; improved seeds, herbicides, insecticides, and the

use of these improved technologies are capital intensive. Stewart (1977) cited that lack of

access to capital, as a major constraint on African women’s use of agricultural technology.

Due to this constraint, the rural women rely mainly on the use of primitive technologies,

access to and control of resources. The progresses of change can come from a number of

factors including technology, which as observed by Stewart, is not value-free or value

neutral, but is seen to be embedded in and to carry social values, institutional forms and

culture. The modernization of agriculture, through the application of technology tends to be

masculine.

2.6 Concept of Efficiency

Production efficiency means the attainment of production goals without waste. The

fundamental idea underlying all efficiency measure is that of the quantity of goods and

services per unit input. Ajibefun and Daramola (1999) defined efficiency in agriculture in

association with the possibility of farm’s production to attain optimum level of output from a

given bundle of input at least cost. Farrell (1957) has derived the three components of

efficiency recognizable in the economic literature. They include: (i) Technical efficiency,

(ii) Allocative efficiency, and (iii) Economic efficiency.

(i) Technical Efficiency

Yao and Liu (1998) defined technical efficiency as the ability to produce maximum

output from a given set of inputs, given the available technology. Technical efficiency

according to (Nwaru, 2003) refers to the ability of a given set of entrepreneurs to employ the

best practice in any industry so that not more than the necessary account of a given set of

resources is used in producing the best level of output. According to Farrell (1957), technical

efficiency evaluates a farmer’s ability to obtain maximum possible output from a given set of

inputs, given the available technology.

Technical efficiency relates to the degree to which a farmer produces a given bundle of

inputs (an output oriented measure), or uses the minimum feasible level of inputs to produce

a given level of output (an input oriented measure). The level of technical efficiency of a

particular farmer is characterized by the relationship between the observed productions

(Greene, 1993).

Technical efficiency is measured by comparing the observed input coefficient points for a

firm with the efficiency frontier input coefficients for the same factor proportions. This

involves using the data from a single time period so that all variation in output, which cannot

be attributable to differential, input use, become part of the efficiency index. However, OLS

residual from estimates of production seem to be the simplest method of obtaining measures

of technical efficiency for farms (Mijindadi and Norman, 1982).

Mijindadi and Norman (1982) stated that the differences in the technical efficiency of the

various crop and animal enterprises might be due to any of the four factors which include:

(i) Differentials in the management capabilities of the various farm operators.

(ii) The employment of different levels of technology based on the type, nature and

quality of the inputs used.

(iii) Differentials in the environmental factors- like the edaphic factors (soil texture,

structure and nutrients’ quality), climatic factors (rainfall, solar radiation, and

evaporation)

(iv) Differentials in the existence of the non-economic and non-technical factors such as

family structure and farmers’ motivation to working hard enough on their

plots thereby achieving the highest level of farm output.

Following Farrell, (1957) the appropriate measure of technical efficiency is input-saving

which gives the maximum rate at which use of all the inputs can be reduced without reducing

output. It defines the total variation of output from the frontier, which can be attributed to

technical efficiency. A stochastic production frontier was estimated, and measures of

efficiency were calculated. When the ratio of the standard error of U to that of V, λ, exceeds

one in value it implies that the one sided error term U dominates the symmetry error V,

indicating a good fit and correctness of the specified distributional assumption (Tadesse and

Krishnamoorthy, 1997). Based on λ we can derive gamma (γ), which measures the effect of

technical efficiency in the variation of observed output. Battese and Corra (1977) defined γ

as the total variation of output from the frontier, which can be attributed to technical

efficiency.

The measurement of firm’ specific technical efficiency is based upon deviations or

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

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

to the potential production defining the level of efficiency of the individual farmer

(Ogundele, 2003). For such inefficient farms, improvements in technical efficiency may be

achieved through the improvement of their production techniques and this may imply

changes in the proportion of the productive factor through factor substitution under the

prevailing technology.

The two most commonly used package for estimating stochastic production frontier and

inefficiency are FRONTIER 4.1(Coelli, 1996a) and LIMDEP (Greene, 1993), with

FRONTIER 4.1 being most used because it incorporates the maximum likelihood estimation

of the parameters. It is flexible in the way that it can be used to estimate both production and

cost functions, can estimate both time varying and invariant efficiencies, or when panel data

is available, and it can be used when the functional form have the dependent variable both in

logged or original units. FRONTIER 4.1 is a single purpose package specifically designed

for the estimation of stochastic production frontiers (and nothing else), while LIMDEP is a

more general package designed for a range of non-standard (i.e. non-OLS) econometric

estimation. An advantage of the former model (FRONTIER) is that estimates of efficiency

are produced as a direct output from the package.

(ii) Allocative Efficiency

Though the technical efficiency is concerned with the physical relationship between input

and output, the allocative efficiency takes into account price relationship in addition to the

physical relationship. Thus, allocative efficiency is the optimum allocation of resources

taking into accounts the prices of the resources. In other words, it is the ability of choosing

optimal input levels for given factor prices.

(iii) Economic Efficiency

Economic efficiency is the integration of the technical and allocative efficiencies together

with the unit prices of inputs. Therefore, the presence of either of technical efficiency and

allocative efficiency is a necessary but not a sufficient condition to achieving the economic

efficiency. When technical efficiency and allocative efficiency are harmonized together, then

sufficient condition for achieving economic efficiency is provided (Yotopoulous and Nugent,

1976).

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. An economically efficient input-

output combination would be on both the frontier function and the expansion path. This

implies that both the necessary and sufficient conditions for optimal combination of inputs

and outputs are met (Xu and Jeffrey, 1998).

The basic concepts underlying Farrell (1957) approach to efficiency measurement are

illustrated in the figure below. The diagram showed the efficient unit isoquant as (SS 1) for a

S

A

Q

farm which uses the least amounts of inputs {labour (X1) and land (X2)} to produce a unit of

output.

X2 S P

Q

O A X1

Source: Singh et al, 2000

In the figure 1 above, production unit operating at point P utilizes two input factors

{labour (X1), and land (X2)} to produce a single output. SS1 is the efficient isoquant

estimated with the prevailing production technology. Point Q on the isoquant represents the

efficient reference of observation P. The technical efficiency (TE) of a production unit

operating at P is measured by the ratio

TE = OQ/OP………………………………………… (1)

This is equal to one minus QP/0Q. It takes a value between zero and one, and hence an

indicator of the degree of technical inefficiency of the production unit. A value of one

indicates the firm is fully technically efficient. For instance, the point Q is technically

efficient because it lies on the efficient isoquant.

If the inputs price ratio, represented by the slope of the isocost line SS’ in figure 1 is also

known, allocative efficiency can be inferred. The allocative efficiency (AE) of a firm

operating at point P is defined to be the ratio

AE = OR/OQ………………………………………… (2)

Since the distance RQ represents the reduction in production costs that would occur if

production were to occur at the allocatively (and technically) efficiency point Q’ (because it

Figure 1: Technical and allocative efficiencies in input-oriented measures

incurs minimum cost), instead of the technically efficient, but allocatively inefficient point Q

because given the price line SS’.

The total economic efficiency (EE) is the product of technical efficiency and allocative

efficiency

EE = (OQ/OP) X (OR/OQ) = OR/OP………………………..(3)

The distance RQ can be explained in terms of cost reduction. In order to ensure an

optimal combination of factors of production, the various existing farms should aim at

producing at point Q’.

The above discussions give an overview of input-based radial measures of efficiency as

they measure the differences in input use between farms for standardized (unit) output. The

radial nature of Farrell’s measures is taken along a ray from origin in input-input space and

this expressed the TE standard as a point on efficient isoquant SS’ having identical input

proportions to the farm whose efficiency is being measured. It also for a simplified cost

interpretation of the AE measures.

Farrell also proposed an output-based measure, which focused on the differences existing

in outputs between farms with standardized input levels. These measures were examined in

details by Timmer, 1971; Farrell and Lovell, 1978. They revealed that the input-based

measure is equivalent to the output based measure only when there is the case of

homogenous technology with constant returns to scale and that both measures break down

when technology is non-homothetic.

Thus, Battese (1992) showed a more general presentation of Farrell’s concept of the

production function (or frontier) as depicted in figure 2 below involving the original input

and output values. The horizontal axis represents the (vector of) inputs, X, associated with

producing the output, Y. The observed input-output values are below the production frontier,

given that farms do not attain the maximum output possible for the inputs involved, given the

technology available, A measure of the technical efficiency of the farm which produces

output, Y, with inputs, X, denoted by point A, is given by Y/Y*, where Y* is the “frontier

output” associated with the level of inputs, X (see point B). This is a measure of technical

efficiency, which is dependent on the levels of the inputs involved.

TE of Firm A =

Production frontier

B=(x,y*)

Output x

Y x

X x

x x

x

x x observed input-output values

x x x A (x,y)

y/y*         

0 x inputs, x

Figure 2: Technical efficiency of firms in input-output space.

Source: Battese, 1992.

2.6.2 Techniques of Efficiency Measurement

There exist two fundamental methods of measuring efficiency as illustrated in previous

literatures; these include: the classical method and the frontier method.

(a.) The Classical Method: This exists based on ratio of output to a particular input. It is also

known as the partial productivity measure because output is compared with an input at a

time; for instance, the measurement of land productivity is given by crop yield per unit of

farmland used. Measures such as tonnes per hectare are deficient in that they only deal with

the land input while ignoring all other inputs, such as labour, machinery, fertilizer and

chemicals. The application of the method in the formulation of management and policy

advice to farmers is likely going to result in excessive use of those inputs, which are not

included in the efficiency measure.

(b.) The Frontier Method: This method has an advantage in the sense that it measures the

productivity of all the inputs at once. The frontier method was developed as a result of the

inadequacy of the classical method. It entails the use of econometric, statistical and linear

programming techniques for analyzing efficiency related issues. The frontier method exists

as:

(1) Non-parametric approach: The non-parametric approach (Farell, 1957; Hanoch and

Rothchild, 1972; Diewert and Parkan, 1983) requires one to construct a free disposal convex

shape in the input- output space from a given sample of observations of inputs and output. It

is a mathematical programming approach often referred to as Data Envelopment Analysis

(DEA). This mathematical programming method focused mainly on the development of

DEA methods engaged with multiple-input and multiple-output production technologies.

DEA approach was first applied by Charnes et al, (1978). The frontier model in their study

assumed constant returns to scale (CRS) model. DEA applies operational programme to

construct piecewise linear production frontiers. One of the various advantages of DEA

approach is that it removes the necessity for the definition or specification of the functional

form of the production frontiers and their assumptions regarding the distributional form of

the Ui. DEA studies the producers’ behaviour by the efficient frontier and the distance

between a production unit and the frontier. The basic DEA models are deterministic. A

major criticism of this approach is that the convex shape, representing the maximum possible

output, is derived using only marginal data rather than all the observations in the sample.

Thus, the technical efficiency measures are susceptible to outliers and measurement errors

(Forsund et. al., 1980). Beke (2007) also affirmed that the method has very demanding data

needs. Finally, being non-parametric, no statistical inferences on the estimates can be carried

out (i.e. does not take into account the possible measurement error and other noise

influencing the data).

(ii) Parametric or Econometric Approach: This has been worked upon to develop the

stochastic frontier models based on the deterministic parameter frontier of Aigner and Chu

(1968). The Stochastic Frontier Analysis (SFA) recognizes the existence of the random noise

around the estimated production frontier. In a simple case of a single output and multiple

inputs, the approach predicts the outputs from inputs by the functional relationships; Yi = f

(Xi, β) +εi, where i denotes the production unit being evaluated and β‘s are the parameters to

be estimated. The residual εi is composed of a random error Vi and inefficiency component

Ui. If we assume that Vi = 0, then SFA is reduced to the Deterministic Frontier Analysis

(DFA). If we assume that Ui = 0, SFA will be reduced to central tendency analysis or

average response analysis. The relative merits of the Stochastic Frontier Analysis of

parametric approach are that it can account for noise as well as allowing the tests of

hypotheses to be conducted.

The econometric approach and the non-parametric approach are at variance in many

ways, but the essential differences and the sources of the advantages of one approach to the

other are captured by the two characteristics described below:

(a) The econometric approach is stochastic, and so attempts to distinguish the effects of noise

from the effects of inefficiency. The programming approach is non- stochastic, and lumps

noise and inefficiency together and calls the combination inefficiency.

(b) The econometric approach is parametric, and confuses the effects of misspecification of

functional form (of both technology and inefficiency) with inefficiency. The programming

approach is non parametric and less prone to this type of specification error.

2.6.2 Review of Production Frontier Models

The estimation of production frontiers has proceeded along two general paths: full-

frontier which forces all observations to be on or below the frontier and hence where all

deviation from the frontier is attributed to inefficiency; and stochastic frontier where

deviation from the frontier is decomposed into random component reflecting measurement

error and statistical noise, and a component reflecting inefficiency. The estimation of full

frontier could be through non-parametric approach (Meller, 1976) or a parametric approach

where a functional form is imposed on the production function and the elements of the

parameter vector describing the function are estimated by programming (Aigner and Chu,

1968) or by statistical techniques (Richmond, 1974; Greene, 1980).

The drawback of these techniques is that they are extremely sensitive to outliers; and

hence if the outliers reflect measurement errors they will heavily distort the estimated frontier

and the efficiency measures derived from it. The stochastic frontier approach, however,

appear more superior because it incorporates the traditional random error of regression. In

this case the random error, besides, capturing the effect unimportant left out variables and

errors of measurement in the dependent variable, it would also capture the effect of random

breakdown on input supply channels not correlated with the error of the regression. What

would have appeared as the major advantages of the full frontier models over the stochastic

model (i.e. the fact that they provided efficiency indexes for each firm) was later overcome

(Jondrow et al, 1982).

Several authors have used several approaches to analyze the determinant of technical

efficiency from stochastic production frontier functions. The first set of authors followed

two-step procedure in which the frontier production function is first estimated to determine

technical efficiency indicators while the indicators thus obtained are regressed against a set

of explanatory variables, which are usually firms’ specific characteristics. Authors in this

category included Kalijaran (1981), Greene (1993), Parikh and Shah (1994), Ben-Belhassen

(2002) and Ogundele (2003). While this approach is very simple to handle, the major

drawback is the fact that it violated the assumption of the error term. In the stochastic frontier

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

distributed (Jondrow et al., 1983). In the second step however, the technical efficiency

indicators obtained are assumed to depend on certain number of factors specific to the firm,

which implies that the inefficiency effects are not identically distributed.

The major drawback led to the development of more consistent approach, which modeled

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

parameters are estimated in one step using maximum likelihood procedure. Authors in this

category included Kumbhakar, Ghosh and McGuckin, 1991; Reifschneider and Stevenson,

1991; Huang and Liu, 1994; and Battese and Coelli, 1995 who proposed a stochastic frontier

production function for panel data. Other authors in recent time included Ajibefun et al.,

(1996); Coelli and Battese, 1996, Battese and Sarfaz, 1998; Seyoum et al.; 1998; Lyubov and

Jensen, 1998; Ajibefun and Abdulkadri, 1999; Ajibefun and Daramola,2003.

2.7 Stochastic Frontier Production Function: Technical Efficiency

Empirical estimation of efficiency is normally done with the methodology of stochastic

frontier production function. The stochastic frontier production model has the advantage of

allowing simultaneous estimation of individual technical and allocative efficiencies of the

respondent farmers as well as determinants of technical efficiency (Battese and Coelli, 1995).

The ideas of production function can be illustrated with a farm using n inputs: X1, X2, …

Xn, to produce output Y. Efficient transformation of inputs into output is characterized by the

production function f (Xi), which shows the maximum output obtainable from various inputs

used in production.

The stochastic frontier production function independently proposed by Aigner et al.,

(1977) and Meeusen and Van Den Broeck, (1977) assumes that maximum output may not be

obtained from a given input or a set of inputs because of the inefficiency effects.

It can be written as:

Where, Yi is the quantity of agricultural output,

Xa is a vector of input quantities and,

b is a vector of parameters

ε i is an error term defined as:

εi = Vi – Ui i = 1, 2, … n farms ………….. (5)

Vi is a symmetric component that accounts for pure random factors on production, which

are outside the farmers’ control such as weather, disease, topography, distribution of

supplies, combined effects of unobserved inputs on production etc. and U i is a one-sided

component, which captures the effects of inefficiency and hence measures the shortfall in

output Yi from its maximum value given by the stochastic frontier f(Xa; b)+ Vi.

