TECHNICAL CHANGE AND THE SMALL FARMER
IN HAUSALAND, NORTHERN NIGERIA*
David W. Norman**
David H. Pryor***
Christopher J. N. Gibbs****
*Published under an Agency for International Development contract
with Michigan State University, "Agricultural Economics Services--Sahel,"
**David W. Norman is Professor of Economics, Kansas State University,
***David H. Pryor is Assistant Research Fellow, Institute for Agricultural
Research, Ahmadu Bello University, Zaria, Nigeria.
****Christopher J. N. Gibbs is Program Officer/Project Specialist
with the Ford Foundation, Manila, Philippines.
TABLE OF CONTENTS
1. INTRODUCTION . . . . . . 1
1.1 Background . . . . . 1
1.2 The Scope of the Paper . . . . 4
1.3 The Farming System . . .... .. .. 5
1.3.1 Technical Factors . . . 5
1.3.2 Human Factors . . . . 6
1.4 Description of Hausaland . . . 8
1.4.1 The Technical Environment . . 9
1.4.2 The Human Environment . . .. 13
2. METHODOLOGY FOR VILLAGE STUDIES . . . 15
2.1 Selection of Areas and Villages . . ... .15
2.2 Selection of Farm Families . . ... 19
2.3 Strategy for Data Collection . . .. 19
2.4 Layout of Paper . . .... . . 21
3. DESCRIPTIVE PROFILE OF TRADITIONAL FARMING IN HAUSALAND 24
3.1 Land . . . . . . 24
3.2 Labor . . . . . . .
3.2.1 Family Size and Organization . . .. 29
3.2.2 Types of Work on the Family Farm . .. 32
3.2.3 Work by Male Adults . . . .. 36
3.2.4 Seasonality . . . ... 37
3.2.5 Labor Bottleneck . . . .. 43
3.2.6 Underemployment of Family Labor . .. 47
3.3 Capital . . . . . . 48
3.4 Cash Production Costs . . . .. 52
3.5 Land and Labor Relationships . . ... 53
3.6 Cropping Systems . . .. . 54
3.6.1 Crops Grown . . . . .. 54
3.6.2 Mixed Cropping . . . ... 56
3.6.3 Justification of Mixed Cropping . .. 59
3.7 Income . . . . . . 64
3.7.1 Introduction . . . . .. 64
3.7.2 Production Function Analysis and Net Farm
Income . . . . . 67
3.7.3 Market Orientation . . . .. 70
3.7.4 Income Distribution . . . .. 72
4. COMPARATIVE ANALYSIS OF TRADITIONAL FARMING IN THE SOKOTO,
ZARIA, AND BAUCHI AREAS OF HAUSALAND. . . ... 74
4.1 Effect of Population Density on Farming . . 74
4.2 Effect of Climate on Farming . . .. 75
4.3 Self-Sufficiency and Incomes Among Areas . .. 79
4.4 Influence of Fadama Land . . . .. 81
4.5 Influence of Cattle Ownership . . .. 82
4.6 Changing Family Structure . . . .. 83
4.7 Influence of Access to Urban Areas . . .. 85
5. ANALYSIS OF IMPROVED TECHNOLOGY PACKAGES IN DAUDAWA
VILLAGE IN THE ZARIA AREA . . . ... 86
5.1 Introduction . . . . . 86
5.2 Testing Improved Technological Packages . .. 89
5.2.1 Compatibility with the Technical Element 91
5.3 Compatibility with Endogenous Factors . .. 93
5.3.1 Return Per Unit of Land . . .. 94
5.3.2 Return Per Unit of Labor . . .. 94
5.3.3 Dependability of Return . . .. 101
5.4 Compatibility with Exogenous Factors . .. 102
6. SUMMARY AND IMPLICATIONS . . . .. 103
6.1 Summary . . . . . . 103
6.1.1 Trends Over Time . . . .. 103
6.1.2 Increasing the Relevancy of the Improved
Technological Packages . . .. 104
6.1.3 Two Special Research Challenges . .. 107
6.2 Implications for Policy Makers in Northern Nigeria
and in Sahelian Countries . . .. 109
6.3 Conclusions. . . . . .. 112
REFERENCES CITED. . . . . . .. 114
APPENDIX. . . . . . . .. 123
LIST OF TABLES
1.1 Characteristics of the Three Study Areas . ... 11
1.2 Climate in the Study Areas . . . ... .12
2.1 Characteristics of Villages Included in the Surveys
in the Three Study Areas . . . ... .17
2.2 Improved Technical Packages Introduced in Daudawa
Village, 1971-74. . . . . 22
3.1 Characteristics of Land Tenure in the Three Study areas 26
3.2 Characteristics of Farm Families in the Three Study Areas 31
3.3 Relationship Between Time Worked by All Family Members
and By Family Male Adults on the Family Farm, and
Other Variables .. . .. . . 35
3.4 Time Worked by Family Male Adults by Study Area and
Overall Average . . . . . 38
3.5 Indicators of Seasonality of Work by Study Area and
Overall Average . . . . .. 42
3.6 Number of Livestock by Type and Value of Farm Capital
by Study Area and Overall Average . . .. .49
3.7 Cost of Using Capital in Crop Production and Cash Costs
per Family by Study Area and Overall Average .... 51
3.8 Type and Acreage of Crops and Crop Enterprises: By
Study Area and Overall Average . . ... 58
3.9 Comparison of Sole and Mixed Crops on Gona Land by
Study Area . . .. . .. ..- 60
3.10 Value of Production From Sole and Mixed Crops Grown
on Gona Land by Study Area. .. .. . .61
3.11 Inputs and Farm Income by Study Area and Overall Average 65
3.12 Production Function for Value of Production Derived
from Crop Production in All Study Areas . .. 66
3.13 Determinants of the Return per Cultivated Acre and per
Man-hour from Crop Production . . .. 69
3.14 Estimates of Self-Sufficiency and Percentage of Cereals
Marketed by Study Area and Overall Average . ... 71
4.1 Comparisons Between Two Mixed Cropping Enterprises in
the Sokoto and Zaria Areas . . . ... 77
5.1 Weather Conditions in Daudawa Village, 1973-74 . .. 90
5.2 Variability in Returns from Sole-crop Enterprises Using
Oxen, Daudawa Village, 1973-74 . . ... 92
5.3 Average Costs and Returns on Sole-crop Enterprises,
Daudawa Village, 1973-74 . . . ... 95
5.4 Change in Labor Requirements and Net Returns from Adopting
the Improved Technological Packages, Daudawa, 1973-74 .. 98
5.5 Change in Labor Requirements and Net Returns from Using
Oxen instead of Hand Power, Daudawa, 1973-74 . .. 100
A.1 Gini Coefficient on Distribution of Land by Village . 123
A.2 Coefficients of Variation for Labor Inputs per Month by
Village . . . . .. . 124
A.3 Correlation Coefficients on the Monthly Distribution of
Work by Village . . . . ... .. .125
A.4 Relationship Between Work per Cultivated Hectare and Number
of Cultivated Hectares. . . . .126
A.5 Gini Coefficients on Income Measures . . ... 127
LIST OF FIGURES
3.2 Seasonal Indices of Labor for the Sokoto Study Area 40
3.3 Composition of Work on the Family Farm by Month and
Operation, Sokoto Study Area . . .. 44
3.4 Relationship between Labor Input per Cultivated Hectare
and Number of Cultivated Hectares . ... 55
LIST OF MAPS
2.1 Land Use Surrounding Gidan Karma Village, Kware District,
Sokoto Province ...................... 18
3.1 Gidan Karma Village, Kware District, Sokoto Province,
Field Boundaries with Serial Numbers. . . ... 27
We wish to express our gratitude to all individuals who, over
the years, have contributed directly or indirectly to the studies
reported in this paper. Considerable administrative and financial
support has been provided by the Institute for Agricultural Research,
Ahmadu Bello University, the Ministries of Natural Resources in the
northern States, the local authorities in Sokoto, Zaria, and Bauchi,
and the Ford Foundation. With reference to individuals, special
thanks are due to colleagues in the Rural Economy Research Unit
and later in the Department of Agricultural Economics and Rural
Sociology. Senior staff colleagues who directly or indirectly helped
in the studies included: G. O. I. Abalu, O. Aligbe, P. Beeden,
B. J. Buntjer, N. Ejiga, E. Etuk, J. Fine, A. D. Goddard, J. Hayward,
H. R. Hallam, H. Hays, W. Kroeker, and E. Simmons. In addition, many
junior staff members helped with the various studies. Their work
was invaluable and included helping collect the primary data and
analyzing it and typing the drafts to the various studies. Among
the many who could be listed are the following: Sabo Abubakar, Mary
Adebija, Julius Adeoye, Hamza Akwanga, Joseph Alabi, John Asaka,
Craig Jagger, Danladi Jarma, Grace Michael, Rukayat Ojelade, Jeremiah
Oji, John Oji, Muili Oladejo, Marion Oladimeji, Stanley Onwuchekwe,
Isa Sada, Adamu Umaru, Jack Weaver, and Adamo Yaro. Finally, a
great debt of gratitude is owed to the farmers who participated in
the various studies and who accepted with equanimity what might
have seemed to be continual questioning.
We also wish to express our gratitude to the reviewers of
this paper including D. Byerlee, C. Liedholm, J. B. Sjo,
and H. M. Hays. We, however, take responsibility for any mistakes
in the paper.
The permission of the Director of the Institute for Agricultural
Research, Ahmadu Bello University to publish this paper is gratefully
acknowledged. Contribution number 79-112-B, Kansas State Agricultural
Agriculture plays a major, indeed a basic, role in the economy
of Nigeria. Prior to the discovery of oil, agricultural output provided
the prime source of foreign exchange. Furthermore, agriculture has
always been the major employer of the bulk of the population. The
geographical area which will receive most of the attention in this
paper--the area popularly known as Hausaland--has a population that is
predominantly rural based, about 73 percent of its inhabitants deriving
their living from agriculture [Ministry of Economic Planning, 1966]. It
is therefore not surprising that rural development is a key element in
governmental policies for the economic development of Nigeria.
The importance of increased agricultural production has been recognized
by the Nigerian government as can be seen in its support of a number of
agricultural research institutes over the years. In northern Nigeria,
agricultural research work by technical scientists was initiated by
the Department of Agriculture in 1924. In 1957, this technical research
became the responsibility of the Research and Specialist Division of
the Ministry of Agriculture of the former northern region of Nigeria.
The Institute for Agriculture Research and Special Services (IAR) was
established when this division was transferred from the Ministry of
Agriculture to Ahmadu Bello University (ABU) in October 1962.
