wole adeleke m.tech thesis
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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.
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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.