The model is expressed as:

2.7.1 Technical Efficiency

The technical efficiency of production of the i-th farmer in the appropriate data set, given

the levels of his inputs, is defined by:

From equations (4) and (5) , the two components Vi and Ui are assumed to be independent of

each other, where Vi is the two-sided, normally distributed random error (

and Ui is the one-sided efficiency component with a half normal distribution (

. Yi and Xi are as defined earlier. The b’s are unknown parameters to be

estimated together with the variance parameters.

The variances of the parameters, symmetric Vi and one-sided Ui, are

respectively and the overall model variance given as are related thus:

=

The measures of total variation of output from the frontier, which can be attributed to

technical efficiency, are lambda (l) and gamma () (Battese & Corra, 1977) while the

variability measures derived by Jondrow et al., (1982) are presented by equations (9) and

(10):

……………………………….. ……………………….(9)

and

……………………………………………………….(10)

On the assumption that Vi and Ui are independent and normally distributed, the

parametersb, , , , were estimated by method of Maximum Likelihood

Estimates (MLE), using the computer program FRONTIER Version 4.1 (Coelli, 1996). This

computer program also computed estimates of technical and allocative efficiencies.

The farm specific technical efficiency (TE) of the i-th farmer was estimated using the

expectation of Ui conditional on the random variable (ei) as shown by Battese and Coelli

(1988). The TE of an individual farmer is defined in terms of the ratio of the observed output

to the corresponding frontier output given the available technology, that is:

(Tadesse and Krishnamoorthy, 1997)

So that:

O £ TE £ 1

2.7.2 Inferential Statistical Analysis

The following statistical methods were used to achieve the stated hypothesis.

a. F-test

In the MLE model a log (generalized) likelihood ratio test replaces the usual F-test of OLS

regression models to evaluate the significance of all or a subset of coefficients. It is also used

to test whether the summation of estimated coefficients of production function are at

constant, increasing or decreasing return to scale (Shakya and Flinn, 1985).

The generalized likelihood ratio test statistic is defined by:

……………………………………..(12)

Where L (H0) is the value of the log-likelihood function under the null hypothesis (i.e., the

restricted model likelihood function) and L (Ha) is the value of the log-likelihood function

under the alternative hypothesis (i.e., the unrestricted model likelihood function). If the null

hypothesis is true, the log-likelihood ratio test has a mixed Chi-square distribution with

degree of freedom equals the number of parameters excluded in the traditional average

response function. This method was used to test for the first hypothesis.

b. The Generalized Likelihood Ratio Test

This ratio defined by the test statistic in equation (12) is also used to test for the presence

of inefficiency effects in the frontier models and the half normal distribution of the

inefficiency effects. The decision rule is that the null hypothesis is accepted if the computed

Chi-square is less than the tabulated Chi-square at 5% level of significance and a given

degree of freedom. This method was used to test for the second hypothesis.

c. T-Ratio Test

In order to test for the significance of the estimated coefficients of socio-economic

variables on the predicted inefficiency function, t-ratio test was used.

The test statistic is given by

tc = bj ………………………………………………(13)

Sbj

Where bjs are the estimated coefficients and Sbjs are the standard errors of the estimated

coefficients. The test stipulates that the null hypothesis (H0), H0= bJ = 0 that is, the

explanatory variable is not significant in explaining the variation in the dependent variable.

The decision rule is that Ho is accepted if t computed is less than t tabulated at a given level

of significance and degree of freedom and Ho is rejected if otherwise. This test was used to

test for the third hypothesis.

The T-test statistic is used where the population variance ( ) is unknown and the sample

size is less than 30 which renders the sampling distribution of means no longer normally

distributed. For this study, instead of using the Z-statistic, a student’s t-ratio was used on the

condition that the population of variable X is normally distributed

d. The T-Test

The t-test is a statistical tool used to test the significance of difference between two

sample means. Thus, the test was used to test for the significant difference between the

means of technical efficiencies (TE) between the cassava farmers in Oluyole and Akinyele

Local Governments. t is computed using the formula

t = X1 - X2

SX1 – X2 ……………………………………………….(14)

Where: t is the test statistic, X1 and X2 are the sample means of TE for Oluyole and

Akinyele Local Governments of Oyo State respectively. S X1 – X2 is the estimated standard

error of the difference, and it is computed using the formula

S12+ S2

2 ……………………………………… (15)

S X1 - S X2 = n1 n2

Where S2, pooled variance is computed as

S2 = (n1 – 1)S12 + (n2-1) S2

2 …………………………………(16)

n1 + n2 – 2

n1 and n2 are sample sizes for variables 1 and 2 respectively; S12 and S2

2 are the variances

for variables 1 and 2 respectively; and n1 + n2 – 2 is the degree of freedom.

Hence,

t = X1 - X2

S12 + S2

2 ………………………………………(17)

n1 n2

(Blalock, 1972; Oloyo, 2001)

2.8 The Empirical Frameworks on Gender and Technical Efficiency

Ajibefun et al., (2002) employed a stochastic frontier production function to analyze

technical efficiency and technological change in Japanese rice industry. The results of the

study showed that the technical inefficiency effects were statistically significant but time

invariant. There was evidence of neutral technological change. Technical efficiencies of the

average rice farm households in the prefectures were only moderately high and the mean

technical efficiency was estimated to be 74.5 percent. It was also shown that the returns –to-

scale parameter was not significantly greater than unity, indicating constant returns to scale,

at the average levels of the inputs used by the rice farmers.

A study of wheat farmers in Pakistan by Battese et al., (1996) applied a single stage

model for estimating technical efficiencies. The inefficiency variables were identified as age

of the farmer, maximum years of schooling and ratio of adult males to the total household

size and were incorporated along with the production variables of land, labour, dummy

variables for fertilizers, land preparation, number of ploughs and quantity of seeds. The

technical inefficiency effects were highly significant meaning that the traditional production

function model was inadequate for the analysis of wheat production in the four districts

involved. The technical efficiency of wheat farmers displays considerable variation over

time within each district such that the mean technical efficiencies ranged from 57 percent to

79 percent in the four districts.

A study of grain production in China by Yao and Liu (1998) specified the dependent

variable as the total output of grain. The independent production variables were land, labour,

machinery, fertilizer and irrigation. The inefficiency variables in their model include

research and development, disaster index, rural population share and crop labour share. The

results of the study revealed that considerable regional differences existed in grain yields and

that there was still a vast potential for raising grain output. The short-term solution is to use

more land augmenting inputs such as fertilizer and irrigation in the medium and low-yield

region. The diminishing return however was applied to shrinking land. Growth in grain

output in the long-term must therefore rely on improvement in technical efficiency.

Seyoum, et al., (1998) carried out a study on technical efficiency and productivity of

maize farmers within and outside the Sasakawa –Global 2000 project in Ethiopia. Their

study used stochastic frontier production analysis in which the technical inefficiency effects

were assumed to be function of age and education of the farmers, together with the time

spent by extension advisers in assisting farmer in their agricultural production operation. The

Cobb-Douglas stochastic frontiers were used for farmers within and outside the project. The

empirical results indicated that farmer within the SG 2000 project were more technically

efficient than farmer outside the project, relative to their respective technologies. The mean

frontier output of maize for farmers within the SG-2000 project was significantly greater than

that for the farmers outside the project.

The study conducted in 1976 by Moock as reported by Quisumbing et al., (1996)

affirmed that the educational level of female farmers was a significant determinant of

technical efficiency. Moock estimated that giving all women farmers at least a year of

primary school education would raise yields by 24%. Yields would also increase be 6% if

women farmers were given certain input levels and characteristics and by 9% if they were

given men’s input levels and characteristics. It was also noted that better-educated farmers

are likely to use better inputs and have better access to credit, improved seeds and fertilizers. 

In the recent times, cross-country regressions have been estimated to support the theory that

gender inequality in education had a significant negative impact on economic growth in

general and had prevented Africa and South Asia from achieving desired development goals.

Both these regions have a large agricultural sector and low female literacy rates that would

have importance for rural development planners (Klasen, 2000). 

Udry’s analysis of data from Burkina Faso (1996) revealed that plots controlled by

women showed yields that were substantially lower by as much as 30 percent than those

controlled by men which clearly violate the Pareto efficiency of resource allocation within

the household. On average, the values of output per hectare were much higher for the plots

controlled by women though average plot size was much smaller. Interestingly, labor inputs

by men and children from the household and by non-household members was higher on plots

controlled by men while female labor was used more intensively on the plots worked by

women. The difference in yields does not imply that women are less efficient since the

estimations are not based on production functions but on reduced form equations. This could

be attributed to the differences in labor input and also the exclusive use of fertilizer on plots

controlled by men. The analysis concluded that reallocation of land, labor and fertilizer

would increase production of a crop for a household in a given year.

Testing the same data for productive efficiency, Udry et al., (1995) found that the plots

controlled by women had significantly lower yields than those controlled by men,

simultaneously planted to the same crop within the same household, on average about 18 %

lower. In one case, that of sorghum, there was a much larger decline, of about 40 %. Even

though women specialized in vegetable crops, these also showed a 20 percent decline in

yields. Again, these estimations were based on reduced form equations and not production

functions, so the differences in yield do not imply that women are less efficient cultivators

than men. The study also finds that female labor is much more productive than male labour;

the gender yield differential is caused by the difference in factor intensities. The Cobb –

Douglas production function estimates imply that output could be increased by between 10

and 20% by reallocating the factors of production actually used between plots controlled by

men and women in the same household. 

Onyenweaku and Nwaru (2005) carried out a study to measure the level of technical

efficiency and its determinants in food crop production in Imo State of Nigeria using a

stochastic frontier production function. The results of their study showed that the estimated

farm level technical efficiency ranges from 31.05 percent with a mean of 57.14 percent. The

results showed that the observed wide variation in the level of technical efficiency indicates

that ample opportunities exist for the farmers to increase their productivity and income

through improvements in technical efficiency. Credit, education, farming experience, farm

size and membership of farmers associations/cooperative societies were found to be

positively and significantly related to technical efficiency while age and household size were

negatively but significantly related to technical efficiency. The study found no relationship

between gender and technical efficiency.

In Bangladesh the introduction of new technology for the growth of vegetables by

women was hampered by social and cultural limitations not built into project design (Naved,

2000). Due to the limited mobility of women they were constrained to apply the new

technology only in the homestead plots resulting in small output and incomes. Moreover,

men mostly controlled the sale of output so that there was little increase in women’s resource

ownership.   

Panin and Brummer’s study (2000) used farm management survey data on smallholder

farmers in 8 villages of Botswana to test whether differences in farm resource ownerships

between male and female farmers result in crop productivity differentials. They investigated

whether farmers were functioning with respect to the production frontier. Female farmers by

virtue of their lack of access to crucial inputs including land, labor, education and credit were

actually functioning under different circumstances as compared with their male counterparts.

Cross-sectional data on 92 female headed households and 189 male headed households were

used, Panin and Brummer estimated a Cobb-Douglas production function and found that if

women and men are supplied with the same level of factors of production in the same area

they should have approximately the same level of output per hectare. Further, all the gender-

input interaction coefficients were statistically insignificant showing that there are no

differences in production elasticities between male headed and female headed households, all

the farmers in the same area use the same technology and combine inputs in a similar way.

The gender differences in resource ownership do not imply productivity differences. Sources

for this difference may be traced to the level and quality of input used. It also found that the

level of education has an impact on crop production.  

Dey Abbas, (1997) underlines the particular problem of the limited control that women

have over the timing and amount of their labor. Unlike men, women face varied demands on

their time: household tasks, child bearing and rearing responsibilities within the home and

agricultural tasks outside. Women typically work on food crops and small personal plots and

also provide labor on the family fields. Men, by contrast, do not work on the small plots

attached to the home. Some of the studies presented above attest to the fact that labor input

on the family fields comes from men, women, sometimes children and even hired workers.

Women are at a disadvantage since they can rely only on their own labor.  

The inadequacy of extension services in general is a constraint on productivity. For

women farmers the problem is compounded by the fact that the few agents available are

usually male. Social circumstances may prevent any interaction between the agent and the

female farmer. The few female extension workers are often responsible for home economics

issues rather than agriculture. The unitary model depends on free flow of information but the

more detailed picture drawn by the collective model clearly demonstrates that this is not

necessarily the case. It cannot be assumed, as it often is, that extension messages will be

disseminated to other members of the household. An evaluation of the performance of T&V

extension in Kenya (Bindlish and Evanson, 1993) found a gender-based difference in the

adoption rates of extension messages with regard to fertilizers. The sample included 36%

female-headed households, defined as households either actually headed by women or where

the farm was managed by the wife as the husband was gainfully employed elsewhere. The

new technology was adopted by 100% of the male headed-households, in either one of the

two forms presented. In contrast, only 44 percent of the female-headed households adopted

one form and 18% the other. Similar constraints were identified in pest and disease control

measures also. The proportion of male-headed households receiving extension advice was 81

percent compared with 49 % of female-headed households. 

Low productivity of plots farmed by women as compared with men can also be traced to

the inability to obtain credit. The general paucity of resources controlled by women leaves

them unable to provide required collateral. When obtained, the use of credit also reflects

gender differences. Using data from a survey carried out in 87 villages in 1991-92, Pitt and

Khandker’s empirical study (1995) on gender and micro-credit programs found that making

credit available to women had a larger impact on the household welfare. The annual

household consumption increased 18 taka for every 100 additional taka borrowed by women

as compared with 11 taka for men’s borrowings. Natural resource management schemes that

do not explicitly incorporate gender concerns are susceptible to failure as in the case of a

reforestation program in the Dominican Republic as found by Fortmannn and Rocheleau

(Alderman et al., 1994). Following men’s needs only indigenous and exotic pines were

planned for watershed management and timber requirements. Women needed palm fronds for

fiber to make baskets and trees for fuel wood supplies. Extra time spent in gathering fuel

wood forced some women to give up their cassava bread processing operations. 

 

CHAPTER THREE

3.0 Research Methodology

3.1 The Study Area

This study was carried out in Oluyole and Akinyele local government areas of Oyo state.

The study area represents two out of the eleven local government areas under Ibadan /Ibarapa

zone of Oyo State Agricultural Development Programme (OYSADEP). The study area is

situated within the tropical rainforest region and agriculture is the predominant occupation in

the study area. The study area has been chosen due to the existence of the large numbers of

smallholder cassava farmers in the area. Ibadan/Ibarapa zone has a large number of

smallholder farmers, thus it allowed for a reasonable selection of the representative sample of

smallholder cassava farmers.

The climate in the study area is of tropical type with two distinct rainfall patterns. The

rainy season, which marks the agricultural production season is normally between the months

of April and October. The heaviest rainfall is recorded between the months of June and

August while driest months are November to March. The average total annual rainfall ranges

between 1000mm and 1500mm with high daily temperature ranging between 280C and 300C

(FAOSTAT, 2004).

Agriculture is the main occupation of the people and the major food crops grown in the

study area include maize, rice, yam cassava and cocoyam while the major cash crops grown

are: cocoa, kola nut and oil palm.

3.2 Sources and Type of Data

The data collected include socio-economic characteristics of farmers such as age, gender,

years of formal education or educational level, marital status, household size, years of

experience in farming, income level, off-farm activities, income sources and amount of farm

credit and loans, expenditure and problems encountered in agricultural production.

Input-output data were also collected. Output data included quantity and values of

cassava output, market prices while input data include quantity and cost of inputs such as

farm size, hired labour, family labour, fertilizers, seeds, chemical and amount on farm

implements. The data obtained pertained to 2006 season and were obtained between the

months of September and November, 2006.

3.3 Sampling Technique

The study used a multi-stage stratified random sampling technique. The first stage

involved purposive selection of Oluyole and Akinyele local government areas noted for

cassava production in Oyo State. The second stage involved random selection of major

villages from the list of cassava-growing villages obtained from the information units of each

LGA. A total of eight villages were sampled, that is, four villages from each of the LGAs. In

Oluyole local government, the villages sampled include: Onidajo, Alata, Olosa and Onipe

while the villages sampled in Akinyele local government included Elekuru, Agbedo, Alore

and Oreku. The last stage involved a stratified random sampling selection of cassava farmers

from each of the four villages in each of the two LGAs (Oluyole and Akinyele) in Oyo State.