The research mandate of the Institute has primarily covered
the northern states of Nigeria. The research priorities of the
Institute are determined by an interactive process between government
representatives and individuals at IAR itself; but the government
has the final responsibility in delineating research priorities.
Historically, the technical disciplines have dominated the Institute.
But, in 1964 a substantial grant was provided by the Ford Foundation
to ABU to encourage research in the social sciences. One of the major
objectives of this funding was to facilitate an interdisciplinary approach
to solving problems of rural areas. Later, as the program developed,
the funding was progressively taken over by ABU, and positions for social
scientists were incorporated into the core budget of IAR.1
In drawing up the research program that was undertaken by the social
scientists in IAR, two basic factors were taken into account [Norman,
1974a]. In the first place, rural development programs in the northern
part of Nigeria have usually emphasized working with the farmer in his
traditional setting rather than moving him to irrigation schemes,
settlement schemes, etc. The farmer's voluntary participation in the
developmental process and the work being undertaken largely within the
traditional setting necessitated research that sought to obtain an
understanding of the problems and constraints faced by farmers at the
1The organizations under which the social scientists were located
were originally the Rural Economy Research Unit and later the Department
of Agricultural Economics and Rural Sociology. The disciplines that
were initially represented were Rural Sociology, Agricultural Economics,
and Geography while later on Agricultural Extension took the place of
Geography. The Rural Economy Research Unit also helped develop research
expertise similar to that being developed in other parts of ABU outside
the Department of Agricultural Economics and Rural Sociology.
village level. Such information can provide a valuable input into
developing improved technologies that are relevant for farmers.
In the second place, there was a definite bias toward a micro- rather
than a macro-oriented research orientation. There were a number of
reasons for this: (a) there was a paucity of accurate data at the village
or micro level in the northern states; (b) the work of technical researchers
and extension specialists at IAR could best be complemented by micro-
research of social scientists; and (c) macroeconomic research expertise
was available at other Nigerian universities and research institutions.
In the light of these considerations, the social science research
program which evolved consisted of four phases [Norman and Simmons, 1973]:
1. Positive phase--determining what farmers are doing
2. Hypothesis-testing phase--determining why farmers do things
a certain way
3. Normative phase--determining what farmers ought to do1
4. Policy phase--determining how the changes suggested under phase
three should be brought about. This phrase may also involve a
consideration of phase two to determine whether the suggested
policy is in conflict with the farmers' reasons for doing things
in the traditional way.
Phases one and two were primarily concerned with village studies
and were called basic studies. Later, increasing emphasis was placed
on change studies, which emphasized phases three and four. The
1Phase three implies an interaction between the change agency
and the farmer.
change studies were articulated by testing the relevancy of improved
technologies, evaluating specific programs designed to encourage
change, and testing various developmental strategies on a small scale.
In order to carry out such a research program, it is essential
to have cooperation between disciplines. The desirability of cooperation
among various social sciences has long been recognized. However, only
recently has the desirability of cooperation between the social and
technical sciences been recognized. There was some cooperation among
the social science disciplines during the first two phases of the work;
cooperation between social and technical disciplines became more common
in the third and fourth phases. These latter phases also involved
considerable cooperation with government agencies.
1.2 The Scope of the Paper
This paper reports results on only a small part of the total research
program undertaken by social scientists at IAR and ABU. Although this
paper focuses on a study of Hausaland, this area is ecologically similar
to areas in other countries of West Africa, from Senegal to the Cameroons.
Therefore, it is anticipated that, with relatively small adjustments,
the methodological approach used in conducting the studies and the
empirical results derived from them may have some applicability to
other countries in the same ecological zone in West Africa.
The general objectives of this paper are as follows:
1. To present a comparative analysis of the economics of small-farm
agriculture in three areas of Hausaland, i.e., Sokoto, Zaria, and
2. To assess the profitability and relevance of three improved
technological packages (i.e., for cotton, sorghum, and maize) for
small farmers in the Daudawa area northwest of Zaria.
3. To discuss the implications of the results for research
workers and policy makers in Hausaland and in the Sahelian countries
with a similar ecological base.
1.3 The Farming System
A farming system includes inputs of land, labor, capital, and
management which are applied to one or more of three types of production--
crops, livestock, and off-farm enterprises--in order to produce products
and income. The functioning of the farming system requires that a number
of interrelated decisions be made about the quantities, qualities, and
ratios of the inputs to be used and the desired quantities and combinations
of products. These decisions are influenced by the total environment
in which the farmer operates. This total environment can be divided
into two parts [Norman, 1976b; Institut d'Economie Rurale, 1976]: technical
and human factors. Technical factors refer to the natural environment
and its conditions--either beneficial or harmful--that define the physical
potential of the farming system. Human factors, determine how the tech-
nical factors will be utilized and modified in order to realize the actual
1.3.1 Technical Factors
The technical factors include both physical factors of the
environment--water, soil, solar radiation, temperature, etc.--and
biological factors--crop and animal physiology, diseases, insect
invasion, etc. Technical scientists develop technology to improve
the potential of the farming system. Examples include irrigation
research to alleviate scarcity of water or drought, fertilizer to improve
soil quality, and new plant varieties which are early-maturing and
resistent to disease.
1.3.2 Human Factors
Traditionally, human factors have received little attention in
agricultural research. However, it is being recognized that the
irrelevance of much of the improved technology is due in part to a
lack of consideration of the impact of technology on the human factors.
These factors can be considered from both an exogenous and an endogenous
point of view.
The exogenous factors that affect the human element include, first,
the general social environment. These factors are largely outside
the influence or control of the individual farmer, but they influence
what he can do. The first of these exogenous factors is the community
structure, the way the society is organized at the village level.
This factor evolves in a unique way within each community. At a broader
level are the infrastructural factors which affect the input into the
farming system and the output produced. In developing countries,
exogenous factors are linked to the government and are influenced by
the government. For example the government can affect inputs by
swaying the opinions of farmers, perhaps through the extension
staff. It can provide farmers with the means of purchasing improved
technology through a credit program. Finally, the government can
assure that improved input distribution is available when needed
and to the proper degree to produce desirable results.
In addition to affecting the input, these infrastructural, or
exogenous, factors also affect the product of the farm system. The
most obvious area in which the government can play a role is in the
marketing process and pricing. The extent of this influence will
vary. The government can control prices directly through setting
prices through marketing boards, etc. Indirectly, the government can
affect the size of the market by improving roads, transport systems,
and so forth.
Finally among the exogenous factors affecting the human element
are geographic considerations. The location of the village will affect
the market for its produce and off-farm employment opportunities for
its people. The density of the population will affect the size of
the farms, hence the type of improved technologies that will be suitable.
In addition to the exogenous factors discussed above, there are
also endogenous factors. These factors relate directly to the individual
farmer as the decision maker. Each farm household has access, in some
measure and quality, to the four basic inputs into the farming system:
land, labor, capital, and management. The farmer must decide how to
utilize these inputs and the exogenous, government-related inputs to
improve his production. Subject to his constraints and attitudes, the
farmer allocates his inputs to the type of farming system he desires
in order, as nearly as possible, to attain his objective.
In this paper, the primary focus is on the farmer, on what he
does and how he makes his decisions, in other words, on the endogenous
factors. The other factors--the conditions of the natural environment,
improved technology, government policy, and government input--define
the boundaries of the farmers' actions, even though within these
boundaries the farmer has considerable choice to act consistent with
his goals. Therefore, the interrelationship between the exogenous
factors and the endogenous factors must be carefully considered. This
relationship is basic to introducing improved technologies which are
Government policies in the northern part of Nigeria have hitherto
favored the introduction of small changes over large areas rather than
large changes over small areas. Traditionally, government agencies
have provided the support services on the input side, such as extension,
input distribution, and credit programs. Improved inputs, particularly
fertilizer, have been subsidized, while cash-crop marketing has been
controlled through marketing boards. The taxing function implicit in
pricing policies for cash crops has in recent years been removed, while
there is a gradual movement toward setting minimum prices for food crops.
A recent development has been the introduction of IBRD-financed
integrated agricultural development projects in specific areas where
the level of support services is much higher than was possible traditionally.
1.4 Description of Hausaland
Hausaland is the name given to the area where people of dominantly
Hausa/Fulani origin live. This area constitutes nearly 30 per cent
of Nigeria--274,000 square kilometers [Ministry of Economic Planning,
1966]. The northern and western boundaries of Hausaland in Nigeria
are with Niger and the Republic of Benin respectively, while the
southern and eastern boundaries correspond approximately to the
latitude 10 degrees north and nine degrees longitude east.
1.4.1 The Technical Environment
Two major ecological zones can be identified in Hausaland. They
are the Northern Guinea Ecological Zone, which is located in the southern
part of the area, and the Sudan Ecological Zone, located in the northern
part [Keay, 1959].1 The two ecological zones have a number of
characteristics that have a profound influence on the agriculture
practiced in each. The Northern Guinea Ecological Zone is dominated
by a basement complex which is granitic in origin. In the Sudan area,
the underlying rocks tend to be primarily of sedimentary origin. In
the south, leached ferruginous soils are typical, while in the north
and northwest, the change in the underlying rock results in soils that
are more sandy in nature and therefore contain a lower proportion of
silt and clay. The general land form is undulating in nature, although
the southeastern part of the area tends to be of higher altitude
(460 to 920 meters), becoming lower in the north and northwest towards
the Sokoto River basin area. The general landscape is broken occasionally
by mesas and lateritic strewn ridges, which can rise up to 60 meters
above the general surface. In addition, a number of small rivers,
The Sub-Sudan Ecological Zone, which was differentiated by Clayton
, has, for the purposes of this paper, been included under the
heading Northern Guinea Zone. It has, in fact, a rainfall more similar
to that of the Sudan Zone, but the length of the rainy season is similar
to that of the Northern Guinea Zone [Klinkenberg, 1975]. Therefore,
it is transitional in nature.
sometimes with broad valleys, dissect the general surface area,
particularly in the north and northwestern part. In these valleys
are located heavier hydromorphic soils of clay texture that are
poorly drained. These often are cultivable during the dry season.
However, such land is available only in very limited quantities.
The temperatures in the area are fairly homogeneous, although
there are some distinctive features in regard to rainfall. The
temperature ranges from a minimum mean monthly temperature of 13C,
in Bauchi, to a maximum mean monthly temperature of 400C, in Sokoto.
Thus temperature itself is not a limiting factor in terms of growth.