A total of 245 cassava farmers (124 male and 121 female) out of the 256 cassava farmers

(128 male and 128 female) interviewed with the aid of a structured questionnaire had

complete information necessary for data analysis as 11 of the respondents ( 3 male and 8

female) had their questionnaire not proper filled .

3.4.0 Methods of Data Analysis

The analytical techniques employed in this study include: the descriptive statistics,

budgeting technique and stochastic frontier production. The descriptive statistics was used to

capture objective (i) (i.e. to discuss the socio- economic characteristics of the male and

female respondents) and objective (v) (i.e. to identify the major constraints to cassava

production in the study area). Budgetary technique was used to analyze objective (ii) (i.e.

examine the costs and returns to cassava production by male and female farmers); Stochastic

Frontier Production Function (Cobb Douglas functional form) was used to analyze objective

(iii) (i.e. to analyze the technical efficiency of the male and female cassava farmers in the

study area) and objective (iv) (i.e. to examine the relationship between the socio-economic

characteristics of cassava farmers (male and female) and their technical efficiencies.

3.4.1 Descriptive Statistics

The descriptive statistics used include: the use of percentages, frequency distribution,

mean, standard deviation, mode, minimum and maximum values. They were used to discuss

the socio-economic and production data of the male and female cassava farmers.

3.4.2 Budgeting Technique

The budgeting technique entailed the use of gross margin (GM) to determine the

profitability of the cassava cultivation.

The GM was specified as shown below:

GMi = TR - TVCi……………………………………..(18)

GM = PQ – Σ CiXi……………………………...........(19)

i=1

Where, GM = Gross Margin

P = price of cassava tuber/ pick-up load

Q = cassava tuber yield (pick-up load)

C1 = Price of stem cutting/bundle

C2 = Price of fertilizer/Kg

C3 = Price of labour/man-day

C4 = Price of herbicide/litre

C5 = Price of pesticide/litre

X1 = Quantity of stem cutting (bundle)

X2 = Quantity of fertilizer (Kg)

X3 = Quantity of labour (man days)

X4 = Quantity of herbicide (litre)

X5 = Quantity of pesticide (litre)

In order to calculate the GM for this study, inputs costs were valued at prices paid by the

farmers or village market prices. In this study, some cassava stem cuttings used during the

cropping season were obtained from the inventory of farmers’ previous harvest. Values were

imputed for such bundle of stem cutting using the average market prices. The prices paid by

each farmer (including transportation costs) were used to determine expenditure on fertilizer,

herbicide and pesticide. Labour was valued at opportunity costs or wage rate paid by farmers

for the operations in the villages. However, costs were imputed for family labour utilization.

3.4.3.0 Efficiency Determination

The econometric method, in form of the stochastic frontier production function was used

to estimate technical efficiency of the male and female cassava farmers and also in

examining the influence of some socio-economic variables on technical efficiency of the

male and female cassava farmers respectively.

3.4.3.1 Models Specification

In congruent with the works of several scholars like the one of Seyoum, et al (1998)

where the Cobb-Douglas stochastic frontiers was used in estimating the technical efficiency

and productivity of maize farmers within and outside the Sasakawa –Global 2000 project in

Ethiopia. Therefore, for the sake of this study, the stochastic frontier production functions in

which Cobb-Douglas as proposed by Battese and Coelli (1995) represents the best functional

form of the production frontier and also as confirmed by Yao and Liu (1998) was applied in

the data analysis in order to better estimate the efficiency of male and female cassava

farmers.

The model of the stochastic frontier production for the estimation of the TE is specified

as:

Where subscript i refers to the observation of the ith farmer, and

Y = output of cassava tubers (Kg)

X1 = Stem Cuttings (bundles)

X2 = Farm Size (ha)

X3 = Fertilizer Quantity (litre)

X4 = Herbicide Quantity (litre)

X5 = Pesticide Quantity (litre)

X6 = Hired Labour (Manday)

X7 = Family Labour (Manday)

bi's = the parameters to be estimated

ln's = natural logarithms

Vi = the two-sided, normally distributed random error

Ui = the one-sided inefficiency component with a half-normal distribution.

3.4.3.2 The Inefficiency Model

For this study, it is assumed that the technical inefficiency measured by the mode of the

truncated normal distribution (i.e. Ui) is a function of socio-economic factors (Yao and Liu,

1998). Thus, the technical efficiency was simultaneously estimated with the determinant of

technical efficiency defined by:

Where:

Ui = technical inefficiency of the ith farmer

Z1 = Age of farmer (years)

Z2 = Household Size

Z3 = Year of farming experience

Z4 = Educational level

Z5 = Extension Contribution

The above equation was used to examine the influence of some of the male and female

farmers’ socio-economic variables on their technical efficiency. Therefore, the socio-

economic variables in equation above were included in the model to indicate their possible

influence on the technical efficiencies of the male and female cassava farmers.

In the presentation of estimates for the parameters of the above frontier production, two

basic models were considered. Model 1 is the traditional response function in which the

inefficiency effects (Ui) are not present. It is a special case of the stochastic frontier

production function model in which the parameter = 0. Model 2 is the general frontier

model where there is no restriction in which , are present. The estimates of the stochastic

frontier production function were appraised using the generalized likelihood ratio test, and

the T-ratio for significant econometric relevance.

CHAPTER FOUR

4.0 RESULTS AND DISCUSSION

4.1 Socio-Economic Characteristics of the Male and Female Cassava Farmers in

Oluyole and Akinyele Local Government Areas of Oyo State.

This section discusses the socio-economic characteristics of the respondents.

4.1.1 Distribution of Respondents by Age

The age distribution of the respondents is presented in Table 1. It is observed from the

table that majority of the male and female cassava farmers (36.3 % and 35.5 %) had their age

between 45-54 years in the study area respectively. This is the most productive age range of

the farmers. About 17.7 % and 19.8 % of the male and female cassava farmers had their age

equal to or more than 65 years respectively in the study area.

Table 1: Age Distribution of Respondents

Male Female

Age Frequency % Frequency %

25-34 13 10.5 11 9.1

35-44 21 16.9 22 18.2

45-54 45 36.3 43 35.5

55-64 23 18.6 21 26.5

>65 22 17.7 24 19.8

Total 124 100 121 100

Mean Age: 50 50

Source: Computed From Field Survey Data, 2006.

4.1.2 Distribution of Respondents by Level of Education

Table 2 shows the distribution of the educational level of the respondents. The level of

education attained by a farmer is known to influence the adoption of innovation, better

farming decision making including efficient use of inputs. The study showed that majority of

the male (31.2 %) and female (37.2 %) cassava farmers had about 6years of formal education

respectively in the study area. The finding implies that literacy level is moderately high

among the male and female cassava farmers as expected in the study areas.

Table 2: Educational Level Distribution of Respondents

Male Female

Educational Level Frequency % Frequency %

Non-formal/adult 28 22.4 22 18.19

Primary 39 31.2 45 37.2

Secondary 25 20 38 31.4

ND/NCE 22 24.8 16 13.22

HND/B.Sc 10 8 - -

Total 124 100.0 121 100.0

Mean Value: 12years (Secondary school) (Male and Female)

Source: Computed From Field Survey Data, 2006.

4.1.3 Distribution of the Respondents according to their Farming Experience

It is expected that the number of years farmers spent in their farm operations, the more

experienced they should have become. Table 3 shows the distribution of farming experience

of respondents. It could be seen in table 4.4 that majority of the male (67.1%) and female

(70.25%) cassava farmers had experience of more than 10 years in the study area. In the

male cassava farms, the rest 32.9% of them had less than 10 years of farm experience while

the rest 29.74% of the female cassava farmers also had less than 10years of farming

experience. The results show that the male and female cassava farmers are well experienced

in cassava production in the study areas.

Table 3: Distribution of Respondents According to their Years of Farming Experience

Male Female

Years Frequency % Frequency %

<5 14 13.7 17 14.04

5-10 24 19.2 19 15.7

>10 86 67.1 85 70.25

Total 124 100.0 121 100.0

Mean: 21years 18years

Source: Computed From Field Survey Data, 2006.

4.1.4 Distribution of the Respondents by Sex

Table 4 shows the distribution of the male and female cassava farmers according to their

sex in the study area.

Table 4: Sex Distribution of Respondents According To L.G.A

Male Female

L.G Frequency % Frequency %

Oluyole 74 59.68 74 61.16

Akinyele 50 40.32 47 38.84

Total 124 100.0 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.5 Distribution of Respondents by Marital Status

Table 5 presents the distribution of respondents by marital status. It is shown in the table

that majority of the respondents were married. About 68 % of the male and 78 % female

cassava farmers were married in the study area. These results have implications on cassava

production in the study area. Married men and women are likely to be relatively stable and

focused in carrying on their farming activities and the likelihood that they will have more

people in the household who contribute to labour input, hence, availability of more family

labour.

Table 5: Distribution of Respondents by Marital Status

Male Female

Marital Status Frequency % Frequency %

Single 12 10 - -

Married 85 68 94 78

Widowed 18 15 24 20

Divorced 9 7 3 2

Total 124 100.0 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.6 Distribution of Respondents by Household size

The family members represent those being fed, clothed and housed by a farmer. This can

be an important indicator of his productivity on the farm if the farmer has no other

occupation apart from farming. The size of the household affects the amount of farm labour,

determines the food and nutritional requirement of the household, and often affects

household food security. Table 6 shows the distribution of respondents according to

household size. Results in the table showed that majority of the male (43.5%) and female

(38%) cassava farmers in the study area have household size of between 6-8 members

respectively. About 22.6 % of the male and 25.5% of the female cassava farmers have more

than 6-8 members per family respectively. It is expected that the family members of a farm

operator will contribute labour to farm work, thus, the farmers’ household member in the

study area are involved in the planting, weeding, and harvesting of cassava.

Table 6: Distribution of Respondents According to Household Size

Male Female

Household size Frequency % Frequency %

≤ 5 42 33.9 44 36.4

6-8 54 43.5 46 38

9- 11 24 19.4 28 23

>11 4 3.2 3 2.5

Mean: 11 10

Total 124 100.0 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.7 Distribution of Respondents According to their Occupation

Table 7 shows the analysis of the distribution of the male and female cassava farmers in

the study area according to their major occupations. It showed that majority of the male

(65%) and female (55%) cassava farmers in the study area were actively in the supervision of

their cassava production enterprise respectively. About 30% and 45% of the male and

female cassava farmers were involved in other businesses respectively. The rest 5% of the

male cassava farmers were civil servants. The distribution of the male and female cassava

farmers based on their major occupations has a direct effect on the level and degree of

supervision of the farm business and economic efficiency of the farm operations.

Table 7: Distribution of the Male and Female Cassava Farmers According to their Occupation Type.

Male Female

Occupation Frequency % Frequency %

Farming 81 65 67 55

Business /Trading 19 15 42 35

Artisans (Driving, 19 15 12 10

Tailoring, Mechanic)

Public/Civil Servant 5 5 - -

Total 124 100.0 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.8 Distribution of Respondents According to Farm Size

The crop output of any farmer depends on the size of farm he/she operates. The

distribution of farm size cultivated by the respondents is presented in Table 8. It could be

seen from the table that majority of the male (92.7%) and female (93.4%) cassava farmers in

the study area cultivated farm size of between 1-5 hectares respectively. About 6.5% of the

male and 5.8% female cassava farmers cultivated a farm size of between 6-10 hectares

while about 0.8 % of the male and female cassava farmers cultivated farm size of 10 hectares

and above. The findings with respect farm size in this study are in congruent with the

findings of Olayide (1980) that stated that generally majority of the farmers are into small

scale production in Nigeria.

Table 8: Farm Size Distribution of Respondents

Male Female

Farm Size (Ha) Frequency % Frequency %

1 - 5 115 92.7 113 93.4

6- 10 8 6.5 7 5.8

>10 1 0.8 1 0.8

Mean: 3.7 3.5

Total 124 100.0 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.9 Distribution of Mode of Land Acquisition for Cassava Cultivation

The nature of access gained to a particular parcel of farmland largely determines the

extent and magnitude of use right and privileges of the farmers. Table 9 showed the mode of

land acquisition predominant in the study area. It could be seen that majority of the male

(34.4%) cassava farmers gained access to their land by inheritance while only 14.88% of the

female cassava farmers had land by inheritance and this is in congruent with the works of

Fabiyi, (1974, 1985); Adekanye,(1985) ; Famoriyo,(1985) ; and Adeyemo,(1991) ; who

posted and demonstrated, that women do not readily have access to land by inheritance and

this constraint, adversely affect their productivity and well being.

Majority of the female (31.40 %) cassava farmers in the study area had land leased to

them either by their husbands or by extended family members of the husband, 28.93% of the

female cassava farmers had land given to them as gift mostly from their husbands, most of

which are not as productive as before and this is consistent with the findings of Karl (1983).

Table 9: Distribution of Respondents by Mode of Land Acquisition

Male Female

Mode Frequency % Frequency %

Owned (Inheritance) 43 34.4 18 14.88

Leased 31 24.8 38 31.40

Purchased 32 25.6 30 24.79

Gift 18 15.2 34 28.93

Total 124 100.0 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.10 Distribution of Respondents by Access to Extension Services

The respondents’ access to extension services is presented in Table 10. The extension

services involve the dissemination of proven agricultural techniques and production

innovations to cassava farmers with the aim of improving their production capacity. The

table showed that majority of the male (62.4%) and female (66.9%) cassava farmers in the

study area had access to extension services respectively. This had a significant influence on

their output and puts them on the same level playing field to be better producers of cassava.

This finding is incongruent with the findings of Seyoum et al., (1998); Bindlish and Evanson

(1993) who attested to the fact that inadequate access to extension services hampers the

productivity of the women farmers.

Table 10: Distribution of Respondents’ Access to Extension Services

Male Female

Access to Frequency % Frequency %

Extension Services

Yes 78 62.4 81 66.9

No 46 37.6 40 33.1

Total 124 100.00 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.12 Distribution of Respondents by the Quantity of Fertilizer Used.

Table 11 showed the quantity of fertilizer used by the male and female cassava farmers in

the study area. The table revealed that fertilizer usage is very high in the study area as

majority of the male (72%) and female (75%) cassava farmers used between 5-10kg of NPK

fertilizer on their cassava farms intercropped with other cereal crops. This suggests that the

land on which cassava is cultivated is marginally fertile.

Table 11: Distribution of Respondents by the Quantity of Fertilizer Used.

Male Female

Fertilizer Qty (kg) Frequency % Frequency %

< 5 26 21 21 17

5 - 10 89 72 91 75

11- 15 9 7 9 8

Total 124 100.00 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.15 Distribution of Respondent by the Quantity of Pesticide Used.

Table 12 showed the quantity of herbicide used by the male and female cassava farmers

in the study area. The table revealed that herbicide usage is moderate in the study area as

majority of the male (81%) and female (79%) cassava farmers used less than 5litres of

Gramazone on their cassava farms intercropped with other cereal crops. This suggests that

the land on which cassava is moderately overtaken by weeds.

Table 12: Distribution of Respondents by the Quantity of Herbicide Used.

Male Female

Herbicide Qty (Litres) Frequency % Frequency %

< 5 100 81 96 79

5 - 10 24 19 25 21

Total 124 100.00 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.16 Distribution of Respondents by the Quantity of Pesticide Used.

Table 13 showed the quantity of pesticide used by the male and female cassava farmers

in the study area. The table revealed that pesticide usage is moderate in the study area as

majority of the male (71%) and female (66%) cassava farmers used less than 5litres of

pesticide on their cassava farms intercropped with other cereal crops. This suggests that the

land on which cassava is moderately susceptible to pests’ invasion.

Table 13: Distribution of Respondents by the Quantity of Pesticide Used.

Male Female

Pesticide Qty (Litres) Frequency % Frequency %

< 5 88 71 80 66

5 - 10 36 29 41 34

Total 124 100.00 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.17 Distribution of Respondents According to Sources of Credit

Availability of credit helps in the procurement of inputs on a timely basis. It also helps

in the adoption of yield increasing innovation thereby increasing the efficiency of farmers.

Table 14 indicates the sources of credit available to the male and female cassava farmers

in the study area. It is shown by the table that majority of the male (52%, 24% and 24%) and

female (38%, 25%, and 25%) cassava farmers obtained their funding from informal sources

like Personal savings, Family members and Relatives/Friends while only 12% of the female

cassava farmers financed their cassava production through Cooperative. This might be an

indication of the fact that it is easy to obtain credit from non-institutional sources than

institutional sources.

Table 14: Distribution of Respondents by their Sources of Credit Facilities

Male Female

Sources of Credit Frequency % Frequency %

Personal savings 64 52 46 38

Family members 30 24 30 25

Friends/Relatives 30 24 30 25

Cooperative society - - 14 12

Commercial Banks - - - -

Total 124 100.0 121 100.0

Source: Computed From Field Survey Data, 2006.

4.1.15 Distribution of Respondents by the Amount of Credit Obtained

It is expected that the larger the amount of credit available to cassava farmers, the greater

the farmers’ tendencies of adopting improved technologies which in turn enhance the

productivity of the male and female cassava enterprises in the study area. Table 16 shows

the distribution of amount of credit obtained by the respondents. The many of the male (38%)

and female (33%) cassava farmers obtained credit of between N61,000- N80,000

respectively. The table also showed that 15% and 17% of the male and female cassava

farmers would adequately manage credit amounts of over N100,000 and above.

Table 15: Distribution of Respondents by the Amount of Credit Obtained.

Male Female

Amount (N) Frequency % Frequency %

< 40,000 20 16 28 23

41,000-60,000 16 13 22 18

61,000-80,000 47 38 40 33

81,000- 100,000 22 18 10 9

> 100,000 19 15 20 17

Total 124 100.0 121 100.0

Source: Computed From Field Survey Data, 2006.

4.2 Gross Margin Analysis

The analysis of gross margin to determine the profitability of cassava production of the

male and female farms is presented in this section. The gross margin per hectare, defined as

the difference between gross revenue per hectare and total variable costs of production per

hectare is shown in Table 16. The average gross margin per hectare for farmers in the male

and female cassava farms in the study area was about N 29,700 and N28, 250 respectively.

These results, which are in line with other findings suggested that cassava production is

profitable in the study area. However, it is more profitable for both the male and female

cassava farmers to continue to produce cassava in the study area based on their level of gross

margins per hectare

Table 16: Costs and Returns per Hectare of the Respondents in Oluyole and

Akinyele Local Government Areas of Oyo State

Male Female

Cassava yield (trucks/ha) 6.5 6.5

Price/truck 17,000 17,000

Total Revenue/ha 137,700 137,700

Variable Cost of Material and Labour Inputs

Cost of cassava stem cuttings (bundle/ha) 2,000 2,200

Cost of fertilizer and Chemical inputs/ha 7,500 7500

Total Cost incurred on all labour works /ha 75,500 77,000

Total transportation cost/ha 16,500 16,550

Land rent/ha 6,500 6,000

Total Variable Cost/ha 108,000 109,250

Fixed Cost

Tool Cost 3500 3000

Gross Margin (TR/ha – TVC/ha) 29,700 28,250

Benefit-Cost Ratio 1.234 1.226

Source: Computed From Field Survey Data, 2006.

4.3.0 The Stochastic Frontier Production Function Analysis

This section discusses the results of technical efficiency estimates of the male and female

cassava farms in Oluyole and Akinyele Local Government Areas of Oyo State. Two

functional forms of the stochastic production frontier model were tried (Linear and Cobb

Douglas functional forms)but only the Cobb Douglas type provided the best fit based on the

explicit detail of the technical efficiency of the male and female cassava farmers as well as

the number of significant variables in the model. Kalirajan and Flinn (1983), Dawson and

Lingard (1989) alluded to the fact that the Cobb Douglas type has certain advantages over the

other functional forms.

4.3.1 Signs and Significance of Estimates of Stochastic Frontier Production

Function(i.e. Cobb-Douglas Frontier Function Type)

The ordinary least square (OLS) (Model 1) and the maximum likelihood parameter

estimates (MLE) (Model 2) of the stochastic production frontier models which was specified

as Cobb-Douglas frontier production function for male and female are presented in Tables 17

to 18. The coefficients of the variables are very important in discussing the results of the

analysis of data. These coefficients represent percentage change in the dependent variables as

a result of percentage change in the independent variables.

Among the male cassava farmers, the variables that were significant included pesticide

quantity used ( at 1%) and hired labour employed ( at 1%) while the other variables like stem

cuttings, farm size, fertilizer quantity used, herbicide quantity used and family labour

employed were all not significant at all known levels of significance. By implication, the

above findings revealed that the major productive inputs that greatly impact on the cassava

output of the male cassava enterprise were the quantity of pesticide used on their farms as

well as the amount of mandays of hired labour employed aside the availability of family

labour. Pesticide quantity had the highest coefficient, with a value 0.3050 in the preferred

model (model 2) and by implication the quantity of pesticide used existed as the most

important input that impact on cassava output of the male farmers. In the preferred model

(model 2) for the male farmers, farm size, herbicide and hired labour carried negative signs

while the others like stem cutting, fertilizer quantity, pesticide quantity and family labour

carried positive signs. The economic implication of the signs is that any increase in the

quantities of viable stem cuttings, fertilizers, pesticide and the amount of family labour

employed would lead to an increase in cassava output of the male farmers, while an increase

in the quantities of farmland, herbicide and hired labour would lead to a decrease in output of

cassava. Negative coefficient on a variable might indicate an excessive utilization of such a

variable. In economic terms, any attempt to increase the quantities of stem cuttings, fertilizer,

pesticide and family labour will be tantamount to raising the level of the cassava outputs of

the male farmers. Also, to allow for the proposition of a better cassava output status, the male

farmers have to engage the size of farmlands that they can actively supervised into cassava

production, control cost incurred on hired labour and herbicide.

Among the female cassava farmers, the significant variables include: fertilizer quantity

( at 1%), herbicide quantity (at 1%) and pesticide quantity ( at 1%) while the other variable

like stem cuttings, farm size, herbicide quantity used, family labour and hired labour were all

not significant at all known levels of significance. The implication of the above findings is

that the productive inputs that greatly impact on cassava output of the female farmers were

the fertilizer quantity (to boost the soil nutrient status of their marginal lands allotted to

cassava production), herbicide quantity (to curtail the adverse economic effects of weeds and

herbs) and pesticide (to control the major pests and vectors of major endemic diseases of

cassava). Among the above three major inputs, pesticide has the highest coefficient with a

value of 0.3572 (Table 18) in the preferred models (model 2) and therefore, it existed as the

most limiting factor that greatly determine what cassava output would be like among the

female farmers. In the preferred model, stem cuttings, fertilizer quantity and pesticide

quantity had positive signs while farm size, herbicide quantity, hired labour and family

labour had negative signs. The implication was that any increase in quantities of stem

cuttings, fertilizer quantity and pesticide quantity would lead to an increase in cassava output

of the female farms while any increase in farm size, herbicide quantity, hired labour and

family labour would greatly reduce the returns to be realized from the sales of cassava output

among the female farmers as extra costs incurred on these inputs does not translate into better

returns.

Among the pool of the cassava farmers in the study area, the significant variables

include: fertilizer quantity (at 5%), herbicide quantity (at 5%) and pesticide quantity (at 1%).

Other variables like stem cuttings, farm size, hired labour and family labour were not

significant at all the known levels of significance. The implication of the above findings is

that in the study area, regardless of the gender of the cassava, the major limiting factors of

cassava production were fertilizers, herbicide and pesticides. It revealed that regardless of the

farm size, quantity of viable stem cuttings used, hired and family labour employed, cassava

output without the considerations of the above three limiting inputs will dwindle. In the

preferred model (model 2), stem cuttings, fertilizer quantity and pesticide quantity had

positive signs while farm size, herbicide quantity, hired labour and family labour had

negative signs the productive inputs that greatly impact on cassava output of the female

farmers were the fertilizer quantity (to boost the soil nutrient status of their marginal lands

available for cassava production), herbicide quantity (to curtail the adverse economic effects

of weeds and herbs) and pesticide (to control the major pests and vectors of major endemic

diseases of cassava). Among the above three major inputs, pesticide has the highest

coefficient with a value of 0.3405 (Table 18) in the preferred models (model 2) and therefore,

it existed as the most limiting factor that greatly determine what cassava output would be like

among the female farmers. In the preferred model (model 2) stem cuttings, fertilizer quantity

and pesticide quantity had positive sign while herbicide quantity, hired labour and family

labour all carried negative signs in the first model. In the second model, stem cuttings,

fertilizer and pesticide quantity had positive signs while farm size, herbicide quantity, hired

labour and family labour had negative signs. The variables with positive coefficient imply

that any increase in such variables would lead to an increase in cassava output of the pooled

farms.

4.3.2 Goodness of Fit

The estimated sigma square ( ) of each of the male and female cassava farmers was

0.1819 (significant at 1%) and 0.4211(significant at 10%) respectively while for the pooled it

was 0.2613(significant at 1%). The values are large and significantly different from zero

(Tables 17 and 18). This indicates a good fit of the model and the correctness of the

specified distributional assumptions.

4.3.3 The estimated Gamma () Parameter

The estimated gamma () parameter of male, female and pooled cassava farms are 0.99,

0.42 and 0.97 respectively and highly significant at 5% level of significance. This means that

99%, 42% and 97% of the variations in the cassava output among the male, female and

pooled cassava farmers in the study area are due to the differences in their technical

efficiencies. This result is consistent with the findings of Yao and Liu (1998); Seyoum et al.,

(1998); Ajibefun et al., (2002); Ajibefun and Aderinola (2004).

Table 17: Maximum Likelihood Estimates for the Parameters of the Stochastic Frontier

Production Function for Male Cassava Farmers in the Study Area.

Variable Parameter Model 2 T-value

General Model (Production Function)

Constant b0 0.1014 31.099

Stem Cutting b1 0.4086 0.8444

Farm Size b2 -0.3845 -0.7598

Fertilizer Quantity b3 0.9099 1.069

Herbicide Quantity b4 -0.4931 -0.3735

Pesticide Quantity b5 0.3050 3.100*

Hired Labour b6 -0.2327 -3.074*

Family Labour b7 0.5026 1.093

Inefficiency ModelConstant 0 -0.2099 -0.2200

Age of Farmer 1 0.4673 0.1820

Household Size 2 0.1531 0.8588

Year of Farming Experience 3 -0.1045 -1.157

Educational Level 4 0.6583 0.6254

Extension Contributions 5 0.4110 2.403**

Variance ParametersSigma Squared 0.1819 6.282*

Gamma

0.99990.2912

Log Likelihood Function -16.27

21.23

14.07

Notes: * =1% level; ** = 5%; *** = 10% (Figures in parentheses are t- values).

Source: Computed from Field Survey Data, 2006.

Table 18: Maximum Likelihood Estimates for the Parameters of the Stochastic Frontier

Production Function for Female Cassava Farmers in the Study Area

Variable Parameter Model 2 T-value

General Model (Production Function)

Constant b0 0.9899 17.43

Stem Cutting b1 0.1005 0.1221

Farm Size b2 -0.5849 -1.134

Fertilizer Quantity b3 0.3094 2.608*

Herbicide Quantity b4 -0.3545 -2.934*

Pesticide Quantity b5 0.3572 3.488*

Hired Labour b6 -0.6450 -0.5956

Family Labour b7 -0.3374 -0.7062

Inefficiency ModelConstant 0 -0.8588 -0.3872

Age of Farmer 1 -0.1252 -0.2043

Household size 2 0.1266 0.4237

Year of farming experience 3 0.1363 0.8908

Educational status 4 0.2435 0.9877

Extension Contributions 5 0.8674 1.576

Variance Parameters

Sigma Squared 0.4211

1.820***

Gamma 0.9853 0.7247

Log Likelihood Function -22.84

34.54

14.07

Notes: ** = 5% level; *** = 10% level. (Figures in parentheses are t-values)

Source: Computed from Field Survey Data, 2006.

Table 19: Maximum Likelihood Estimates for the Parameters of the Stochastic Frontier

Production Function for Pooled Cassava Farmers in the Study Area.

Variable Parameter Model 2 T-value

General Model (Production Function)

Constant b0 0.9777 0.1999

Stem Cutting b1 0.5927 1.011

Farm sizeb2

-0.2339-0.6099

Fertilizer Quantity b3 0.2586 2.813**

Herbicide Quantity b4 -0.2573 -2.590**

Pesticide Quantity b5 0.3405 4.418*

Hired Labour b6 -0.9349 -0.9560

Family Labour b7 -0.3390 -0.8614

Inefficiency ModelConstant 0 -0.2444 -0.2344

Age of Farmer 1 -0.6046 -0.2133

Household Size 2 0.1089 0.7082

Year of Farming Experience3

-0.4658-0.6170

Educational Level 4 0.15571.358

Extension Contributions 5 0.5568 2.369**

Variance Parameters

Sigma Squared 0.2614 3.325*

Gamma 0.9735 0.4554

Log Likelihood Function -46.3447.79

14.07

Notes: ** = 5% level, *** = 10% level. (Figures in parentheses are t-values).

Source: Computed from Field Survey Data, 2006.

4.4 Inefficiency Model

The estimated parameters of the inefficiency model in the stochastic frontier models of

the male, female and pooled cassava farmers in Oluyole and Akinyele Local Government

Areas of Oyo State are presented in Tables 17 and 18. The analysis of the inefficiency model

shown in Tables 17 and 18 showed that the signs and significance of the estimated

coefficients in the inefficiency model have important policy implications on the technical

efficiency (TE) of the male and female cassava farmers.

Among the male cassava farmers, the coefficients of age, household size, education level

and extension contributions were positive while the years of farming experience was

negative. The findings above revealed that the age, household size, educational level and

extension contribution tend to increase the level of technical inefficiency of the male cassava

farmers while the years of farming experience tend to reduce the level of technical

inefficiency of the male farmers. The above findings were not conformed to a priori

expectation and were incongruent to the findings of Ajibefun and Daramola, 1999; Ojo, 2003

and Seyoum et al., 1998; Obwona, 2000 and Kalirajan, 1981 . The reasons for age, household

size, educational level and extension contributions contributing to the inefficiency level of

the male farmers may include inefficient and inadequate family labour input, lack of proper

supervision of their farms due to other profitable off-farm activities as well as trivialization

of proven extension information on personal grounds.

Among the female cassava farms in the study area, the coefficient of age was negative

thereby conforming to a priori expectation. The coefficients of household size, educational

level, years of farming and extension contacts had positive relationship with the technical

inefficiency of the female farmers and this was against the a priori expectation and as well

incongruent with the findings of Obwona, 2000 and Kalirajan, 1981. The findings revealed

that the age had a negative relationship with their technical inefficiency level and this means

that the younger the female farmers, the less technically inefficient they will be, as such the

more technically efficient they will be. The findings also revealed that years of farming

experience, education level, household size and extension contribution had positive

relationship with the technical inefficiency level of the female cassava farm; these imply that

the larger the household size and the more educated coupled with relevant extension

contributions, the more inefficient the female cassava farmers in the study area will be and

the reasons may be due to inefficient family labour input, lack of proper supervision of their

farms to availability of other lucrative off-farm activities as well as trivialization of extension

information on personal grounds.

The expected signs for these variables are summarized in Table 20 .

Table 20: Expected Signs for Variables Influencing Technical Inefficiency

Variable Parameter Expected Sign

Age δ1 +/-

Household size δ2 -

Farming experience δ3 -

Educational level δ4 -

Extension contact δ5 -

Source: Coelli and Battese, 1996.

4.5.0 Productivity Analysis

The estimated productivity parameters such as elasticities of production and returns to

scale are discussed in this section.

4.5.1 Elasticities (εP) and Returns To Scale (RTS) of Cassava Production of the

Male and Female Farmers in Oluyole and Akinyele Local Government of Oyo State.

The elasticity of production of each input shows the proportional change in the quantity

of output as a result of one percent change (increase) in the quantity of the one input when

other quantities of other inputs are kept constant. Returns to scale show the proportional

change in the quantity of output as a result of one percent change (increase) in the quantities

of all the inputs simultaneously. Table 21 presents the estimated elasticities of production

(εP) and returns to scale (RTS) for all the sampled male and female cassava farmers in the

study areas.