Rainfall is, of course, the major limiting factor. The whole area has
a distinctive rainy season with a unimodal peak in August and a seasonal
length of four to six months. However, there are a number of distinguishing
features in the amount of rain as one moves from the Northern Guinea
Ecological Zone into the Sudan Ecological Zone. The total rainfall
decreases toward the north (Tables 1.1 and 1.2). Rainfall of 900 to
1400 millimeters is characteristic of the Northern Guinea Ecological
Zone, while a total of 500 to 900 millimeters is more characteristic of the
Sudan Zone. The length of the rainy season also decreases as one
moves north. Kowal and Knabe  have calculated that the length
of the rainy season decreases one day for every 5.5 kilometers one
moves north. This decrease results in a corresponding decline in
the length of the growing season in the north (Table 1.2).
There is considerable annual variation in the amount and distribution
of rainfall as shown in Table 1.2. Of even greater concern is the
Table 1.1. CHARACTERISTICS OF THE THREE STUDY AREAS
Population Density of Province
Study Area Location Zone 1952 1963
Sokoto 130 O1'N 50 15'E Sudan 30 49
Zaria 110 11'N 70 38'E Northern Guinea 18 31
Bauchi 100 17'N 90 49'E Northern Guineaa 13 24
Sources: Kowal and Knabe ; Ministry of Economic Planning .
aBauchi is technically in the sub-Sudan Zone.
Table 1.2. CLIMATE IN THE STUDY AREASa
Rain for Individual
Length Growing Season Months in ms (cv)
Mean Monthly Total cv of Months
temperature Rain in Rainy when Start Peak End
Study in mms Monthly period Length _Water is
Area Min. Max. (c.v.) Rainfall (days) (days) Start End Surplus April May Aug. Sept. Oct.
Sokoto 15.0 40.0 29.6 137 120 150 June Oct. July-Sept 11 42 250 134 23
1-10 21-30 (255) (148)(59) (72) (223)
Zaria 13.9 35.0 43.9 115 160 190 May Nov. June-Sept 37 132 281 230 36
(14.9) 11-20 1-10 (133) (80) (56) (53) (193)
Bauchi 12.8 36.7 43.4 127 150 180 May Nov. June-Sept 33 91 346 185 37
(19.0) 21-30 1-10 (157) (90) (46) (56) (164)
Source: Kowal and Knabe .
a The symbol cv is the coefficient of variation. The start of the rains and the start of the growing season is
defined as the first ten-day period in which the amount of the rainfall is equal to or more than 25.4 mms followed by
a subsequent ten day period in which the amount of rainfall is at least equal to one-half the evapotranspiration demand.
The end of the rains is assumed to occur when the water storage in the top four inches of soil is used up. Water-surplus
months are defined as those in which rain exceeds evapotranspiration and soil water storage.
irregularity of the rains at the beginning and at the end of the
rainy period. The harsh climatic regime of Hausaland, particularly
with respect to the short growing season and limited and unstable
rainfall, places considerable restriction on the types of crops
that can be grown in the area.
1.4.2 The Human Environment
Individuals of Hausa/Fulani origin constitute 75 per cent of the
population in the Hausaland area of northern Nigeria and 28 per cent
of the population of Nigeria [Ministry of Economic Planning, 1966].
However, as Hill  has pointed out, the concept of Hausa is a
linguistic and not an ethnic term and refers to those who, by birth,
speak the Hausa language. Therefore, individuals of a number of
different ethnic origins use Hausa as their first language, including
many of Fulani origin who are settled farmers. The dominance of the
Hausa-speaking group is illustrated by the fact that an estimated 15
million people speak that language in the northern part of Nigeria and
the Hausa are the largest linguistic group in sub-Saharan Africa. In
iThe climate and geology of the area interact to produce savanna
woodland vegetation in the Northern Guinea Zone. Grasses consist
mostly of Hyperrhenia/Andropogon species, while the main trees are
Isoberlinia species. The density of population in this area is variable
(Table 1.1), and in some parts the natural ecology is still visible.
However, in the northern part of the Sudan Zone where the population
density is higher much of the land is continuously cultivated. The
natural vegetation, where it does exist, consists of Andropogon gayanus,
which is the dominant grass, together with Combretum, Acacia, and
Commiphora species, which are the most common trees.
Individuals of Hausa origin are also found in Niger, while Fulani
(i.e., Fulbe, Peuhl) are distributed throughout West Africa.
spite of the differences in ethnic origin, Hausa people as a whole
have a high degree of cultural, linguistic, and religious uniformity.
Hill has noted that the differing patterns of socioeconomic organization
are a function of the rural and urban life styles rather than of
ethnic differentiation. The area has a traditionally strong hierarchical
authority structure under the leadership of an emir or sultan at the
emirate level. The power structure is held by the Fulani, the
group which achieved a degree of dominance after the jihad, or holy
war, in 1804. However, there has been a high degree of cultural
assimilation of the Fulani with the Hausa people, and this has prevented
the continuance of ethnic exclusivity [Hill, 1972].
People of Hausaland live mainly in the rural areas and can be
differentiated according to three major modes of living. The first
are the Fulani, who practice varying degrees of transhumance [Van Raay,
19691 and move throughout the area with their herds of cattle, sheep,
and goats. The second are the magazawa, who are settled farmers who
have not accepted the Islamic religion and are still basically animists.
Usually they live in isolated settlements and are a distinct minority
in the area. Finally there are the settled farmers, both Fulani and
Hausa, who have accepted the Islamic faith and live in nucleated
settlements. These people constitute the majority of the population
of Hausaland. The main focus of this paper will be on these settled
farmers, particularly with respect to their agricultural practices.
2. METHODOLOGY FOR VILLAGE STUDIES
2.1 Selection of Areas and Villages
The data analyzed in this paper were collected from three different
areas in Hausaland which were centered on the towns of Sokoto, Zaria,
and Bauchi. These three areas, as emphasized earlier, are fairly
homogeneous culturally. However, two geographical characteristics
differentiate them (Tables 1.1 and 1.2): Sokoto has a lower rainfall
together with a shorter rainy season; the population density is high
in Sokoto and much lower in Bauchi.
Within each area, three villages were purposively selected and
considerable effort was made to ensure that these villages were repre-
sentative of others in the same general location. Although it is
difficult to select representative villages, we believe that this
selection process is simpler in situations where hand labor is the
main power source. In such situations the potential for wide variations
in input and product combinations is considerably circumscribed [Clayton,
Two of the main criteria employed in the selection of the survey
villages were as follows:
1. The villages should differ in their ease of access to the main
city in the area. This criterion also implied that the villages were of
different population densities, because in all the areas the population
density was positively correlated with the ease of access to the
main city. The underlying basis for the adoption of this criterion
was the concentric ring theory of von ThUnen, later reformulated by
Schultz . This more sophisticated version, which considers both
the factor and product markets, reasons that farmers' incomes will tend
to be higher near urban areas because of the greater efficiency of the
factor and product markets.1
2. The village in each area that represented an intermediate degree
of access to the major city should have a relatively higher proportion
of land which could support crops during the dry season (i.e., fadama,
or river bottom land). The purpose for selecting this village was to
capture the differences in farming systems that evolve when there is a
possibility of extending the agricultural season into the long dry
The villages selected in each area together with some of their
characteristics are listed in Table 2.1.
In addition to the surveys carried out in the three areas discussed
above, a study was made of the relevancy of three improved technological
packages in the village of Daudawa (11038'N, 709'E), located about
eighty kilometers from Zaria.2
Other criteria that were used in selecting suitable villages are
discussed in detail elsewhere [Norman, 1973].
2The improved technological package for cotton was also tested
at four other sites in the general area of Zaria [Norman, Hayward, and
Hallam, 1974]. However, the major emphasis in this paper is devoted
to the results obtained from Daudawa, where, in addition to cotton,
improved technological packages were also tested for both sorghum and
Table 2.1. CHARACTERISTICS OF VILLAGES INCLUDED IN
THE SURVEYS IN THE THREE STUDY AREAS
Percent Percent Amount Size Sampling
Study Hectares of Land of Land of Potential of Percent- Survey
Area Village Resident Fallowed Fadama Farm Land KeyC Sample age Year
Takatuku 0.58 5.2 2.8 1 HN 31 23.9 April '67
Kaura Kumba 0.49 1.0 38.8 1 HI 31 16.0 to
Gidan Karma 1.16 3.7 2.0 2 HR 38 32.2 March '68
Hanwa 0.27 2.5 5.5 2 IN 38 43.2 April '66
Doha 0.52 26.8 12.2 3 II 44 28.8 to
Dan Mahawayi 0.76 21.2 8.4 4 IR 42 38.5 March '67
Bishi 0.60 17.6 0.0 5 LN 40 37.0 April '67
Nasarawa 0.47 28.0 16.2 5 LI 37 39.0 to
Nabayi 1.04 48.2 0.0 6 LR 39 49.4 March '68
aDue to the difficulties in defining village boundaries and measuring the village area, these figures represent the hectares of
farmland per resident. An example of land use around one of the villages (Gidan Karma) is given in Figure 2.1.
bThis is a subjective ranking of land in the village areas which is not at present farmed or fallowed but could potentially be
cultivated: 1 indicates very low availability while 6 indicates a considerable amount of bushland that could potentially be cultivated.
cTo help clarify presentation in the text, each village is identified with two letters: the first indicates population density
(high, intermediate or low) and the second signifies location with respect to the main city in the area (near, intermediate, or remote).
DI Upland Cultivation
M Lowland Cultivation
FT 1000 0 1000 2000 3000
Lowland Bush IGullies
E Gallery Forests rrrrr. Scarps
Plantations ff Settlements
E'jRock & Laterite outcrops a Compound
4000 5000 6000 7000
- Main Road
r==== Bush Road
Compiled by A.OGoddard L Drwn byBob W;llgms
Figure 2.1 Land Use Surrounding Gidan Karma Village, Kware District,
I I I
2.2 Selection of Farm Families
The prevalence of the extended family system in the region
provided a major problem in defining a fixed family unit.1 A farm
family was defined as a unit with the same composition throughout
the year and with consumption and work units being synonomous in
so far as possible. Ultimately, we define a family as "those people
eating from one pot."2
Initial field work in each of the villages involved undertaking
a census of the inhabitants. From the frame of farm families which
resulted, a random sample was selected for the detailed study. The
sizes of the samples in the villages ranged from 31 to 44 (Table 2.1).
2.3 Strategy for Data Collection
A combination of strategies was used in collecting data at the
1Hill  and others have documented the fact that the family
unit can vary in composition from season to season.
2In Hausa, this is interpreted as "suna ci daga tukunya daya."