4.5.2 Elasticities of Production (εP)

Among the male cassava farmers, the estimated elasticities of the explanatory variables

of the preferred model (Model 2) show that farm size, herbicide quantity and hired labour

were negative (decreasing) functions to the factors and this showed that the use of an extra

unit of these inputs will bring about a decrease in the cassava output of the male cassava

farmers (i.e. this indicates over-use of such variables). Stem cutting, fertilizer quantity,

pesticide quantity and family labour were positive (increasing) functions to the factors which

indicate that the use and allocation of these variables was profitable and as such a unit

increase in these inputs will eventually result in an increase in the cassava output of the male

farmers.

Among the female cassava farmers, the estimated elasticities of the explanatory variables

of the preferred model (Model 2) show that farm size, herbicide quantity, hired labour and

family labour were negative (decreasing) functions to the factors. This showed that an over-

use of these variables and therefore a unit increase in these inputs will bring a decline in the

cassava output among the female cassava farmers. Stem cutting, fertilizer quantity and

herbicide quantity were positive (increasing) functions to the factors which indicate that the

use and allocation of these variables was profitable and as such a unit increase in these inputs

will eventually result in an increase in the cassava output of the female farmers.

The elasticity of cassava output with respect to fertilizer quantity has the highest value

among the male cassava farmers while hired labour prevailed among the female cassava

farmers. These findings indicated that fertilizer was the most important variable factor of

production among the male cassava farmers in the study area and should be readily available.

Among the female farmers, hired labour existed as the most important factor of production;

hence, there should be wage control scheme in order to enable female farmers maximize its

usage on their farms considering their restricted access to credit facilities for farm activities.

4.5.3 Returns To Scale (RTS)

The analysis of results in Table 21 shows that the RTS for the male and female cassava

farmers were 1.03 and -1.15 in the study areas respectively. Among the male cassava

farmers, there existed increasing returns to scale and they were operating in the irrational

zone of production (stage 1). The female cassava farmers had diminishing returns to scale

and this revealed that they were operating in the stage 3 (a highly irrational zone of

production) with the implication that the resources are not efficiently allocated and used on

their farms.

Table 21: Elasticities (εP) and Returns-to-Scale (RTS) of the Male and Female

Cassava Farmers in Oluyole and Akinyele Local Government Areas of

Oyo State.

ΕP Male Female

Stem Cutting 0.4086 0.1005

Farm Size -0.3845 -0.5849

Fertilizer Quantity 0.9099 0.3094

-0.4931 -0.3545

Pesticide Quantity 0.3050 0.3572

Hired Labour -0.2327 -0.6450

Family Labour 0.5026 -0.3374

RTS 1.03 -1.15 Source: Computed from Field Survey Data, 2006.

4.6 Efficiency Analysis

4.6.1 Technical Efficiency Analysis of Male and Female Cassava Farmers in the

Study Area

The predicted technical efficiency estimates obtained using the estimated stochastic

frontier models for the individual male and female cassava farmers in the study area

presented in Tables 22 to 24.

Tables 22 and 23 show the predicted technical efficiency estimates for the male and

female cassava farmers in the study area. The predicted cassava farm specific technical

efficiency (TE) for the male cassava farmers’ indices ranged from a minimum of 24.88% to a

maximum of 98.60% for the farms, with a mean of 65.98% while for the female cassava

farmers, it ranged from a minimum of 26.56% to a maximum of 96.06% with a mean of

70.28%. Thus, in the short run, an average male and female cassava farmer have the scope of

increasing his/her cassava production by about 34.02% and 29.72% respectively by adopting

the technology and techniques used by the best practiced (most efficient) male and female

cassava farmers. Such male and female cassava farmers could also realize 33.08% and

26.83% cost savings (i.e.1 – [65.98/ 98.60] and1 –[70.28/96.06]) respectively in order to

achieve the TE level of his most efficient counterpart (Bravo-Ureta and Evenson, 1994;

Bravo-Ureta and Pinheiro, 1997). The above findings unfolds the capacity of an average

male and female cassava farmers to increase his/her technical efficiency level to a tune of

34% and 29% respectively and in turn attain a cost-saving status of about 33% and 26% that

the most technically efficient male and female cassava farmer had enjoyed in his/her cassava

production enterprise using the available production techniques and technology in the study

area.

A similar calculation for the most technically inefficient male and female cassava farmer

reveals cost saving of about 74.77% and 72.35% (i.e., 1 – [24.88/98.60] and 1 –

[26.56/96.06] as shown in Table 5.8. The decile range of the frequency distribution of the TE

indicates that about 45.15 % and 57.02% of the male and female cassava farmers had TE of

over 70 % and about 30.65 % and 26.45% had TE ranging between 51 % and 70 %

respectively. The above findings from the analyses of the most technically inefficient male

and female cassava farmer revealed that he/she has an untapped ability to realize a cost-

saving of about 75% and 72% respectively. To realize this latter cost-saving status, the male

and female cassava farmers would have to employ the right amount of the various production

inputs, maximize the use of available technology as well as proper supervision of their

cassava farms to the activities of thieves and intruders on their farms.

Table 22: Decile Range of Frequency Distribution of Technical Efficiencies of the

Male Cassava Farmers in Oluyole and Akinyele Local Government Areas

of Oyo State.

Decile Range (%) Technical EfficiencyNo %

> 90 17 13.70

81 – 90 20 16.13

71 – 80 19 15.32

61 – 70 18 14.52

51 – 60 20 16.13

41- 5031- 40

21- 30 4

17

9

13.70

7.25

3.23Mean % 65.98 %

Minimum % 24.88 %

Maximum % 98.68 %

Source: Computed from Field Survey Data, 2006.

Table 23: Decile Range of Frequency Distribution of Technical Efficiencies of the

Female Cassava Farmers in Oluyole and Akinyele Local Government

Areas of Oyo State.

Decile Range (%) Technical

EfficiencyNo %

> 90 20 16.52

81 – 90 26 21.49

71 – 80 23 19.01

61 – 70 15 12.40

51 – 60 17 14.05

41 – 50 10 8.26

31- 40

21- 30

4

7

3.31

5.79

Mean % 70.28 %

Minimum % 26.56 %

Maximum % 96.06 %Source: Computed from Field Survey Data, 2006.

Table 24: Summary of Cost Savings According to Efficiency Indicator by Male

Cassava Farmers in Oluyole and Akinyele Local Government Areas of Oyo State.

Efficiency Indicator Value of Savings (%)

Most Technically Efficient 34.02

TE Most Technically Inefficient 74.77

Source: Computed from Field Survey Data, 2006.

Table 25: Summary of Cost Savings According to Efficiency Indicator by Female

Cassava Farmers in Oluyole and Akinyele Local Government Areas

of Oyo State.

Efficiency Indicator Value of Savings (%)

Most Technically Efficient 29.72

TE Most Technically Inefficient 72.35

Source: Computed from Field Survey Data, 2006.

4.7.0 Test of Hypotheses

The results from the test conducted on the specified null hypotheses are discussed in

tables below.

4.7.1 Test of Hypothesis for the Absence of Inefficiency Effects

The null hypothesis specifies that the male and female cassava farmers were technically

efficient in their production and that the variation in their output was only due to random

effects. The hypothesis is defined thus: H02: = 0

The generalized likelihood ratio test was conducted and the Chi-square (X2) statistics was

computed. Table 26 shows the results of the generalized likelihood ratio test for the absence

of technical inefficiency effects. The null hypothesis, = 0, was rejected among the male and

female cassava farmers in the study area. This revealed that the technical inefficiency effects

existed among the male and female cassava farmers in the study area and that the variations

in their production processes may be due to certain inefficiency factors in the study area.

Table 26: Test of Hypotheses on Technical Efficiency

H02: Male and Female Cassava farmers are fully technically efficient ( = 0)

L.G.A L (H0) L (Ha) d.f Decision

Male 26.88 16.27 21.23 7 14.07 Reject H0

Female 40.10 22.84 34.54 7 14.07 Reject H0

Pooled 70.24 46.35 47.79 7 14.07 Reject H0

Source: Computed from Field Survey Data, 2006

4.7.2 Test of the Significance of Coefficients of the Socio-Economic Variables of the

Inefficiency Model

The null hypothesis states that each of the estimated coefficients of the explanatory

variables of the inefficiency model of the stochastic frontier production function is not

statistically significant (i.e. socio-economic variables do not have any significant relationship

with TE of the male and female cassava farmers).

The hypothesis is defined thus: H03: i = 0, where i is the individual explanatory

coefficient. The test used was the t-ratio test and was conducted at = 0.05 given a degree of

freedom 122 and 119 for both the male and female cassava farmers respectively. Table 27

showed the results of t-ratio tests for the coefficients of the inefficiency model of the

stochastic frontier production function for the male and female cassava farmers respectively.

It has been seen that among the male cassava farmers that only the extension contributions

was significant and as such the null hypothesis was rejected for only extension contributions

among the other inefficiency variables of the male cassava farmers. Among the female

cassava farmers, none of the inefficiency variable was significantly different from zero,

hence; the null hypothesis was accepted for each of these variables. Therefore, it can be

concluded that the only the production function variables determine TE among the female

cassava farmers while among the male cassava farmers, there exist a significant inefficiency

effect from one of their inefficiency variables in the study area.

Table 27: T-Ratio Test for the Significance of Coefficients of the Socio-Economic

Variables of the Inefficiency Models of the Male and Female Cassava

Farmers.

H03: Socio-Economic variables have no significant relationship on the farmers’ TE (I = 0)

Male Female

Variables Parameter Coefficient T-Ratio T-Critical Decision Coefficient T-Ratio T-Critical Decision

Age of Farmer 1 0.4673 0.1820 1.645 Accept

0

-0.1252 -0.2043 1.645 Accept H0

Household size 2 0.1531 0.8588 1.645 Accept

0

0.1266 0.4237 1.645 Accept H0

Years of Farming

Expexperience 3 -0.1045 -1.157 1.645

Accept

0

0.1363 0.8908 1.645Accept H0

Educational

Level

4 0.6583 0.6254 1.645 Accept

0

0.2435 0.9877 1.645 Accept H0

Extension 5 0.4111 2.403 1.645 Reject

0

0.8675 1.576 1.645 Accept H0

Source: Computed from Field Survey Data, 2006

4.7.3 Test of Hypothesis on the Significant Difference of Mean Technical Efficiency

of the Male and Female Farmers in the Study Area.

The null hypothesis states that the mean TE of the male and female cassava farmers in the

study areas are not different. The hypothesis is defined thus: H04: Ua = Ub

Where Ua and Ub are the population means of TE of the male and female cassava farmers in the

study area respectively. The test used was the test of significance for difference of means for

large samples (n > 30). The results of the test are presented in Table 28. The implication of

rejecting the null hypothesis (i.e. there was no significant difference between the mean technical

efficiencies of male and female cassava farmers in the study area) is that there exist a significant

difference in the mean technical efficiencies of male and female cassava farmers. Therefore, the

means TE of male cassava farmers in the study area are significantly different from the mean of

the TE of the female cassava farmers in the study area.

Table 28: Test of Significant Differences of Mean Technical Efficiencies between the

Male and Female Cassava Farmers in the study area

Item

Male Female t-

computed

t-Critical Decision

No of farms 124 121

Mean TE 0.6599 0.7028 7.66 1.96 Reject H0

Standard deviation 0.1984 0.1928

Source: Computed from Field Survey Data, 2006.

CHAPTER FIVE

5.0 Summary, Conclusions and Recommendation

5.1 Summary of Findings

This research work broadly examined gender and the technical efficiency in cassava

production in Oluyole and Akinyele Local Government Areas of Oyo State, Nigeria. The study in

its specific objectives considered the socio-economic characteristics of the male and female

cassava farmers in the study area; the estimates of the costs and returns to cassava production of

the male and female farmers in the study area; the analyses of the technical efficiency of the male

and female cassava farmers in the study area; the major constraints to cassava production in the

study area. The study employed the use of cross-sectional data from household survey conducted

on a sample of 248 farmers from eight villages in the study areas. The data were collected with the

aid of structured questionnaire and were later analyzed.

The study employed the following analytical tools in order to analyze the data collected from

the field: Descriptive Statistics such as frequency distribution, percentages, mean, standard

deviation were used to describe the socio-economic characteristics of cassava farmers; Budgeting

technique such as the gross margin analysis to determine the profitability of cassava production

among the male and female farmers in the study area; Econometric analytical models such as

stochastic frontier production function analysis which was used to analyze the technical

efficiencies of male and female cassava farmers as well as the determinants of their technical

efficiencies; .The null hypotheses stated were tested by the use of tools such as generalized

likelihood ratio test and t-ratio test.

The mean ages of the male and female cassava farmers in the study area were 50 years

respectively. Many of the male and female cassava farmers had about 12 years of formal education

respectively. The average household size per farming family was 11 persons among the male

cassava farmers and about 10 persons among the female cassava farmers. The mean years of

farming experience were 21years for the male and female cassava farmers respectively. The

average number of years of schooling is 12years for both male and female cassava farmers

respectively. The average farm sizes for the male and female cassava farmers were 3.69 and 3.4

hectares respectively. The average production costs for the male and female cassava farmers were

N 108,000 and N109, 250 respectively in the study area. The mean output of cassava tuber

production was about 9.35t/ha of farmland among the male cassava farmers and 9.33 t/ha of

farmland among the female cassava farmers, the average revenue was about N137,700 among the

male cassava farmers and it was N137,700 among the female cassava farmers in the study.

This research revealed that about 68 % of the male and 78 % female cassava farmers were

married in the study area. Many of the male (65%) and female (55%) cassava farmers in the study

area were actively engaged in cassava farmers respectively. Many of the male (34.4%) cassava

farmers gained access to their land by inheritance while only 14.88% of the female cassava

farmers had land by inheritance. Many of the female (31.40 %) cassava farmers in the study area

had land leased to them either by their husbands or by extended family members of the husband,

28.93% of the female cassava farmers had land given to them as gift mostly from their husbands,

most of which are not as productive as before. majority of the male (62.4%) and female (66.9%)

cassava farmers in the study area had access to extension services respectively. This had a

significant influence on their output and puts them on the same level playing field to be better

producers of cassava.

Most of the male (72%) and female (75%) cassava farmers used NPK fertilizers on their

cassava farms. This suggests that the land on which cassava is cultivated is moderately fertile.

Many of the male (81%) and female (79%) cassava farmers used Gramazone (Herbicide) on their

cassava farms intercropped with other cereal crops. Also, many of the male (71%) and female

(66%) cassava farmers used pesticide on their cassava farms intercropped with other cereal crops.

This suggested that many of the male and female cassava farmers used chemicals on their cassava

farms and the reasons were to improve soil fertility, reduce weed invasion and control insects,

pests and disease attacks on their cassava farms.

Most of the male (52%, 24% and 24%) and female (38%, 25%, and 25%) cassava farmers

obtained their funding from informal sources like Personal savings, Family members and

Relatives/Friends while only 12% of the female cassava farmers financed their cassava production

through Cooperative. This might be an indication of the fact that it is easy to obtain credit from

non-institutional sources than institutional sources. Many of the male (38%) and female (33%)

cassava farmers obtained credit of between N80,000 - N100,000 respectively. Average gross

margin per hectare for farmers in the male and female cassava farms in the study area was about N

29,700 and N28,250 respectively. It can be seen that it is profitable to produce cassava among both

the male and female farmers in the study area.

Among the male cassava farmers, the variables that were significant included pesticide

quantity used (at 1%) and hired labour employed (at 1%).By implication, the above findings

revealed that the major productive inputs that greatly impact on the cassava output of the

male cassava enterprise were the quantity of pesticide used on their farms as well as the

amount of mandays of hired labour employed. The quantity of pesticide used existed as the

most important input that impact on cassava output of the male farmers.

Among the female cassava farmers, the significant variables include: fertilizer quantity

(at 1%), herbicide quantity (at 1%) and pesticide quantity (at 1%). The productive inputs that

greatly impact on cassava output of the female farmers were the fertilizer quantity, herbicide

quantity and pesticide. Pesticide existed as the most limiting factor that greatly determine

what cassava output would be like among the female farmers. In the preferred model for the

female cassava farmers, stem cuttings, fertilizer quantity and pesticide quantity had positive

signs while farm size, herbicide quantity, hired labour and family labour had negative signs.

The estimated sigma square ( ) for the male and female cassava farmers were 0.1819

(significant at 1%) and 0.4211(significant at 10%) respectively while for the pooled it was

0.2613(significant at 1%). The estimated gamma () parameter of male, female and pooled

cassava farms revealed that 99%, 42% and 97% of the variations in the cassava output among

the male, female and pooled cassava farmers in the study area are due to the differences in

their technical efficiencies. This result is consistent with the findings of Yao and Liu (1998);

Seyoum et al., (1998); Ajibefun et al., (2002); Ajibefun and Aderinola (2004).

Among the male cassava farmers, the coefficients of age, household size, education level

and extension contributions were positive while the years of farming experience was

negative. The findings above revealed that the age, household size, educational level and

extension contribution tend to increase the level of technical inefficiency of the male cassava

farmers while the years of farming experience tend to reduce the level of technical

inefficiency of the male farmers.

Among the female cassava farms in the study area, the coefficient of age was negative

thereby conforming to a priori expectation. The coefficients of household size, educational

level, years of farming and extension contacts had positive relationship with the technical

inefficiency of the female farmers and this was against the a priori expectation.

Among the male cassava farmers, the estimated elasticities of the explanatory variables

of the preferred model (Model 2) show that farm size, herbicide quantity and hired labour

were negative (decreasing) functions to the factors and this showed that the use of an extra

unit of these inputs will bring about a decrease in the cassava output of the male cassava

farmers (i.e. this indicates over-use of such variables). Stem cutting, fertilizer quantity,

pesticide quantity and family labour were positive (increasing) functions to the factors which

indicate that the use and allocation of these variables was profitable and as such a unit

increase in these inputs will eventually result in an increase in the cassava output of the male

farmers.

Among the female cassava farmers, the estimated elasticities of the explanatory variables

of the preferred model (Model 2) show that farm size, herbicide quantity, hired labour and

family labour were negative (decreasing) functions to the factors. This showed that an over-

use of these variables and therefore a unit increase in these inputs will bring a decline in the

cassava output among the female cassava farmers. Stem cutting, fertilizer quantity and

herbicide quantity were positive (increasing) functions to the factors which indicate that the

use and allocation of these variables was profitable and as such a unit increase in these inputs

will eventually result in an increase in the cassava output of the female farmers.

The elasticity of cassava output with respect to fertilizer quantity has the highest value

among the male cassava farmers while hired labour prevailed among the female cassava

farmers. These findings indicated that fertilizer was the most important variable factor of

production among the male cassava farmers in the study area and should be readily available.

Among the female farmers, hired labour existed as the most important factor of production;

hence, there should be wage control scheme in order to enable female farmers maximize its

usage on their farms considering their restricted access to credit facilities for farm activities.

The RTS for the male and female cassava farmers were 1.03 and -1.15 in the study areas

respectively. Among the male cassava farmers, there existed increasing returns to scale and

they were operating in the irrational zone of production (stage 1). The female cassava

farmers had diminishing returns to scale and this revealed that they were operating in the

stage 3 (a highly irrational zone of production) with the implication that the resources are not

efficiently allocated and used on their farms.

The predicted cassava farm specific technical efficiency (TE) for the male cassava

farmers’ indices ranged from a minimum of 24.88% to a maximum of 98.60% for the farms,

with a mean of 65.98% while for the female cassava farmers, it ranged from a minimum of

26.56% to a maximum of 96.06% with a mean of 70.28%. The findings here revealed the

capacity of an average male and female cassava farmers to increase his/her technical

efficiency level to a tune of 34% and 29% respectively and in turn attain a cost-saving status

of about 33% and 26% that the most technically efficient male and female cassava farmer

had enjoyed in his/her cassava production enterprise using the available production

techniques and technology in the study area.

A similar calculation for the most technically inefficient male and female cassava farmer

reveals cost saving of about 74.77% and 72.35%. The decile range of the frequency

distribution of the TE indicates that about 45.15 % and 57.02% of the male and female

cassava farmers had TE of over 70 % and about 30.65 % and 26.45% had TE ranging

between 51 % and 70 % respectively. The above findings from the analyses of the most

technically inefficient male and female cassava farmer revealed that he/she has an untapped

ability to realize a cost-saving of about 75% and 72% respectively. To realize this latter cost-

saving status, the male and female cassava farmers would have to employ the right amount of

the various production inputs, maximize the use of available technology as well as proper

supervision of their cassava farms to the activities of thieves and intruders on their farms.

The findings showed the existence of technical inefficiency effects among the male and

female cassava farmers in the study area and that the variations in their production processes

may be due to certain inefficiency factors in the study area. Among the male cassava farmers

that only the extension contributions was significant and as such the null hypothesis was

rejected for only extension contributions among the other inefficiency variables of the male

cassava farmers. Among the female cassava farmers, none of the inefficiency variable was

significantly different from zero, hence; the null hypothesis was accepted for each of these

variables. Therefore, it can be concluded that the only the production function variables

determine TE among the female cassava farmers while among the male cassava farmers,

there exist a significant inefficiency effect from one of their inefficiency variables in the

study area. There exists a significant difference in the mean technical efficiencies of male

and female cassava farmers. Therefore, the means TE of male cassava farmers in the study

area are significantly different from the mean of the TE of the female cassava farmers in the

study area.

5.2 Conclusions

This study has empirically examined gender and technical efficiency of cassava

production in Oluyole and Akinyele Local Government area of Oyo State. The following

conclusions were drawn based on the major findings of this study:

Cassava production was more profitable for the male cassava farmers than for the

female cassava farmers in the study area.

Male and female cassava farmers were not fully technically efficient in the use of

production resources.

In the short run, an average male and female cassava farmer have the scope of

increasing their cassava production by about 34.02% and 29.72% respectively by adopting

the technology and techniques used by the best practiced (most efficient) male and female

cassava farmers. Such male and female cassava farmers could also realize 33.08% and

26.83% cost savings respectively in order to achieve the TE level of his most efficient

counterpart (Bravo-Ureta and Evenson, 1994; Bravo-Ureta and Pinheiro, 1997).

The most technically inefficient male and female cassava farmer revealed cost saving

of about 74.77% and 72.35%

About 45.15 % of the male cassava farmers had TE of over 70 % and about 30.65 %

had TE ranging between 51 % and 70 % while about 57.02 % of the female cassava farmers

had TE of over 70 % and about 26.45 % had TE ranging between 51 % and 70 %.

The analysis of the influence of socio-economic variables on technical efficiencies of

the male and female cassava farmers showed that none of the socio-economic variables had

significant influence on their TE in the study area.

For the male cassava farmers, the variables that significantly affected their technical

efficiencies include stem cutting, farm size, pesticide quantity used and family labour

employed. Stem cuttings, Farm size, Fertilizer quantity and Pesticide quantity carried

positive signs while Herbicide quantity, Hired labour and Family labour carried negative

sign.

For the female cassava farmers, the variables that significantly affect their technical

efficiencies include stem cutting, fertilizer quantity and pesticide quantity. Stem cuttings,

Farm size, Fertilizer quantity, Pesticide quantity and hired labour had positive signs while

Herbicide quantity and Family labour all carried negative sign.

5.4 Policy Implications and Recommendations

The policy implications and recommendations of this study based on the major findings

include:

1. Cassava production in the study area should be manned by young and better-educated

male and female farmers who will be able to adopt the new and improved technologies which

are both labour and cost - saving in nature bearing in mind the goals of maximizing the use of

endowed resources of land, labour, capital and others in the study area.

2. There should be improvement in the farmers’ access to extension services and technical

advisory services with special emphasis placed on the training of farmers for easy adoption of

improved technology such that whatever technology is in place, there will be efficiency of its

usage. The extension services in the study area should be strengthened through the provision of

funds and better ratio of change agents per farm families in the study area.

3. There should be provision of institutional credit to farmers especially the female gender on

timely basis and with easy access to such credit facilities. This measure would allow both the

male and female cassava farmers to purchase inputs like fertilizer, pesticides, herbicides and

modern farm implements and as such expand their initial land area allotted to crop production.

4. New and improved technological innovations will always enhance the productivity of any

farmer, therefore new and improved technological innovations like the use of labour-saving

device (e.g. Tractor) should be developed and farmers should be made to have access to such at

affordable prices.

5.4 Suggestions for Further Studies

Further studies on this research area should investigate the differentials in the technical

efficiency of the farmers based on certain risks inherent and peculiar to their production system.

They should also examine the trend of cassava production for a period of years (Pre-Sap and

Post-Sap Era) in terms of its resource use efficiency and productivity to be able to determine the

technical change and efficiency with respect to cassava. Relative impacts of environmental

factors (Precipitation) on technical efficiency could also be examined. Finally, technical

efficiency differentials among cassava farmers could also be examined through market structure,

conduct and performance.

REFERENCES

ADB (African Development Bank), (1999): African Development Report, 1999, Oxford

University Press, Oxford, UK.

Adegboye, R.O. (1976): “Land Tenure In Some Parts Of West Africa”. Department of

Agricultural Economics and Extension, University of Ibadan, p.2.

Adekanye, T.O. (1983): "Gender Bias in African Agriculture and Sustainable

Development: the case of Nigeria". Paper presented at the International Conference:

Out of Margin, Feminist Perspectives of Economic Theory”. Amsterdam, the

Netherlands, June 1983.

Adekanye, T.O. (1985): “Access of women farmers to land resource”. Nigerian Journal of

Rural Sociology. 4(1): 86.

Adeyemo, S.O. (1991): “Allocation of agricultural inputs to women farmers”. Nigeria

Journal of Rural Sociology 10 (6):3-10.

Adeyeye, V.A. (1986): “Relative Economic Efficiencies in Farm Organisation: a Comparable

Study of Cooperative and Non- cooperative Farms in Oyo and Kwara State,

Nigeria”. Unpublished Ph.D Thesis. Department of Agricultural Economics,

University of Ibadan, Ibadan.

Aishatu, M.S. (2002): “Gender and Sustainable Development”. National Centre for

Economic Management and Administration (NCEMA), Ibadan August 27th, 2002.

Ajao, A.O. Ajetomobi, J.O. and Olarinde, L.O. (2004). “Gender differences in resources

productivity among cassava farmers in Oyo State Nigeria”. Science Focus 8:109- 113

Alderman, Harold, Lawrence Haddad, John Hoddinott and Stephen Vosti (1994):

“Strengthening Agricultural and Natural Resource Policy through Intrahousehold

Analysis: an Introduction.” American Journal of Agricultural Economics 76(5):

1208-12. 

Aigner, D.J., Lovell, C.A.K., Schmidt, P.J., (1977): Formulation and Estimation of

Stochastic Frontier Production Function Models. J. Econometrics 6, 21–37.

Aigner, D.J. and S.F. Chu (1968): “On Measuring the Industry Production Function”.

American Economic Review 58 (4):826-839.

Aigner, D, Lovell, C.A.K. and Schmidt, P. (1977): “Formulation and Estimation of

Stochastic Frontier Production Models”. Journal of Econometrics 6 (1): 21 – 37.

Ajibefun, I.A. and Aderinola, E.A. (2004): “Determinants of Technical Efficiency and

Policy Implications in Traditional Agricultural Production: Empirical Study

of Nigerian Food Crop Farmers”. Final Report presented at the Bi-annual

Research Workshop of the African Economic Research Consortium

(AERC), Nairobi, Kenya, May 29 – June 24, 41 pp.

Ajibefun, I.A. and Daramola, A.G. (1999): “Measurement and Sources of Technical

Inefficiency in Poultry Egg Production in Ondo State, Nigeria”. Journal of Rural

Economics and Development 13 (2): 85 –94.

Ajibefun, I.A.; G.E. Battese and R. Kada (2002): “Technical Efficiency Technological

Change and Productivity of Hired and Family Labour in the Japanese Rice

Industry”. Empirical Economic Letters (1): 21-31.

Ajibefun, I.A; Battese, G.E. and Daramola, A.G. (2002): “Determinants of Technical

Efficiency in Smallholder Food Crop Farming: Application of Stochastic Frontier

Production Function”. Quarterly Journal of International Agriculture 41 (3): 225-

240.

Ajibefun, I.A, G.E. Battese and A.G. Daramola (1996): “Investigation of Factors Influencing

Technical Efficiency of Smallholder croppers in Nigeria”. CEPA Working Papers, No

10/96. Department of Econometrics, University of New England.

Ajibefun, I.A and A.G. Daramola (2003): Efficiency of Micro-Enterprise in the Nigerian

Economy. African Economic Research Consortium Pp 134. Nairobi, Kenya.

Ajibefun, I.A, and O.A. Abdulkadri (1999): “An Investigation of Technical Efficiency of

Farmers Under the National Directorate of Employment in Ondo State, Nigeria”.

Applied Economic Letter, 6,111-114, Routledge, London.

Bailey Dee Von, Basudeb Biswas Subal C. Kumbhakar and B. Kris Schulthies (1987): "An

Analysis of Technical, Allocative, and Scale Inefficiency: The Case of Ecuadorian

Dairy Farms” Western Journal of Agricultural Economics.14 (1): 36 - 37.

Battese, G.E. and Coelli T.J., (1992): ‘’Frontier Production Functions, Technical Efficiency

and Panel Data: With Application to Paddy Farmers in India”. J.Productivity Anal.

3: 153-69.

Battese, G.E. and Corra, G.S. (1977): “Estimation of a Production Function Model with

Application to the Pastoral Zone of Eastern Australia”. Australia Journal of

Agricultural Economics 21: 169-179.

Battese, G.E. and Coelli, T.J. (1988): “Production of Firm Level Technical Efficiencies

with a Generalized Frontier Production Function and Panel Delta”. Journal of

Economics 38: 387-389.

Battese, G.E. (1992): “Frontier Production Function and Technical Efficiency: A Survey of

Empirical Applications in Agricultural Economics”. Agricultural Economics 7:185-

208.

Battese, G.E. and Coelli, T.J. (1995): “A Model for Technical Inefficiency Effects in a

Stochastic Frontier Production Function for Panel Delta”. Empirical Economics

20:325- 335.

Battese, G.E; S.J. Malik and M.A. Gill (1996): “An Investigation of Technical

Inefficiencies of Production of Wheat Farmers in Four Districts of Pakistan”.

Journal of Agricultural Economics 47:37-49.

Battese, G.E and T.J. Coelli (1995): “A model for Technical Inefficiency Effects in

Stochastic Frontier Production Function for Panel Data”. Empirical Economics 20:

325 – 332.

Battese, G.E and H. Sarfaz (1998): “Technical Efficiency of Cotton farmers in Vehari

District of Punjab Pakistan”. CEPA Working Paper No.8/98.Department of

Econometrics, University of New England, Armidale, Australia.

Beke, T.E (2007): “Adoption of New Rice Varieties and Technical Efficiency of Rice

Farmers in Cote D’Ivoire”. African Economic Research Consortium

Collaborative Ph.D Programme, Thesis Proposal.

Belbase, K. and Grabowski, R. (1985): “Technical Efficiency in Nepalese Agriculture”.

Journal of Developing Area 19:515-525.

Ben-Belhassen, B (2000): Measure and Explanation of Technical Efficiency in Missouri

Hog Production. Selected Paper, America Agricultural Economics Association.

Annual Meeting Tampa, Florida, July 30th – August 2nd.

Berger, M.O. (1985): “Gender discrimination against women farmers”. International

Journal of Rural Sociology 15: 89. 8th, Dec 1985.

Berry, S. (1993): “Socio Economic Aspect of Cassava Cultivation and Use: Africa

Implication for the Development of Appropriate Technology”. COSCA working

paper No 8, Collaborative Study of Cassava in Africa IITA, Ibadan. 23:135

Esteve-Volart, (2004): “Gender Discrimination and Growth: Theory and Evidence from India”

The Suntory Centre Suntory and Toyota international Centres for Economics and

Related Disciplines London School of Economics and Political Science Houghton

Street DEDPS 42 London WC2A 2AE.

Bindlish, Vishva, and Robert Evenson (1993): Evaluation of the Performance of T and V

Extension in Kenya. World Bank Technical Paper 208. Washington, D.C. 

Blackden, C. Mark, and Chitra Bhanu, 1999, “Gender, Growth, and Poverty Reduction:

Special Program of Assistance for Africa,” World Bank Technical Paper No. 428,

World Bank

Blalock, H.M. (1972): Social Statistics (2nd Edition), New York: McGraw-Hill Inc. pp

219-235.