This definition is, in fact, identical to that adopted by the Federal
Office of Statistics in the agricultural sample surveys. This definition,
though not ideal, was the best that could be practically applied. The
words "pot," "family," and "household" are considered synonomous in the
3These sizes reflect the number of records on which the analysis
was based. They do not reflect those which were not completed (very
few were in this category) or those which were rejected in the analytical
stages because of inconsistencies in the data (a more common problem).
The size of sample that was selected in each village was based on the
number of farming families which two enumerators could handle. Also
involved in the size of sample was the notion that a reasonable statistical
level of precision could be obtained on most variables through sample
sizes of 30 to 50 [Zarkovich, 1966].
farm level. The characteristics of the variables determined whether
an interview or direct measurement approach was used. If an interviewing
approach was used, characteristics of the variables also determined
whether a structured or unstructured questionnaire was used and
whether data needed to be collected at frequent or infrequent intervals.
Details concerning the approach used, the questionnaires, and
the numerous problems encountered in collecting the data are discussed
in considerable detail elsewhere [Norman, 1967-72, 1973, 1977b; Kearl,
1976]. In general, emphasis was placed on direct measurement, structured
questions and interviewing each farm family twice a week.1 Detailed
day-to-day information was collected on farm inputs (i.e., labor, land,
seeds, fertilizer, tools, and animals), output, marketing, and farm
activities of family members, etc. Direct measurement methods were
used to collect information data on cultivated area and crop yields.
Thus information was collected on all facets of the farming system.
The farmers included in the studies of improved technological package
in Daudawa village all had expressed an interest in participating in
the programs of new technology. Therefore, the sample of farm families
It should not be assumed that the approach used in these studies
was optimal. We would advocate a number of changes in future studies.
Consideration of the conflicts between reducing sampling errors or
measurement errors and fundamental differences between registered
single-point and nonregistered continuous types of data lead us to
recommend two levels of samples. The first one would be a fairly large
sample concentrating on minimizing sampling errors and collecting
single-point registered types of data, while the second sample would be
much smaller, would emphasize minimizing measurement errors, and would
concentrate on collecting nonregistered continuous types of data. The
latter approach was used by Matlon  in his recent study in one
part of Hausaland.
who were interviewed could be biased toward the better farmers.
However, the wide variation in attitudes and performance of farmers
adopting the technologies appeared to indicate that there was not a
serious bias in the selection of farmers [Beeden, Norman, Pryor, Kroeker,
Haus, and Huizinga, 1976]. The studies in Daudawa involved the
collection of data on fields where the improved technological package
was used; data were also collected on other fields where the farmer used
his indigenous practice and technology for cultivating the crop (Table
2.2). Advice was given to the farmers about when to undertake the various
operations on the improved plots and improved inputs were supplied on
credit. Since all the work was undertaken by the farmer, he did not
always follow the time schedule recommended by the extension agent. The
extension staff and the inputs for the project were provided by the Kaduna
2.4 Layout of Paper
Due to the complexity of the farming systems, there were many
analytical problems. In order that the results of the studies of the
Indeed, it is suspected that the main reason that most farmers
wanted to participate in the project was to have access to the improved
inputs in the technological packages. During the years in which the
technological packages were tested, the input distribution system was
somewhat deficient, particularly with respect to shortage of fertilizer.
Therefore, it is likely that the desire to participate in the project would
not be confined simply to the more progressive farmers in the society.
We recognize that the approach used can be legitimately criticized, and
we advocate that a more random sampling procedure be used in future
studies of a similar nature.
2Methodological problems involved in analyzing the data are discussed
in considerable detail in Norman, 1967-72, 1973; Norman, Fine, Goddard,
Pryor, and Kroeker, 1976.
Table 2.2. IMPROVED TECHNICAL PACKAGES INTR DUCED
IN DAUDAWA VILLAGE, 1971-74
of 1971 1972 1973 1974
Package Hectares Nos. farmers Hectares Nos. farmers Hectares Nos. farmers Hectares Nos. farmers
Improved 8.2 19 8.8 20
Improved 18.9 19 22.0 12
Indigenous 15.9 24 22.3 9
Improved 12.9 5 26.0 13 27.4 19 27.9 23
Intermediate 45.7 21 22.0 11 12.4 8 22.4 12
aMost of the analysis in Section 5 is actually based only on the results for 1973-74.
three areas can be presented in an orderly manner, the following
format is used:
1. Presentation of an overview of farming in the Hausa region
as a whole.
2. Assessment of differences in traditional farming in the three
3. A brief summary of the influence of village location, land
type, cattle ownership, and family structure on the performance of
To simplify as far as possible the presentation of the results,
a weighting system was employed in which each village was weighted
equally in terms of the relationships mentioned under the above items.
In terms of assessing the relevancy of the improved technological
packages, the analytical problem was much simpler. In addition to
much smaller sample sizes (Table 2.2), the data collected and analyzed
involved only a comparison of the improved technological package with
indigenous practices for the same crop. Thus there was no attempt to
analyze the whole farming system.
Both Matlon  and Hill  have stressed the heterogeneous
nature of farm families together with the potential dangers that could
result from using average figures rather than looking at distributions.
Although the breakdown implied in this section implies some disaggregation,
space limitations do not permit a detailed discussion of these nor a detailed
analysis of the distribution of resources and incomes. These are discussed
to some extent elsewhere [Norman, 1967-72; Goddard, Fine, and Norman, 1971;
and Norman, Fine, Goddard, Pryor, and Kroeker, 1976c]. It is unfortunate
that the distributional question did not receive more emphasis during the
data collection phase since more recent work by Palmer-Jones would appear
to indicate that this could well be becoming an increasingly important
issue. See also Norman, Ouedraogo, and Newman, 1979
3. DESCRIPTIVE PROFILE OF TRADITIONAL FARMING IN HAUSALAND
This section provides an overview of agriculture in the three
study areas of Hausaland. Resource availability and use are first
considered, followed by a discussion of production and income. What
emerges is a profile of an agriculture that, in the late 1960s, was
still largely based on traditional practices.
Land tenure in Hausaland has a double ancestry--both in the
traditional African concept of communal ownership and in Islamic
land law, which recognizes individual tenure [Goddard, 1972]. At present,
people have usufructuary rights to the use of land within the community
where they reside [Abalu and Ogungbile, 1976]. This implies that land
ownership is still largely vested in the community and that the individual
residing in the community has no right to alienate the land he holds.
Goddard  has identified four possible factors which can lead to
inefficient land use under such a system. These are:
1. Lack of security of land title may discourage farmers from
making long-term improvements in the land. Also, communal tenure
implies that no individual has the right to mortgage or sell the land
without the consent of other members
2. There is a restriction on the geographic mobility of farmers
resulting in considerable inequality of land distribution among areas
3. Population pressure leads to a progressive subdivision of the
farm of each land-holding group. Therefore, farm sizes tend to become
smaller over time, which, along with the lack of relevant improved
technologies, in some areas results in declining standards of living
4. Family land is partitioned on inheritance. In the study areas,
an average of 76 percent of the land area is held by this system of
land transference (Table 3.1).1 The principal effect of this system
of land transference has been increased fragmentation of farms (e.g., see
Figure 3.1 of Gidan Karma). The average family farm of 3.9 hectares in
the three study areas consists of more than six different fields.
Fragmentation of the land has a number of advantages in traditional
agriculture. These revolve around the notion of greater equitability--for
example, in distributing land of different soil types, minimizing the
effect of microvariations in rainfall, particularly at the beginning and
end of the rainy season, and minimizing the inconvenience of field
location since most families live in nucleated settlements. The
disadvantage of excessive fragmentation under traditional agriculture
is that a disproportionate amount of time is spent in farmers' walking
between the residence and the various fields.3 However, Goddard 
1The tables in this section, in addition, include some information
for each study area. However, discussion of differences among study
areas is deferred to Section 4.
2A farm is defined in the paper as the sum of the acreage over
which the farming family possessed usufructuary rights during the survey
year. A field was defined as a unitary piece of land farmed by one family
3Cleave  has reported that in some parts of Africa, up to 30
percent of farmwork is spent in farmers' walking to and from fields.
CHARACTERISTICS OF LAND TENURE
IN THE THREE STUDY AREASa
Sokoto Zaria Bauchi Average
Details of farm in hectares:b
Farm size 3.9 (3.3) 3.9 (16.8) 3.9 (31.3) 3.9 (17.1)
Gona 3.5 (2.8) 3.5 (16.9) 3.7 (30.6) 3.6 (16.8)
Fadama 0.4 (101) 0.4 (18.3) 0.2 (42.7) 0.3 (23.7)
20:80 percentile points on farm sizec 2.3:6.1 1.7:5.2 1.6:5.5 1.9:5.6
Average number of fields 5.8 6.2 6.8 6.3
Land inherited (%) d 70.1 64.9 92.3 75.8
Land mobility index 2.64 2.41 2.84 2.63
aIn the comparative analysis apart from Tables 3.4, 3.8, 3.9, 3.10, 3.12, 3.13,
A.4 each village is weighted equally in the average for each area-and in the overall
sizes for the individual, villages are given in Table 2.1.
3.14, 4.1, and
bFigures in parentheses represent the percent of land fallowed.
cThat is 20 percent of the farms are less than first figure and 20 percent are greater than the
dThe method of calculating the index is presented elsewhere [Norman, 1967-72]. The value of the
index can vary from 1 to 3. A low value implies a preponderance of more mobile types of tenure (i.e.,
purchase, rent, lease, etc.).
to eo s -oa o
AVOAVA ,.,A A>2v, ,)
VA i ,,. AA
o s ast
cmo e Au lA VIA .0, s ),AIo A unAA A oSe
I -I - -
r 3 n Ei a A E lN PCo vAE VILLAGE fI
il onai 1e.iaA N es ~ -
A ,,d ----NAA A _- -- - SIV -- -- -- -- -- -- -
FigureA 3.1 *ia Aam Village, Kwr Vitrct Soot roine
Field Boundaries with Serl N
I VA' IA A
IA. VA -
A A- FEr A A
CVVAVAV ,VVKi AI' AII A
OIEAA AAVIOVAV M
LIE ---; ~" "'.'. Lil
FIE~S AVVIS bybA~lr VIVIKUVI
AVAS,, And LAAIIAAVAAP V.I
Figure 3.1 Gia am ilge wr ititSkt rvne
Fiel Bonare with Seria Nunters
found in more densely populated areas, where excessive fragmentation
is becoming a problem, that farmers of larger holdings are consolidating
their fields through various land transactions such as exchange, sale,
or purchase. Fragmentation could cause problems when certain types of
modern systems of agriculture are introduced. For example, land
improvement and conservation measures may be more difficult because of
the need for cooperation among neighbors, and small fields may prevent
the introduction of mechanization.1
The above description of the land tenure arrangements would imply
that land is fairly equitably distributed within villages. There was,
however, somewhat more inequality in distribution than would have been
expected, although much less than in some societies. The average value
of the Gini coefficient for the nine villages varied between 0.20 and
0.56 (Table A.1). However, when cultivated land alone was considered,
the coefficients were reduced to a range between 0.20 and 0.49, which
indicated a more equitable distribution of land. In the region as a whole,
an average of 17 percent of the land was fallowed.