Bravo-Ureta, B.E. and Evenson, R.E. (1994): “Efficiency in Agricultural Production:

The Case of Peasant Farms in Eastern Paraguay”. America Journal of Agricultural

Economics 10: 27-37.

Bravo-Ureta, B.E. and Pinheiro A.E. (1997): “Technical, Economic and Allocative

Efficiency in Peasant Farming: Evidence from the Dominican Republic”. The

Developing Economies. America Journal of Agricultural Economics 35(1): 48- 67.

Bravo - Ureta B.E. and Laszlo Rieger (1991) "Dairy Farm Efficiency Measurement

Using Stochastic Frontiers and Neoclassical Duality". American Journal of Agric.

Econs.

Bravo – Ureta, B.E. and Pinheiro A.E. (1997). "Technical, Economic and Allocate Efficiency

in Peasant Farming, Evidence from the Dominican Republic". The Developing

Economies XXXV -1 March pp 48-69.

Cater, S.E (1995): “Introduction and Diffusion of Cassava in Africa”, IITA research Guide

49:32-45

CBN (Central Bank of Nigeria): (1994): Statistical bulletin 1994. pp 25

Charnes, A.,W.W. Cooper and E. Rhodes (1978): “Measuring the Efficiency of Decision

Making Units”. European Journal of Operational Research 2 (6): 429-444.

Chiebowska, G.H. (1990). “The world distribution of women”, International Journal of

Rural Sociology 19(5):68, Dec. 1990.

Christopher Udry, (1996):“Gender, Agricultural Production and the Theory of the Household.”

Journal of Political Economy 104 (5): 1010-46. 

Cloud, K. (1987): "Gender Issues in AIDS Agricultural Projects: How Efficient Are

We? Washington D.C. US Agency for International Development.

Cock, J.H. (1985): Cassava: New Potential For A Neglected Crop. West view Press,

Boulder Colorado, USA.

Coelli, T.J. (1995): “Recent Development in Frontier Modelling and Efficiency

Measurements”. Australian Journal of Agricultural Economics 39 (3): 219-245.

Coelli, T.J. (1996a): “A Guide to Frontier Version 4.1 Computer Program for Stochastic

Frontier Function and Cost Function Estimation”. Unpublished Paper, Department of

Econometrics, University of New England, Armidale, NSW2351, Australia. Pp 32.

Coelli, T.J. and Battese, G.E (1996): “Identification of Factors which Influence the

Technical Inefficiency of Indian Farmers”. Australian Journal of Agricultural

Economics 40 (2): 103-128.

Coelli, T. D.S. Prasada Rao and Battese, G.E. (1998): An Introduction to Efficiency

Measurement and Productivity Analysis. Boston: Kluwer Academic Publishers.

Dawson, P.J; J. Lingard and C.H. Woodford (1991): “A Generalized Measure of Farm

Specific Technical Efficiency”. America Journal of Agricultural Economics, pp

1098-1104.

Debreu, G. (1951): “The Coefficient of Resource Utilization”. Econometrica.19:273 – 292.

Dey Abbas, Jennie. (1997). “Gender Asymmetries in Intrahousehold resource Allocation in

Sub-Saharan Africa: Some Policy Implications for Land and Labor Productivity.”

In Lawrence Haddad, John Hoddinott and Harold Alderman, eds., Intrahousehold

Resource Allocation in Developing Countries:   Models, Methods, and Policy .

Baltimore, Md. And London: The Johns Hopkins University Press. 

D’Situ and K. Bysmouth.(1994): Poverty alleviation through agricultural projects.

EDI Seminar Report 30.

Diewert, W.E. and Parkan (1983): “Linear Programming Tests of Regularity Conditions

For Production Function”. In W.Eishorn, R. Henn, K. Neumann and R.W.

Shephard, eds., Quantitative Studies on Production and Prices. Vienna.

Dostie, B., L. Rabenasola and J. Randriamamonjy (1999): La filiere manioc: Amortisseur

Oublie de vulnerables Antananarir. Institut National de la statisque.

Dykeman, Z.E. (1998): “The sustainability of rural communities in the world”, International

Journal of Rural Sociology 25 (4):15

Ekandem, M.J. (1962): “Cassava in Nigeria”. Federal Department of Agricultural

Research Memo No. 42. Ibadan, Nigeria.

Ezumal, H.C. and Okigbo, B.N. (1980): “Cassava Planning System in Friea”. Proceeding of a

workshop held in Salvao, Balia, Brazil. March (1980), pp 44-49.

Fabiyi, Y.L and R.O. Adegboye (1978): “The Changing Outlook of Young Farmers To

Land Tenure In Nigeria: Implication For Agricultural Development: African

Journal Of Agricultural Sciences V (1): 17-29.

Fabiyi, Y.L and T.I. Omolu (1988): “A Study of The Administrative Structure for the

Implementation of the Land Use decree (ACT) in Bendel State, Nigeria”. Nigeria

Journal of Applied Science, 6(1): 91-103.

FACU, (1993): “Status of Cassava Production, Marketing and Utilization”. Federal

Agricultural Coordinating Unit, Abuja, Nigeria.

Famoriyo, R.B. (1985): “Accessibility of rural women farmers to agricultural inputs”

International Journal of Rural Sociology 22 (3): 22

FAO. 2002. www.fao.org, accessed June 2003.

FAO,(Food and Agricultural Organization), (1996): “Multiple roles of women in Nigeria”,

FAO Annual Report, Dec. 1996, pp15.

FAO, (1995): “Formulation Reports: Roots and Tubers Expansion Programme, FAO,

Rome, Italy, FMANR”. Nigeria Agricultural Statistics, (Time Series Data).

Department of Planning, Research and Statistics, Abuja, Nigeria.

Food and Agriculture Organization (FAO) (2004): Focus on the Issue: “Rice is Life”

Increased Sustainable Rice Production, Key to Global Food Security. Rome.

Farell, R. and C.A.K. Lovell (1978): “Measuring The Technical Efficiency of Production”.

Journal of Econometrics Theory 19:150-162.

Farrell, M.J. (1957): The Measurement of Production Efficiency. J. Royal Stat. Soc.

120 A. 257 - 281.

Farell, M.J (1957): “The Measurement of Productive Efficiency”. Journal of Royal

Statistical Society. Series A (General), Part III, 120:253-290.

Farell, M.J (1957): “The Measurement of Productive Efficiency”. Journal of the Royal

Statistical Society A 120: 253 – 281.

Federal Office of Statistics (FOS), (1996): Annual Abstract of Statistics, Lagos.

Federal Ministry of Agriculture, Water Resources and Rural Development (FMAWR and

RD) (1993): Food Security and Nigerian Agriculture: A National Agenda Table 4,

7, Lagos, Nigeria.

Food and Agriculture Organization (FAO), (1995): Year 1994 Food Balance Sheet for

Nigeria.

Forsund, F.R., C.A.K. Lovell and P. Schmidt (1980): “A Survey of Frontier Production

Function and Their Relationship to Efficiency Measurement”. Journal of

Econometrics, 13:115-122.

FOS (Federal Office of Statistics), (1999): Poverty and agricultural sector in Nigeria.

FOS, Abuja, Nigeria.

FMANR (Federal Ministry of Agriculture and Natural Resources),(2000): Cassava

development in Nigeria: a country case study towards a global strategy for

cassava development. The Global Cassava Development Strategy

Validation Forum. FAO, Rome, Italy and International Fund for Agricultural

Development, Rome, Italy. 88 pp.

FMARD (Federal Ministry of Agriculture and Rural Development), (2002): Crop area

yield survey (CAYS). Projects Coordinating Unit, Federal Ministry of

Agriculture and Rural Development, Abuja, Nigeria.

FMARD (Federal Ministry of Agriculture and Rural Development), (2003): Draft report

of the National Committee on Cassava Production and Export.

Fresco, L. (1986): Cassava in shifting cultivation: A Systems Approach to

Agricultural Technology Development in Africa. Amsterdam, the Netherlands:

Royal tropical Institute.

Fresco, L.O. (1993): “The Dynamics of Cassava in Africa: An outline of Research Issues”.

COSCA working paper No 9. Collaborative Study of Cassava in Africa. IITA,

Ibadan, Nigeria. Pp37.

Gluckman, Max (1945): “African Land Tenure”. Rhodes Livingstone Institute Journal

June 1945.

Greene, W.H (1980): “Maximum Likelihood Estimation of Econometric Frontier Function”.

Journal of Econometrics 13 (1): 27 – 56.

Greene, W.H (1993): The Econometrics Approach to Efficiency Analysis” in Fried.

Haddad, Lawrence, John Hoddinott, and Harold Alderman, eds. 1997. “Intrahousehold

Resource Allocation in Developing Countries: Methods, Models and Policy.

Baltimore: Johns Hopkins University Press for the International Food Policy

Research Institute. 

Haggblade, S. and B. Zulu (2003): Conservation fanning in Zambia. EPTD Discussion

Paper 108. Washington D.C. International Food Policy Research Institute.

Hann, S.K. (1995): “Traditional Processing and Utilization of Cassava in Africa”. IITA

Research Guide. pp41

Hann, S.K (1998): “Resistance Breeding and Tuber Crop”. The International Institute of

Tropical Agriculture (IITA), Ibadan in Crop Protection. June 1998, 8: 147- 168.

Hanoch, G. and Rothchild, (1972): “Testing The Assumption of Production Theory: A

Non-parametric Approach”. Journal of Political Economy, 80:256-75.

Huang, C.J and J.T. Liu (1994): “Estimation of a Non-neutral Stochastic Frontier

Production Function”. Journal of Productivity Analysis 4:171- 180.

Henderson, J.M and Quandt, R.E. (1980): Micro-economic Theory: A Mathematical

Approach (3rd Edition). New York: McGraw-Hill Book Company, pp 64-73.

IFAD, (1994): Multi-state Agricultural Development Project Interim Evaluation Report,

Volume 2. International Fund for Agricultural Development, Rome Italy.

IITA, UI and IFPRI (International Institute of Tropical Agriculture, University of Ibadan,

and International Food Policy Research Institute) (2003): Assessment

of Nigeria’s Agricultural Policy (ANAP), (2003): mitigating constraints to

commercialization and investments in Nigeria’s agriculture. Draft

report (15 May), implemented by IITA in collaboration with UI and IFPRI.

147pp.

Jibowo, A.A. (1994): Essentials of Rural Sociology. Abeokuta, Nigeria, Gbemi Sodapo

Press Limited.

Joan Holmes, (2000): “Women and Ending Hunger: The Global Perspective”, New Delhi,

India

Jondrow, J; Lovell, C.A.K.; Materov, I.S. Schmidt, P. (1982): “On The Estimation of

Technical Inefficiency in the Stochastic Frontier Production Function Model”

Journal of Econometrics 19: 233 – 238.

Jones, W.O. (1959): Manioc in Africa. Food Research Institute. Stanford, C.A. USA:

Stanford University Press.

Kalirajan, K.P. and Shand, R.T. (1986): “Estimating Location Specific and Firm-Specific

Technical Efficiency: An Analysis of Malaysian Agriculture”. Journal of

Economic Development 11:147-160.

Kalirajan, K.P. and Shand, R.T. (1985): “Types of Education and Agricultural

Productivity” A Quantitative Analysis of Tamil Nadu Rice Farming” Journal of

Development Studies 21:232-243.

Kalirajan, K (1981a): “The Economic Efficiency of Farmers Growing High Yielding,

Irrigated Rice in Indian”. American Journal of Agricultural Economics 63(3):566 –

569.

Kalirajan, K (1981b): “An Econometric Analysis of Yield Variability in Paddy Production”.

Canadian Journal of Agricultural Economics 29: 283-294.

Kalirajan, K. and J.C. Flinn (1983): “Measurement of Farm Specific Technical Efficiency”.

Pakistan Journal of Applied Economics 2: 167-180.

Karl, M.B. (1983):”Effects of tenural system on women farmers,” International Journal of

Rural Sociology 12(154):34 Dec. 1983.

Klasen, S.(2000). “Does Gender Inequality Reduce Growth and Development? Evidence from

Cross-Country Regressions.” Background paper for Engendering Development:

Through Gender Equality in Rights, Resources and Voice. World Bank. Washington,

D.C. 

Kline, R.B. (1994): “Rural community development. A Global Issues”, International

Journal of Rural Sociology 22 (8):16.

Koopmas, T.C (1951): An Analysis of Production as an Efficient Combination of

Activities. In T.C Koopmas, (ed); Activity Analysis of Production and Allocation,

Cowles Commission for Research in Economics, No. 13 Monograph, Wiley, New

York.

Kormawa, P.M. and G. Asumugha (2003): Gender impacts of small farmers’

commercialization in Nigeria: case studies of cassava, ginger, and fresh

milk enterprises. Report submitted to the FAO-RAF, Management,

Marketing and Rural Finance Service (AGSP), Gender and Development

Service (SDWW).

Kumbhaker, S.C. (1997). “Modelling Allocative Inefficiency in a Translog Cost Function

and Cost Share Equations - An Exact Relationship’’. Journal of Econometrics

76: 351 - 356.

Kumbhakar, S.C., S. Ghosh and Mc Guckin (1991): “A Generalized Production Frontier

Approach for Estimating Determinants of Inefficiency in US. Dairy Farms”. Journal

of Business and Economic Statistics 9: 279 – 286.

Lyubov, A.K. and H.H. Jensen (1998): “Technical Efficiency of Grain Production in

Ukraine”. Centre for Agricultural and Rural Development. Iowa State University,

Ames, Iowa.

Meeusen, W and Van den Broeck (1977): “Efficiency Estimates from Cobb-Douglas

Production Function with Composed Errors”. International Economics Review

18 (2): 435 – 444.

Meller (1976): “Efficiency Frontier for Industrial Establishment of Different Sizes”.

Exploration in Economic Research Occasional paper of the National Bureau of

Economics Research.

Mjindadi, N. and Norman, D.W. (1982): “Determinants of Technical Efficiency on

Traditional Farms in the Zaria Area of Northern Nigeria” Nigeria Journal of

Agricultural Economics 1 (1): 41-48.

Mortimore, M and Y.L. Fabiyi (2003): “Competitiveness and Growth - The Impact of

Land Policy. Department for International Development in Nigeria, Land Policy

2.

Naved, R.T. (2000): “Intra-household Impact of the Transfer of Modern Agricultural

Technology: A Gender Perspective.” Food Consumption and Nutrition Division

Discussion Paper no. 85. International Food Policy research Institute. Washington, D.C. 

National Centre for Economic Management and Administration, (2002): Workshop on

Gender and Sustainable Development. Ibadan, August 26-30.pp168.

Nigerian National Report, (2006): A report presented at International Conference on

Agrarian Reform and Rural Development. Porto Alegre, 7 – 10th March

NPC (National Population Commission), (1998): Statistical Bulletin 1998, pp28

Nwaru, J.C. (2003): “Gender and Relative Production Efficiency in Food Crop Farming in

Abia State of Nigeria”. Nigerian Agricultural Journal 34:1-10.

Nweke, F.I., D.S.C. Spencer, and J.K. Lynam (2002): The cassava transformation:

Africa’s best kept secret. Michigan State University, East Lansing,

Michigan. 273 pp.

Nweke, F.I, B.O. Ugwu, A. and Dixon, (1996): “Spread and Performance of improved

Cassava Varieties in Nigeria”. COSCA working paper No 15. Collaborative Study

of Cassava in Africa, IITA, Ibadan, Nigeria.

Nweke, F.I, B.O. Ugwu, G.O. Dixon, C.L.A Asadu and Ajobo,O. (1997): “Cassava

Production in Nigeria: A Function of Farmers’ access to market and to improved

production and processing technologies”, COSCA working paper No 21.

Collaborative Study of Cassava in Africa. IITA Ibadan, Nigeria.

Nweke, F. (2004): New challenges in the cassava transformation in Nigeria and Ghana.

EPTD Discussion Paper No.l 18 International Food Policy Research Institute,

Washington, D. C. USA.

Obwona, M. (2000): “Determinants of Technical Efficiency among Small and Medium

Scale Farmers in Uganda: A Case Study of Tobacco Grower”. A Final Report

Presented at AERC Bi-annual Workshop at Nairobi, Kenya.

Odurukwe, S.O. and Anuebunwa, F.O. (1997). “Women cassava producers and processors

in Amoba, Arochukwu: Report of a Survey”. National Root Crops Research

Insitute, Umudike, Umuahia, Nigeria.