STwo types of farmland can be differentiated: first, rainfed upland
fields (i.e., gona), which support crops of relatively lower value per
unit of land area, such as millet, sorghum, groundnuts, cowpeas, cassava,
and cotton; second, lowland fields (i.e., fadama), which support more
1This is not meant to imply that mechanization (e.g., oxen and
equipment and tractors) will necessarily be desirable. Delgado 
has noted this problem with respect to the introduction of mixed farming
(i.e., oxen and equipment) in Upper Volta. In Senegal, the Experimental
Units have tried to ensure that farmers adopting mixed farming have field
sizes of at least one hectare [Richard, 1978].
labor intensive crops of relatively higher value per unit of land
area, such as sugarcane and, to a lesser extent, rice and calabash.
Gona land is by far the most dominant, accounting for 92 percent
or 3.6 hectares of the average farm (Table 3.1). Crops on gona land
are primarily grown in mixtures. Gona land near residential areas is
usually permanently cultivated, soil fertility being maintained through
the addition of organic fertilizer; farther away from the residences,
land that is fallowed is more common. Slightly less than 17 percent
of the gona land was fallowed.
Fadama land permits year-round cultivation because the water table
is located close to the surface. Crops on fadama are usually grown
in sole stands or in a double cropping system. One would logically
expect fadama land to be cultivated very intensively. However, a number
of factors determine whether such land is used intensively. For example,
there may be a lack of labor required for cultivating such land because
of off-farm employment opportunities during the dry season; there may
be flooding during the rainy season or lack of market outlets. Many
of the crops produced on fadama land are primarily cash crops which are
of low value per unit weight and therefore expensive to transport.
As a result of these problems on the average almost 24 percent of the
fadama land was in fallow (Table 3.1).
3.2.1 Family Size and Organization
The average family consisted of six to seven individuals constituting
about five consumer units (Table 3.2).1 Families can be divided into
two types of units: a simple or iyali family unit, which consists of
one married man with his wives and dependent children, and a composite
family or gandu, which is composed of two or more male adults, usually
married, together with their wives and children.
In general, simple family units were found to be more common than
composite families. In the study areas, an average of 38 percent of the
farming families were organized under the gandu system. Both Buntjer
 and Goddard  have observed that traditionally the gandu
family was the preferred type. One of the reasons why the gandu system
is declining is because the head of the composite family has considerable
authority [Hill, 1972]: he supervises the farming activities on most
of the family fields,2 and he instructs the family members about what
and how much work should be done. At the same time he has some obligations:
he is responsible for providing food for the family and for paying
any taxes. According to Buntjer, there is an increasing tendency of
family members to resent the power of the head of the composite family.
Also, in the composite family unit, young married male adults are not
in a position to undertake a management role as far as farming activities
The consumer units were based on dietary requirements suggested
by Food and Agriculture Organization . The weighting systems
employed were as follows: male adult (more than 14 years old) equals
one consumer unit; female adult (more than 14 years old) equals 0.73
consumer unit; an older child (7 to 14 years old) equals 0.71 consumer
unit; and a younger child (less than 7 years old) equals 0.43 consumer
A few fields, e.g., less than 10 percent of the cultivated area in
the Zaria area [Norman, 1967-72], known as gayauna fields, were farmed
by individuals other than the household head.
CHARACTERISTICS OF FARM FAMILIES
IN THE THREE STUDY AREAS
Sokoto Zaria Bauchi Average
Sizea b 5.6 (1.5) 8.6 (2.2) 6.0 (1.6) 6.7 (1.8)
20:80 percentile points 4:7 5:11 3:8 4:9
Consumer units 4.1 6.2 4.4 4.9
Percent of families of
iyali type 71.9 51.0 64.4 62.5
Land in hectares per:
(a) Resident b 0.7 0.5 0.7 0.6
20:80 percentile points 0.4:1.1 0.2:0.8 0.3:1.0 0.3:0.9
(b) Consumer unit b 1.1 0.7 0.9 0.9
20:80 percentile points 0.6:1.4 0.4:1.0 0.4:1.3 0.4:1.3
Annual man-hours on the 1566.3 1800.0 1316.5 1560.9
Source of farm work as a percent
of total man-hours:
Family: Male adults 72.4 72.2 75.2 73.3
Female adults 1.1 0.3 3.3 1.6
Older children 8.5 8.9 9.9 9.1
Hiredc: Kwadago 7.7 (3.2) 8.6 (4.3) 3.1 (5.0) 6.4 (4.2)
Jinga 3.0 (7.2) 9.1 (6.3) 1.5 (7.7) 4.5 (7.1)
Gaya 7.3 (0.9) 0.9 (1.1) 7.0 (1.3) 5.1 (1.1)
aFigures in parentheses represent the average number of male adults per family.
bThat is 20 percent of the families have values less than the first-figure and 20 percent greater than
CFigures in parentheses reflect the average wage rate per man-hour in Lobo.
are concerned. For these reasons, the composite family organization
apparently is being superseded by the simple family type. The rate at
which this change is taking place depends on a number of complex inter-
actions such as availability of land, opportunities for off-farm employment,
and ownership of cattle [Buntjer, 1970; Goddard, 1969; Norman, Pryor, and
3.2.2 Types of Work on the Family Farm
Hand labor was the power source on farms in the study areas. This
helps to explain the relatively low average size of farms--3.9 hectares.
In terms of the land-labor ratio, the average was 0.6 hectares per
resident (i.e., 0.85 hectares per consumer unit). The average annual
labor input on the family farm was found to be 1,560 man-hours, excluding
time farmers spent walking to and from the fields (Table 3.2).2 Eighty-four
percent of the total labor input on the family farm originated from family
sources. However, less than 2 percent of this was contributed by women.
Female work on farms was confined to specific operations, such as planting,
Implications of this are examined in Section 4.6.
2To permit direct comparisons between different types of labor, it is
necessary to express days and hours in terms of a common denominator (i.e.,
man-days and man-hours, respectively). Much controversy exists in the
literature over the relative weights to use [Collinson, 1972]. This problem
is further complicated because relative work productivities vary depending
on the type of task being performed [Hall, 1970; Cleave, 1974]. The
approach used in this study can be criticized as being too simplistic, but
it did cut down the computational burden. The weights used were as follows:
young child (less than 7 years old) = 0.00 of a male adult equivalent;
older child (ages 7 to 14) = 0.50 of a male adult equivalent; female adult
(more than 14 years old) = 0.75 of a male adult equivalent; and male
adult (more than 14 years) = 1.00 of a male adult equivalent. The concept
used by Spencer and Byerlee , who considered that wage rates of men
and women reflected different degrees of productivity and who therefore
used a weighting system based on this, was more precise than the one used
in this paper.
separating groundnuts from the haulm, and picking cotton. The lack
of participation by women in the work force is due to the Moslem practice
of auren kulle or seclusion of women [Smith, 1955]. Hill  has
observed that the practice of seclusion of women in the Hausa area is
more strict than in other parts of rural Moslem West Africa. Children
contributed about 11 percent of the total family labor input on the family
farm. Therefore male adults contributed the bulk of the labor from the
Hired labor contributed only 16 percent of the total labor input
on the family farm (Table 3.2). A major reason for this is that there
is no class of landless laborers in Hausaland. Also, since farming is
only partially commercialized, there is often a cash constraint on hiring
labor. Three types of hired labor are used: kwadago, which is hired
labor paid by the hour; jinga, which is paid by the job; and gaya, which
is communal labor. Remuneration for these types of labor can be in cash
or kind. However, payment for gaya labor is minimal and where given is
usually in kind. Although it appeared that all three types of hired labor
were equally popular in the study areas gaya labor was more closely linked
with traditional settings and with cattle ownership [Norman, Fine, Goddard,
Pryor and Kroeker, 1976]. It is possible, therefore, to hypothesize that
as development proceeds within the villages, gaya labor is likely to
become less popular. Also the introduction of improved technology will
invariably make farming more labor intensive if the power base does not
change from hand labor to the use of oxen. If the increasing scarcity
of hired labor persists, it is likely that jinga labor may become more
popular than kwadago since the wage rate--when expressed in per man-hour
terms--resulting from jinga work was, on the average, 69 percent higher
than for kwadago work.
It is likely that the family will remain the main source of farm
labor in Hausaland. Table 3.3 presents the results of a regression model
showing the relationship of family labor devoted to work on the family
farm to a number of other variables. As would be expected, the amount
of work input in the family farm was directly related to the size of
the family (i.e., total residents), and number of hectares of fadama and
gona cultivated. Also, as would be expected, there was a negative
relationship between family labor and the amount of hired labor employed
on the family farm. An additional variable reflecting time spent by family
members on off-farm occupations was included in the initial analysis. A
negative relationship existed between this variable and the dependent
variable; however, it was not significant. This is not surprising since
much of the off-farm work takes place during the dry season when work on
the family farm is minimal, implying that time devoted to off-farm occupa-
tions does not compete with time devoted to work on the family farm.2
However, care should be taken in interpreting these results because
of implying cause and effect relationships (e.g., hectares cultivated could
be a function of labor inputs) and the mixture of supply and demand vari-
ables among the independent variables (e.g., size of family and number of
Since family male adults contribute most of the family labor on the
farm analogous results were obtained when work done by the farmer was
used as the dependent variable (Table 3.2). In addition, a negative
relationship was found between the number of male adults in the family
and the time they work on the family farm.
Table 3.3. RELATIONSHIP BETWEEN TIMiE WORKED BY ALL FAMILY
MEMBERS AND BY FAMILY MALE ADULTS ON THE
FAMILY FARM, AND OTHER VARIABLES
Family Man-hours Days Worked per
Devoted to Work n Family Male Adult
Independent Variables the Family Farn on the Family Farm
Constant 52.6405 147.2285
Size of family X1 90.6812* (9.5869)
Number of male adults in family X2 -28.9208* (4.0447)
Gona X3 157.5709* (18.9643) 6.9533* (2.0945)
Fadama X4 483.5380* (23.6897) 56.5856* (9.1995)
Hired labor: Man-hours X5 -0.6148* (0.0749)
Man-days X6 -0.2108* (0.0465)
(a) Representing study area:
S1 (Zaria = 1) X7 27.1919 (77.2779) 18.4388 (8.6849)
S2 (Sokoto = 1) X -30.4405 (82.8956) -4.1466 (9.2588)
(b) Representing location
of village relative to major city
in study area:
VI (Near village = 1) X9 440.0466* (82.3579) 39.6604* (9.4580)
V2 (Intermediate village = 1) X10 205.2618 (90.0740) 34.9357* (10.1170)
R 0.7444* 0.4842*
Syx 571.5868 64.1518
aFigures in parentheses are standard errors.
bExcludes time devoted walking to and from fields.
CThe dummy variables are included to take account of differences in location (i.e., among study
areas and in ease of access). Location differences can embody differences in climate, soil types,
population densities, etc.
*Significantly different from zero at the 5 percent level. N = 340.
3.2.3 Work by Male Adults
On the average, a male adult in Hausaland was found to work about
1,270 hours per year, which included time required to walk to and from
fields, and both farm and off-farm work. This labor was spread over a
period of 244 days with an average length of 5.2 hours per working
day. Table 3.4 gives an idea of the distribution of time worked by
different male adults. These results were quite similar to those of
other researchers in West Africa [Luning, 1963; Kohlhatkar, 1965; Mann,
1967; Galleti, Baldwin, and Dina, 1956; Guillard, 1958; Haswell, 1953].
The amount of work undertaken by male adults can be considered
simplistically as a function of a number of factors including;
1. The ability to work, which is a function of health and
2. Incentive to undertake work, which is a function of subsistence
needs and a desire for income over and above that required for subsistence
3. Opportunities for work, in which location will be important
in determining whether farm and/or off-farm work is possible. Opportunities
for farm work will be a function of a number of factors mentioned earlier
in this paper, for example, the indigenous resource base possessed by
the farming family, which will be influenced by physical factors (e.g.,
climate, including seasonality, and soil), biological, and exogenous
factors Ce.g., presence of markets).
An important problem in Hausaland is the seasonal nature of
agriculture which largely restricts crop production to the rainy season and
results in substantial underemployment in the long, dry season. As
a result, considerable time is spent in off-farm employment which is
undertaken both in and out of the village. On the average in the study
areas, 41 percent of the total days worked by a male adult was spent
in off-farm work (Table 3.4). This definition of off-farm work includes
all time spent on activities other than crop production on the family
farm. Therefore, time spent herding animals, working on other people's
farm, etc., would be included under off-farm activities.
Off-farm occupations can be divided into two groups: traditional
and modern. Traditional occupations are defined as the kind of work
that has been undertaken for many generations. In contrast, jobs
in the modern sector are defined as those which have come about directly
or indirectly as a result of improved communications and the development
of large cities, commercial firms, and governmental bodies. The
significance and composition of off-farm employment will depend on a
complex interaction of a number of factors. As the developmental
process begins and communications improve, it is likely that the relative
significance of activities in the modern sector will increase compared
to traditional activities. During the survey years, however, traditional
activities still accounted for 86 percent of the total time spent in
off-farm occupations (Table 3.4).
The seasonal nature of rainfall together with the relatively low
ratio of fadama land means that there is considerable seasonal variation
in the level of agricultural activity.1 The values of the coefficients
De Wilde  was one of the first writers to document the
problems and implications of seasonal bottlenecks.
Table 3.4. TIME WORKED BY FAMILY MALE ADULTS
BY STUDY AREA AND OVERALL AVERAGE
Sokoto Zaria Bauchi Average
Days worked by family male adults per year:
Family farm 159.3 140.1 134.2 144.5
Off-farm: In village 78.3 88.6 97.0 88.0
Outside village 35.4 0.0 0.0 11.8
Total 273.0 228.7 231.2 244.3
Hours per day worked on:
Farm" 5.8 (5.0) 5.1 (4.4) 5.6 (4.7) 5.5 (4.7)
Off-farm 4.8b 5.1 4.2 4.7
Total hours worked per year 1484.3 1166.4 1158.9 1269.9
Distribution of time worked per year in days:c
20 percent worked d
less than 196 (670) 154 (687) 159 (671) 170 (676)
20 percent worked d
more than 340 (1607) 319 (1544) 334 (1468) 331 (1540)
Type of off-farm work (percent of days):
Traditional: Primary 16.4 15.5 30.8 20.9
Manufacturing 27.4 18.9 4.3 16.9
Services 34.4 27.4 39.6 33.8
Trading 7.7 20.4 16.9 15.0
Modern: Services 14.1 17.8 8.4 13.4
aThe figures in parentheses exclude time farmers spent walking to and from fields.
bInclues assumption that, in work outside village individuals, worked the same length of day as
in off-farm occupations in the village. If estimates for work done outside the village are excluded,
the average time worked is 1315.7 hours.
CThe figures in parentheses are hours.
dDistribution of man-hours for Sokoto excludes time worked outside the village.
of variation calculated with respect to the number of man-hours spent
per month on the family farm ranged from 42 percent to 78 percent
(Table A.2). These values are high compared with those from other
areas where there are opportunities for a more even distribution of
agricultural activity throughout the year [Spencer and Byerlee, 1977].
The month in which agricultural activity was at the peak varied
between May and August, although in most cases June and July were the
peak months. An average of 241 man-hours per month was spent on the
family farm during the peak month (Table 3.5). This was 85 percent more
than the average annual monthly input of 130 man-hours. The slackest
month occurred between January and March. The labor input on the
family farm during the slackest month amounted to only 28 man-hours,
which is 79 percent lower than the average annual monthly input. The
disparity in the monthly distribution of labor on the family farm is
emphasized even further by the fact that the four busiest months of
the farming year (i.e., May to August or June to September) accounted
for over 53 percent of the total annual labor input, while the four
slackest months (i.e., December through March) accounted for only 13
percent of the total annual labor input on the family farm. The
seasonal distribution of labor on the family farm for the Sokoto area
is illustrated in Figure 3.2. This seasonal variation gives rise to
two problems, which will be discussed in the following sections: the
labor bottleneck period and provision of gainful employment for the
family throughout the year.
- Man-hours on family farm (100 = 131.5)
---- Man-days on family farm (100 = 26.3)
(a) Total work on family farm.
Female adult (100 = 1.5)
*.-.e Olderchlldren(100 = 11.9)
Mal adults (100 = 92.8)
(b) Man-hours by family members on the family farm.
-*-.e Hired(100 = 25.3)
- Family (100 m 106.2)
I I I I I I I I I I I
Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar.
(c) Family and hired man-hours on family farm.
Figure 3.2 Seasonal Indices of Labor for the Sokoto Study Area
On alarm (100 13.0)
e--* Off farm work (100 9.5)
-- Total (100 = 22.6)
-~ --=.- *'-. '
100 :4 4 :2
'* % ..-. /
(d) Family male adult days.
Off farm work In village (100 6.5)
--- Off farm work out of village, Ie., cin ran (100 = 3.0)
*.- Off farm total(100 9.6)
(a) Family male adult days In off farm work.
Hired laboron family farm (100 = S.S)
-- Family male adult: off farm In village (100 = 5.0)
.*- Family male adult: farm In vllag (100 = 4.5)
Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar.
(f) Average mn-hour per day worked.
Figure3.2 Seasonal Indices of Labor for the Sokoto Study Area (continued)
Table 3.5. INDICATORS OF SEASONALITY OF WORK BY
STUDY AREA AND OVERALL AVERAGEa
Sokoto Zaria Bauchi Average
Four busiest months:
Percent of total man-hours on family farm
Total man-hours on family farm
Percent of total man-hours contributed
by hired labor
Family male adults:
Hours per day worked on farm
Four slackest months:
Percent of total man-hours of family farm
Total man-hours on family farm
Percent of total man-hours
contributed by hired labor
Family male adults:
Hours per day worked on farm
Total man-hours on family farm
Hours per day worked by family
male adults on family farm
aThe months specified in the table do not apply to every village. Where differences arose, the
majority month was listed, or if each month was mentioned equally, the mid-month was selected. Labor
hours exclude time walking to and from fields.
3.2.5 Labor Bottleneck
The amount of land that a family can work during the labor
bottleneck period fundamentally determines the level of agricultural
activity during the rest of the year. The labor bottleneck occurs
during crop cultivation which includes thinning, weeding, and ridging
activities as shown in the histogram constructed for the Sokoto area
(Figure 3.3). The significance of the labor bottleneck period has
been illustrated by linear programming models reported elsewhere
[Ogunfowora, 1972; Norman, 1970] in which estimates of the marginal
productivity of labor during this period were four times higher than
the going wage rate. In summary our research has shown that seasonal
labor bottlenecks are a major restriction on the level of agricultural
activity during the rest of the year. There are at least three
possible ways of modifying this restriction.
1. Increase family labor inputs in the family farm. Family
members could contribute more time by giving up leisure time and
reducing time spent in off-farm activities. Community norms, however,
largely inhibit female adults from contributing much to work on the
family farm. In addition, the labor inputs of older children at present
represent only a small proportion of the total labor input on family
farms. In addition, as education becomes more widespread (i.e.,
through the Universal Primary Education program recently introduced)
it is likely that the labor input of older children will become
smaller. Therefore, it is apparent that family male adults must continue
to provide the major input on the farm. At the moment, they alleviate
the bottleneck period in two major ways. First, male adults work
F Fertilizer application
- Land preparation
Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar.
S Major farming operations
--- Secondary tfrming-operationb
I I I I I a l l I l
Apr. May June July Aug. Sept. Oct Nov. Dec. Jan Feb. Mar.
SA major farming operation was one In which monthly labor expended on that operation constituted
3 percent or more of the total annual labor used on all farming operations.
bA secondary farming operation was one In which monthly labor expended on that operation consti-
tuted between 1.00 and 2.99 percent of the total annual labor used on all farming operations.