Ogundele, O.O (2003): “Technology Differentials and Resource-use Efficiency in rice

production in Kaduna State, Nigeria. Unpublished Ph.D Thesis. Department of

Agricultural Economics, University of Ibadan, Nigeria.

Ojo, S.O (2003): “Productivity and Technical Efficiency of Poultry Egg Production in

Nigeria”. International Journal of Poultry Science 2 (6): 459 – 464.

Okuneye, P.A. and Okuneye, M.Y. (1988): “Under Employment and Increased Labour

Productivity: A Linear Programming Analysis of Family and Community Farms in

Nigeria” in Y.L. Fabiyi et. al., (eds). Rural Employment Generation in Nigeria

NAAE, Ile-Ife. p. 203-213.

Omoregbe, F.K. (1995): Communication of improved farm practices to rural women

contact- farmers in Benue State, Nigeria M.Sc. Thesis, O.A.U, Ile-Ife.

Olagoke, M.A. (1991):”Efficiency of Resource –Use in Rice Production Systems in

Anambra State, Nigeria” in Doss, C.R. and C. olso (eds). Issues in African Rural

Development. Winrock International Institute for Agriculture Development, USA

pp 319-342.

Olawoye, J.E. (1994): “Situation Analysis of Nigeria Women and the Female Child:

Agricultural Production and Rural Productivity”: Sector Report for the WORDOC

Co-ordinated Study for UNICEF.

Olawoye, J.E. (1996): “Empowerment for Rural Women” in Women‘s Empowerment and

Reproductive Health. Social Sciences and Reproductive Health Network, Ibadan.

Olayemi, J.K. (1980): “Food Crop Production by Small Farmers in Nigeria” in Olayide, S.O.

et. al., eds. Nigeria Small Farmers: Problems and Prospects in Integrated Rural

Development. CARD; Ibadan p 17-33.

Olayemi, J.K. and Dittoh, (1995): “Macroeconomic Policies and The Agricultural

Sector”. In Macroeconomic Policies Issues in an Open Developing Economy: A Case

Study of Nigeria. Iwayem A. (Ed) Ibadan; NCEMA.

Olayide, S.O.: and Heady, E.O. (1982): Introduction to Agricultural Production Economics.

Ibadan: University of Ibadan Press, Nigeria, chapters 2, 5 and 7.

Olowofeso, O.E. and Ajibefun I.A. (1999): “The Maximum Likelihood Estimation of

The Stochastic Frontier Production Functions with Technical Efficiency Using

Time Series Data”. Journal of Science Engineering Technology 6 (1): 1527-1536.

Oloyo, R.A. (2001): Fundamental of Research Methodology for Social and Applied

Sciences . Ilaro: ROA Educational Press: p 89-93.

Oluwasola, S.O. (1998). “Effect of land tenure system on women farmers’ productivity.”

International Journal of Rural Sociology 26 (6):32

Onyenweaku, C.E. (1987) “Resource Productivity and Efficiency in Food Production

Among Settlers of Ulonna North Farm Settlement, Umuahia, Imo State” A Paper

Presented at the 4th Annual Conference of Farm Management Association of

Nigeria FUT, Owerri.

Onyenweaku, C.E. and Nwaru, J.C. (2005): Application of a Stochastic Frontier Production

to the Measurement of Technical Efficiency in Food Crop Production in Imo State,

Nigeria. Nigeria Agricultural Journal 36:1-12.

Panin, Anthony, and Bernhard Brummer (2000): “Gender Differentials in Resources

Ownership and Crop Productivity of Smallholder Farmers in Africa: A Case

Study.” Quarterly Journal of International Agriculture 39 (1) 93-107.  

Parikh, A., F. Ali and M.K. Shah (1994): “Measurement of Economic Efficiency in

Pakistani Agriculture”. American Journal of Agricultural Economics 77:675 – 685.

Parsons, K.H. (1977): “The Political Economy of Agricultural Development”. A Paper

Presented to the VI, National Congress of Colombian Economists, Coli,

Colombia, Dec 6-9, 1977,p.6.

Penn, R.J: “Understanding The Pressures For Land Reform”, Economic Development in

South America, LTC Reprint Series No.7, (Madison: Land Tenure Centre,

University Of Wisconsin).

Pitt, Mark M., and Shahidur R. Khandker (1995):“The Impact of Group-Based Credit

Programs on Poor Households in Bangladesh: Does the Gender of the Participants

Matter?”. Journal of Political Economy 106: 958-96. 

Quisumbing, Agnes R., and John Maluccio. 2000. “Intrahousehold Allocation and Gender

Relations: New Empirical Evidence.” Background paper for Engendering

Development: Through Gender Equality in Rights, Resources and Voice. World

Bank, Washington, D.C. 

Quisumbing, Agnes R., L.R. Brown, L. Haddad and R. Meinzen-Dick. (1996):“The

Importance of Gender Issues for Environmentally and Socially Sustainable Rural

Development.” In Ernst Lutz (with the assistance of H. Binswanger, P. Hazell and

A. McCalla) ed., Agriculture and the Environment: Perspectives on Sustainable

Rural Development. World Bank. Washington, D.C. 

Ramsey, B.E. (1995): “The revitalization of rural communities”. International Journal of

Rural Sociology 23 (6):16

Reifschneider, D and R. Stevenson (1999): “Systematic Departures from the Frontier: A

Framework for the Analysis of Firm Inefficiency”. International Economic Review

15:715 – 723.

Richmond, J. (1974): Estimating the Efficiency of Production. International Economic

Review. 15: 515 – 521.

RMRDC (Raw Materials Research and Development Council) (1997): Raw material

sourcing for manufacturing in Nigeria, 3rd edition. RMRDC, Abuja,

Nigeria.

Schmidt, S.S., Forsund, F.R., C.A.K. Lovell (1979): “A Survey of Frontier Production

function and their Relationship to Efficiency Measurement” Journal of

Econometrics 13:15-25.

Schmidt, P. and C.A.K Lovell (1979): “Estimating technical and Allocative Inefficiency

Relative to Stochastic Production and Cost Frontiers”. Journal of Econometrics 9: 343-366.

Schmidt, P. and Lovell, C.A.K. (1979): “Estimating Technical and Allocative Inefficiency

Relative to Stochastic Production and Cost Frontiers”. Journal of Econometrics 9: 343-

346.

Seyoum, E.T.; Battese, G.E. and Flemming, E.M. (1998): “Technical Efficiency and

Productivity of Maize Producers in Eastern Ethiopia: A Study of Farmers within and

Outside the Sasakawa-Global 2000 Project”. Agricultural Economics 19: 341 – 348.

Shankya, P.B. and Flinn, J.C. (1985): “Adoption of Modern Varieties and Their Implications

for Development Policies”. Journal of Development Studies 19: 179 – 190.

Shapiro, K.H. (1983):“Efficiency Differentials in Peasant Agriculture and Their Implications

for Development Policies”. Journal of Development Studies 19: 179 – 190.

Singh, S.; E. Flemming and Coelli, T. (2000): “Efficiency and Productivity Analysis of

Cooperative Dairy Plants in Haryana and Punjab States of India”. Working Paper

Series No 2 (2000) in Agricultural and Resource Economics. Graduate School of

Agricultural and Resource Economics, University of New England, Australia.

Spencer, D.S.C. and Associates. (1997): Cassava in Africa: past, present, and future.

Consultancy report prepared for IITA, for the Global Cassava

Development Strategy Validation Forum. FAO, Rome, Italy. Dunstan

Spencer and Associates. Freetown, Sierra Leone /International

Fund for Agricultural Development, Rome, Italy. 50 pp.

Stewart, R.O. (1977): “Analysis of women’s output of crops: A Global Issues.” Journal of

Agricultural and Resource Economics 11:1-20.

Tadesse, B. and S. Krishna moorthy (1997). ‘’Technical Efficiency in Paddy Farms of

Tamil Nadu: An Analysis Based on Farm Size and Ecological Zone’’.Agricultural

Economics. 16:185 - 192.

Timmer, C.P. (1971) “Using a Probabilistic Frontier Production to Measure Technical

Efficiency”. Journal of Political Economics 79:776-794.

Udele, G.O. (1981): "The Rural women in Agricultural Marketing (Case Study of Cassava

in Isoko Local Government Area, Bendel State, Nigeria)’’ Unpublished Master of

Science Degree Thesis, Submitted to Dept. of Agric. Econs. University of Ibadan,

Ibadan.

Udry, Christopher, John Hoddinott, Harold Alderman and Lawrence Haddad (1995):

“Gender Differentials in Farm Productivity: Implications for Household Efficiency

and Agricultural Policy.” Food Policy 20 (5): 407-23. 

Ugwu, B.O. (1996): “Increasing Cassava Production in Nigeria and Prospects For Sustaining

The Trend”. Outlook on Agro-culture 25(3): 179-185.

World Bank (2000): Attacking poverty. World Development Report 2000/2001.

World Bank Report, (1989): “The role of women in agriculture: A case study of Sub-

Saharan Africa”, World Bank Discussion Paper No 114, pp 1-5.

World Bank (1995): African Development Indicators, Washington D.C.: World Bank.

Xu, X. and Jeffrey, S.R. (1998): “Efficiency and Technical Progress in Traditional and

Modern Agriculture” Evidence from Rice Production in China. Agricultural

Economics 18:157-165.

Yao, S. and Liu, Z. (1998): “Determinants of Grain Production and Technical Efficiency

in China”. Journal of Agricultural Economics 49 (2): 171 – 184.

Yotopoulous, P.A. and Nuggent, J.B. (1976): Economics of Development: Empirical

Investigations .New York: Harper and Raw.

LADOKE AKINTOLA UNIVERSITY OF TECHNOLOGY, OGBOMOSO

FACULTY OF AGRICULTURAL SCIENCES, DEPARTMENT OF AGRICULTURAL ECONOMICS AND EXTENSION

QUESTIONNAIRE PREPARED FOR AN M.TECH RESEARCH PROJECT ON GENDER AND TECHNICAL

EFFICIENCY AMONG CASSAVA FARMERS IN OLUYOLE AND AKINYELE LOCAL GOVERNMENT AREAS

OF OYO STATE.

A. PERSONAL INFORMATION

1. Name…………………………………

2. Age……

3. Sex of respondent (a) Male ( ) (b) Female ( )

4. L.G.A /Name of Village…………………

5. Marital status: Married ( ) Single ( ) Widow ( )

6. If married, how many wives have you?…………

7. Do you wives/husband work in the farm? (a)Yes ( ) (b) No ( )

8. If yes, which of these works do they do?

9. Number of children………

10. Number of dependents…………..

11. Sex of dependents: (a) Male ( ) (b) Female ( )

12. What is your main occupation?……………

13. Do you have any other occupation apart from this? (a) Yes ( ) (b) No ( )

14. If yes, specify? ………………

15. How long have you been in this farming business?………….years

16. Level of education : Primary ( ) Post primary ( ) NCE ( ) ND ( ) HND ( ) University ( )

B. INFORMATION ON INPUTS USED

Land ownership and use

16. How did you obtain your land? (a) Family land ( ) (b) Lease arrangement ( ) (c) Purchases ( )

(d) Gift ( ) (e) Others (specify)

17 What is your total farm size?……………

18 How many hectares did you use to cultivate cassava in 2006 cropping season?………………

19 Planting material

Variety of cassava Quantity of Source Qty of stem from Price Transportation N Total cost N

stem bought previous harvest /unit

N

TMS-30572

TMS-30555

TMS-30001

Local Variety

20. What difficulties did you face in obtaining plating materials?

i…………………………………………….ii……………………………………

iii……………………………………iv……………………………………

21. Fertilizer usage

Type Qty used (kg, bag) Sources Cost/unit N Transportation

N

Totalcost

N

22. What difficulties did you encounter in obtaining fertilizer?

i……………………………………….ii……………………………………

iii……………………………………..iv……………………………………

23. Did you use herbicide to control weeds? (a) Yes ( ) (b) No ( )

24. If yes, complete the following table:

Herbicide Type Qty used/Ha Source Cost/unit N Transportation N Total cost N

Ransteal

Weedoff

Atrazine

Gramozone

2,4-D

25. What difficulties did you face in obtain herbicide?

i…………………………………………..ii………………………………………

iii…………………………………………..iv……………………………………

26. Give the quantity and cost of the following cultivating tools and implement you

bought.

Type of tool bought Quantity Price per unit when bought N Total cost N

Hoe

Cutlass

Basket

Rake

Others (specify)

27. What difficulties did you face in obtaining the farming tools?

i……………………………………..ii………………………………………..

iii…………………………………….iv……………………………………….

28. Did you hire any machinery (tractor and etc) for your operations in years 2006? (a) Yes ( )

(b) No ( )

29. If yes, kindly give the following details.

30. Where did you obtain the machinery?

(a) Local ADP’s office ( ) (b) government tractor hire services ( )

(c) Private tractor hires service ( )

31. What difficulties did you encounter in obtaining the machinery? (a) Too costly ( )

(b) Not available on time ( )

32. What was your source of finance or capital foe cassava production in 2006 farming season?

Source Amount obtained (N) Interest rate (%) Duration of

loan (mths)

Satisfactory

Yes /No

(i) Personal savings

(ii) Family inheritance

(iii)Thrift and credit societies

(iv) Friends / relatives

(v) Cooperative society

(vi)Agric credit cooperation

(vii) NACB

(viii) Commercial bank

(ix) Money lender

(x) Others (specify)

33. (i) How may times did the extension agents visitor you on your farm last season?

…………………………………………………………………………

(ii) Has the visit improved your production? (a) Yes ( ) (b) No ( )

34. (i) Did you receive any other technical assistance from other

sources apart from the extension agents? (a) Yes ( ) (b) No ( )

(ii) If yes, please list the technical assistance and the source

Technical assistance Source (s)

I

Ii

Iii

Iv

35. Please give the following details on innovation adopted by you

Innovations Those already adopted Period before adoption Reason if not adopted

i. Improved variety

ii. Fertilizer

iii. Mechanization

iv. Agro-chemicals

v. New storage and processing device

36. Labour utilization

Please give the following details on the hired labour employed by you farm in 2006

farming season.

Operation Children Adult female Adult male Cost/day N Total cost N

Land preparation

Ridging

Planting

Weeding

Herbicide application

Harvesting

Transportation

37. What difficulties did you encounter in getting hired labour?

i……………………………………..ii……………………………………………

iii…………………………………….iv…………………………………………...

38. Did you use any family labour? (a)Yes ( ) (b) No ( )

39. If yes, please give the following details on the family labour employed b you on

your farm 2006 in farming

Operation Children Adult female Adult male Cost/day N Total cost N

Land preparation

Ridging

Planting

Weeding

Herbicide application

Harvesting

Transportation s

C. INFORMATION ON CASSAVA OUTPUT, SALES AND REVENUE FOR 2006 PRODUCTION

SEASON.

40. Please provide the following information on the amount of commodities produced

on your farm in the year 2006.

Commodities Hectare Qty produced (tubers) Estimated production

value N

Qty (tubers) Estimated value N Total returns

N

A

B

C

D

E

41. What quantity of cassava tuber did you consume or give out from the total production?

Item Unit of measure (tubers) Total unit Price / per unit N Total price N

Consume by family

Given to friends /relative

42. What was the portion of cassava tubers sold to the following categories of buyers?

Category Unit of quantity sold

(tubers basket)

Price per unit of

sale N

Total amount N Total cost of

transportation N

Wholesalers

Middle men

Retailers

Industries

Direct consumers

Others

43. What difficulties did you face in selling your cassava output?

i…………………………………………..ii……………………………………….

iii………………………………………….iv……………………………………...

44. Do you sell your cassava stems? (a) Yes ( ) (b) No ( )

45. If yes, how much do you sell a stem of cassava? N……………………..

46. Do you belong to any cassava farmers association? (a)Yes ( ) (b) No ( )

47. If yes, give the name of the association……………………………………

48. What are the benefits you are deriving from this association?

i………………………………………….ii…………………………………….

iii………………………………………..iv………………………………………..

49. What are the problems you face in the production of cassava crops?

i…………………………………………ii………………………………

iii……………………

50. Kindly mention the uses of cassava crop to man and animals

i……………………………………………..ii…………………………………….

iii…………………………………………….iv………………………………….....

Thanks for your cooperation and sparing your time to answer the above questions.