Figure 3.3 Composition of work on the family farm by month and operation, Sokoto study area.
harder on the family farm during the bottleneck period. For example,
the average time spent working on the farm during the peak month
was 5.5 hours per day compared with 3.5 hours per day during the
slackest month (Table 3.5).2 Second, by giving up leisure or reducing
work in off-farm employment, male adults allocate more time to their
farms. During the peak month, a male adult spent almost 26 days
working, which is 26 percent more than the 20 days he spent in an
average month during the year. Of that 26 days, almost 73 percent,
or 19 days, was spent on work on the family farm. Even in the peak
farming month, seven days were spent in off-farm work. Therefore,
the potential for substitution between farm work and off-farm work
is perhaps not so great as would be desirable. The major reason may
be that to be moderately successful in the off-farm operation during
the dry season, it is necessary to provide some continuity during the
year. This is particularly true for occupations that involve regular
clientele (e.g., crafts and services such as trading) and for cattle
At first glance, this figure does not seem to be high enough to
constitute a bottleneck. However, it should be noted that this excludes
time farmers spent walking to and from the field and also time devoted
to off-farm occupations that are sometimes undertaken on the same days
as work on the farm. Also, as has recently been well documented at a
Conference on Seasonal Dimensions to Rural Poverty, held in July 1978
at IDS, University of Sussex, U.K., health and nutritional levels are
often at their poorest level during the rainy season when agricultural
activity is greatest.
2The variation in hours per day worked in off-farm employment was,
in fact, lower than in the case of on-farm employment (i.e., 4.7 hours
maximum compared with a minimum of 4.2 hours when viewed on a monthly
basis). This means that the average value of the coefficient of variation
in the hours per day worked on the family farm by male adults was higher
than in off-farm employment (Table A.2). This conclusion is also
illustrated in Figure 3.2 (f).
owners it involved a year round commitment. The importance of
off-farm work for many farmers is further emphasized by the fact
that little income is obtained from farming activities until after
the bottleneck period is over. Cash and food resources tend to be
low at the peak period of farming activities since most crops are
harvested between August and December. Therefore, those farmers
facing severe depletion of cash and food resources would be compelled
to work in off-farm employment even though the needs on their own farms
might be high.
2. Increase the use of hired labor. One would expect that since
labor is in such demand during the labor bottleneck period the bulk
of hired or nonfamily labor would be utilized duirng this period.
It is true that relatively more labor is hired in the peak period.
In fact, 17 percent of the total man-hour input on the family farm
during the peak month was contributed by hired labor and the greater
amount of work undertaken by hired labor during the peak month involved
longer hours and more days. However, in spite of the evidence that
somewhat more hired labor is utilized during the peak period, it is
not so great as expected. There are two possible reasons why more
hired labor is not used in the peak or the bottleneck period. First,
there is no class of landless laborers to fill this demand, thus the
period in which hired labor is most in demand is the time when individuals
are busiest on their own farms. Second, the low level of cash resources
1Delgado , in Upper Volta, through linear programming, found
that the seasonal labor conflicts between crop production and keeping
cattle were irreconcilable if farmers were to maintain their objective
of growing enough millet for home consumption.
during this period imposes a restriction on the amount of labor
that can be hired by farm families. Therefore, it appears that
there is not a great deal of potential for increasing the amount of
hired labor used by farming families.
3. The introduction of improved technology to modify the labor-
bottleneck period. This will be discussed later in the paper. However,
it is pertinent to mention that the possibility of developing improved
technology either to overcome or to circumvent the labor-bottleneck
period has not received the emphasis by researchers that is warranted
in the light of the above discussion.
3.2.6 Underemployment of Family Labor
Farming is likely to remain the major occupation of the bulk of
the rural population for many years to come. Also, it is likely that
family male adult labor will continue to provide the major labor input
in the family farm. However, the seasonal nature of agriculture produces
special problems for employment of family labor. Family labor is
occupied on the farm at certain times of the year, but at other periods
there is little farm work. Therefore, on an annual basis it appears
that labor is underemployed. In Hausaland, attempts have been made by
farming families to overcome this problem by engaging in substantial
off-farm occupations, particularly during the dry season. The results
for the Sokoto area, given in Figure 3.2 (d), illustrate this. However,
the main problem is the lack of sufficient off-farm employment opportunities.
The two main inputs of traditional agriculture are labor and land.
The amount of capital and the proportion of income invested in
traditional agriculture are usually low. Low capital formation in
traditional agriculture may not be due only to a low capacity for
saving but also, in part, to low return on investments. It is, therefore,
not surprising that investment in durable capital was low. Dependency
on hand tools, together with the absence of farm buildings other than
grain stores and an occasional livestock hut, resulted in an average
inventory value of investment in these items of only N8.29 (Table 3.6).
This investment covered only the crop production component of the farming
Livestock, the other major source of investment, is largely
independent of crop production. The investment in livestock (i.e.,
chicken, sheep, and goats, and a few guinea fowl, donkeys, and horses)
was relatively significant in spite of the fact that such livestock
did not play an important role in the farming activities and incomes of
most families. However, livestock is a form of investment that can be
easily translated into cash. The average level of investment in livestock
amounted to N4.17. The largest investment in durable capital was made
by those farmers who owned cattle. The overall average investment in
cattle was N57, giving an average total investment in durable capital
of N79.46. Those farmers who owned cattle derived substantial benefits
from them. However, the level of investment required to possess cattle
NUMBER OF LIVESTOCK BY TYPE AND VALUE OF FARM
CAPITAL BY STUDY AREA AND OVERALL AVERAGE
Sokoto Zaria Banchi Average
Number of livestock:
Cattle: 1.5 (38.2) 3.4 (15.8) 1.2 (12.7) 2.0 (22.2)
Sheep 0.1 2.2 1.1 1.1
Goats 4.6 2.1 2.5 3.1
Poultry 4.7 6.4 2.4 4.5
Guinea fowl 1.4 0.9 0.8
Ducks 0.1 0.1
Donkeys 1.0 0.2 0.1 0.4
Horses 0.2 0.1 0.1 0.1
Inventory value of durable capital
(a) Crop production (buildings,
tools, and equipment) 14.25 4.51 6.12 8.29
Cattle 49.74 95.11 26.14 57.00
Other 17.79 15.62 9.09 14.17
Total 81.78 115.24 41.35 79.46
aThe figures in parentheses represent the average percentage of families owning cattle.
bl Naira (N) = 100 kobo = $1.50.
was relatively high. As will be seen later in the paper, the ownership
of cattle bestows special advantages on families; they have organic manure
for field, substantial sources of supplementary income, etc.
On the average, the cost of capital used in crop production during
the survey year was N23.39 per family (Table 3.7). However, much of this
was an inputed cost and was not translated into cash. Durable capital
costs amounted to N2.91. The remaining capital costs were for seed and
fertilizer. The seed component amounted to only N8.82 per year, with much
of the seed used being saved from the previous cropping year. Inputed
cost of fertilizer was N11.60. Of this, only 65 kobo could be attributed
to the use of inorganic fertilizer. Therefore, most of the fertilizer
applied was in the form of organic manure, which was derived from livestock
owned by the families or through contracts with nomadic Fulani cattle
owners. Under these contracts, the manure produced on the field was usually
considered sufficient payment for the right of the Fulani to graze their
cattle on the residues of the harvested crop. The application of organic
manure has become progressively more important in the attempt to maintain
soil fertility in the face of an increase in population density, while
the traditional means of fallowing has of necessity becomes less common.
The average number of chicken, sheep, goats, guinea fowl, donkeys,
and horses owned by households is given in Table 3.6. Only an average of
22 percent of the farmers actually owned cattle. Farmers with cattle owned
an average of 2.0.
These figures must be treated with caution. This is particularly
true with the input of organic fertilizer. It is difficult to measure
the level of application of organic manure and problems of costing further
compound it [Norman, 1967-72].
3These surveys were undertaken in 1966-68. It is more likely that the
use of inorganic fertilizer has increased in more recent years.
COST OF USING CAPITAL IN CROP PRODUCTION AND
CASH COSTS PER FAMILY BY STUDY AREA
AND OVERALL AVERAGE
Sokoto Zaria Bauchi Average
Estimated cost of using
capital during survey year (N)
Durable 4.13 1.44 3.16 2.91
Seed 8.20 13.59 4.67 8.82
Fertilizer: Organic 25.48 5.40 3.89 11.60
Inorganic 0.03 0.17 0.06
Total (N) 37.84 20.60 11.72 23.39
Total (N) 15.86 25.04 8.33 16.41
Land 3.8 4.4 3.6 3.9
Other durable 25.4 13.1 34.8 24.5
Seed 13.2 (16.4) 11.9 (20.4) 2.8 (6.7) 9.3 (14.5)
Fertilizer 4.9 (6.5) 3.3 (9.0) (-) 2.7 (5.2)
Hiring Labor 49.5 63.9 57.1 56.8
Marketing costs 3.2 8.4 1.7 2.8
aFigures in parentheses refer to
used which was purchased.
average percent of the total value of seed or organic fertilizer
3.4 Cash Production Costs
Cash costs are used to obtain the services of inputs either on a
temporary basis (e.g., renting, pledging, or leasing land, hiring labor,
and purchasing seeds and fertilizer) or on a more permanent basis (e.g.,
purchasing the usufructuary rights to land and equipment). Cash costs
for crop production by families amounted to an average of only N16.41
(Table 3.7). This constituted about 11 percent of the total value of
production derived from crops.
Land, as discussed earlier, is owned by the community; individuals
possess only usufructuary rights to it. For this reason, it was not
considered as a component of durable capital investment. Indeed, even in
cash terms, the expenditure on land is low. Only 4 percent of the cash
expenses was on the average devoted to obtaining usufructuary rights to
the land. Another 24 percent was allocated to durable capital investment,
while nondurable capital accounted for an average of only 12 percent of
total cash expenses. In terms of the two components of nondurable capital,
only 14 percent of the total amount of seed used in crop production was
purchased. In the case of organic manure, the same figure was only 5 percent.
Marketing costs accounted for 3 percent of the total cash expenses. The
insignificance of this is related both to the low proportion of total
production sold (see Section 3.7.3) and to the operation of middlemen
or traders who often purchase products directly from the farmers and
arranged for their transport to the market.
Hiring labor was by far the most important item of cash. expenditure
on crop production; it accounted for an average of 57 percent of the total
cash expenses. The significance of this is apparent from the earlier
analysis in which labor was shown to be limiting at certain times of
the year. The significance of hired labor is further underlined by
a study by King  in Hausaland in which he found that an average
of 74 percent of the credit borrowed under informal loans was used for
hiring labor. It is to be expected that the introduction of yield-increasing
improved technology, without a change in the power base, will involve
an increase in absolute levels of expenditure on nondurable capital
(e.g., improved seed, inorganic fertilizer) and a substantial increase
in expenditure on hired labor. Institutional credit programs have often
given credit for both durable and nondurable capital. In order to try to
safeguard against misuse of such credit, these items have often been given
in kind. Because of possible abuse of funds given in cash, and perhaps
also because of a lack of recognition of the increased labor inputs that
occur through the use of improved technology, such credit programs have
often not included provision for hiring extra labor. This omission may
well become an important constraint on the adoption of improved technology.
3.5 Land and Labor Relationships
Since hand labor was the only power source, the relationship between
land and labor was important in determining the intensity of agriculture.
The average labor input was 622 man-hours per cultivated hectare. It
is reasonable to hypothesize that the amount of labor used per hectare
will be inversely related to the number of cultivated hectares on the
farm. The relationship is complicated by other factors such as the
quality of land. Two possible indicators of quality of land are the
proportion of cultivated land that is fadama and the amount of organic
manure applied per hectare. It is hypothesized that the higher the
quality of land, the greater will be the number of man-hours devoted
to it on a per hectare basis. Regression models verifying the above
relationships are given in Table A.4, while graphs constructed from
them are shown in Figure 3.4. As can be seen from the functions
estimated, the level of family input per cultivated hectare decreased
more rapidly than total man-hours per cultivated hectare as the number
of cultivated hectares increased. The difference can, of course, be
attributed to the use of hired labor. The total number of man-hours
of hired labor was shown to be significant, positively related to the
number of cultivated hectares. However, this significant relationship
did not hold when hired hours were expressed per cultivated hectare.2
This means that the use of hired labor could only partially offset the
decrease in family labor inputs per cultivated hectare as the number
of cultivated hectares increased.
3.6 Cropping Systems
3.6.1 Crops Grown
The cropping systems that have evolved in Hausaland reflect the
end result of an interaction of the physical, biological, exogenous, and
endogenous factors. A total of 29 crops, differing greatly in significance,
was grown in the study areas. Cereal crops accounted for almost 60 percent
The partial correlation coefficient between the logs of these
variables, controlling for the two land quality variables, was 0.31, which
was significantly different from zero at the 5 percent level.
2The partial correlation coefficient between the logs of this variable
and cultivated land, controlling for the land quality variables, was 0.05,
which was not significantly different from zero at the 5 percent level.
er \ Total man-hours per hectare
250 Family man-hours per hectare
II I II I I
I 2 3 4 5 6 7
S Number of cultivated hectares
per < Total man-hours per hectare
hectare -_ -..-
Family man-hours per hectare
I II II 1 I
S 2 3 4 5 6 7
Number of cultivated hectares
(b) Gidan Karma
a Constructed from functions estimated in Table A.4.
b X2 and X3 held at means, i.e.,X2 = 0.1455, X3= 0.7610
c X2 and X3 held at means, i.e.,X2 =0.0150, X3 = 1.0660
Figure 3.4 Relationship between labor input per cultivated
hectare and number of cultivated hectares.
of the total adjusted acres and grain legumes for another 24 percent.
Starchy roots and tubers made up 6 percent, while vegetables, sugarcane,
and nonfood crops accounted for the remainder.
The results given in Table 3.8 indicate that the major crops grown
on gona land are millet (25 percent of the adjusted hectarage), sorghum
(30 percent), cowpeas (16 percent), groundnuts (9 percent), and cotton
(3 percent). Millet and sorghum constitute the major food crops grown
by farmers in Hausaland [Simmons, 1976a]. The major crops grown on the
fadama land are rice (4 percent) and sugarcane (2.5 percent). Cassava
(4 percent) is grown on both gona and fadama land.
3.6.2 Mixed Cropping
On the average, only 26 percent of the cultivated land was sole
cropped. The remaining land was primarily devoted to crop mixtures,
i.e., two or more crops grown on a given piece of land at the same time.2
Because of the shortness of the rainy season, double cropping was precluded
on the gona or rainfed land. Therefore, it has been suggested that in
A definition of adjusted hectares appears in Table 3.8. The use of
adjusted hectares has been legitimately criticized [Matlon, 1979]. Although
it is simple to calculate, the bias inherent in its calculation is likely
to underestimate the significance of the dominant crops such as cereals.
The different crops may be together for a short or a long time.
Such a characteristic has made an acceptable definition of crop mixtures
a contentious issue. For the purposes of this paper, any degree of over-
lapping in terms of time was considered to be mixed cropping.
Double cropping, however, is possible on fadama land and was practiced
to a minor extent. Six crop enterprises involving double cropping were
identified, but these accounted for only 1.2 percent of the total cultivated
land. There were, in addition, a few other minor crop enterprises which
consisted of a combination of double and mixed cropping.
order to maximize the return per hectare per year, it is best to
grow crops in mixtures [Dalrymple, 1970].
A total of 23 crops was grown in sole stands. In addition, a
total of 230 different crop mixtures was identified. These mixtures
did not take into account differences in planting patterns or plant
population densities. Although the number of crop mixtures identified
was very large, 53 percent of the total cultivated area in fact was
accounted for by six crop enterprises as shown in Table 3.8. Of these
mixtures, millet/sorghum and millet/sorghum/cowpeas were by far the
The crop mixture index in Table 3.8 gives some idea of the relative
number of crops grown in the mixtures. Although for each crop mixture
many different spatial arrangements are possible, it was found that
certain arrangements of crop constituents were most popular, particularly
within a given village [Norman, 1967-72; Norman, 1974b; Norman, Fine
Goddard, Pryor and Kroeker, 1976]. In general, systematic planting
patterns are followed. On gona land, crops are usually planted in
ridges one meter apart, while on low-lying fadama land crops tend to
be planted on the flat.
Table 3.8 shows that some crops more than others are grown in sole
stands. The major factor underlying the value of growing different
species in mixtures depends on whether the relationship between them
is competitive or complementary [Andrews, 1972]. When the relationship
is complementary, it is likely that the different species will be grown
together in a mixture. Complementarity will be enhanced when one or
more of the following characteristics offset the competitive relationship
Table 3.8. TYPE AND ACREAGE OF CROPS AND CROP ENTERPRISES:
BY STUDY AREA AND OVERALL AVERAGEa
Sokoto Zaria Bauchi Average
Adjusted hectares grown:b
Millet and late millet 130.6 (4.5) 78.7 (2.0) 62.2 (25.4) 90.5 (10.6)
Sorghum 70.0 (3.0) 114.0 (26.6) 143.2 (57.0) 109.1 (28.9)
Rice 4.7 (31.6) 30.9 (73.4) 8.5 (100.0) 14.7 (68.3)
Groundnuts 8.5 (27.7) 42.5 (15.7) 36.8 (49.9) 29.2 (31.1)
Cowpeas 108.1 (0.0) 43.7 (2.1) 26.0 (4.3) 59.2 (2.1)
Cassava 20.7 (69.0) 9.3 (65.3) 7.6 (95.1) 12.5 (76.5)
Red sorrel 20.2 (0.0) 0.4 (0.0) 6.9 (0.0)
Sugarcane 2.4 (90.3) 23.4 (97.7) 3.0 (82.5) 9.6 (90.2)
Cotton 0.2 (81.8) 37.4 (31.3) 0.2 (100.0) 12.6 (71.0)
Total (all crops) 409.5 397.0 299.2 368.6
Percent of cultivated hectares
Sole cropped 9.1 23.0 46.2. 26.1
Value of mixed cropping indexc 2.73 2.43 1.75 2.30
Major crop enterprises (hectares):d
Sorghum 2.1 30.3 81.6 38.0
Sugarcane 2.2 22.8 2.5 9.1
Millet/sorghum 3.3 93.1 62.4 52.9
Millet/cowpeas 65.2 21.7
Millet/sorghum/cowpeas 142.3 14.0 19.2 58.5
red sorrel 48.5 16.2
Total number of crop enterprises 75 200 60 111.7
aThe figures for hectares are aggregates for each area; those in parentheses represent the per-
cent of the total adjusted hectarage each crop grown sole. For the overall average each area was
bOnly crops for which more than 20 adjusted hectares were grown in at least one study area were
included in the table. The calculation of adjusted hectarage was necessary because of extensive use
of mixed crops. The hectarage of each crop in the mixture was calculated by dividing the hectares
the crop mixture by the number of crops in the mixture. For example, a 2-hectare millet/sorghum mix-
ture was recorded as 1 hectare of millet and 1 hectare of sorghum.
cDetails on the method of calculating the mixed cropping or intercropping index are given
elsewhere [Norman, 1967-72]. A higher value indicated the preponderance of more crops in the mixture.
dOnly crop enterprises for which more than 20 hectares were grown in at least one study area were
included in the table.
between the species under consideration: different growth cycles,
different water and soil nutrient demands, different rooting habits,
symbiotic relationships between different species, differential
labor demands and practices, etc.
As examples of the above, sugarcane and rice are usually grown
as sole crops on fadama land. The potentially harmful shading effect
of the tall dense stands of sugarcane limits the value of growing other
crops in a mixture with it. Rice is often not planted in rows;
therefore, weeding, cultivation, and harvesting would be complex if other
crops were grown in a mixture with it. As shown in Table 3.8, millet
and cowpeas are usually mixed on gona land. Millet is harvested in the
middle of or before the end of the growing season, while cowpeas are
not planted until well after the beginning of the rainy season. Also,
millet is very commonly grown in mixtures in which another major constituent
is sorghum. Millet matures early and thus complements the growth cycle
of the long-season sorghum. It also has a rooting habit complementary
to sorghum [Andrews, 1972, 1974]. Another justification for growing
the nonsprayed cowpeas in mixtures is some evidence that insect damage
is thereby reduced [Institute for Agricultural Research, 1972].
3.6.3 Justification of Mixed Cropping
When asked why they grew crops in mixtures, farmers gave a number
of reasons. The major reason could be interpreted as the need to
maximize the return from the most limiting factor. Such a reason is
consistent with the goal of profit maximization. Fewer farmers gave
the need for security as their main reason for growing crops in mixtures.
However, in addition, a number of farmers mentioned the fact that it was
Table 3.9. COMPARISON OF SOLE AND MIXED CROPS ON
GONA LAND BY STUDY AREA
Sokoto Zaria Bauchi
Soleb Crop Sole Crop Sole Crop change from Sole
Crops Mixtures Crops Mixtures Crops Mixtures to Crop Mixtures
Annual 425.6 485.4 362.1 586.1 564.6 597.2 27.2
Labor peak period 232.4 237.9 122.3 157.8 247.2 247.2 10.5
Millet 736 686 366 727 393 -26.4
Sorghum 652 122 786 644 839 729 -37.5
Groundnuts 429 188 587 412 392 217 -43.5
Cowpeas 56 132 52
Value of production (N)
Hectare 31.65 40.80 38.00 61.36 29.50 33.73 34.9
Annual man-hour 0.06 0.12 0.13 0.11 0.08 0.08 28.2
Man-hour during peak
period 0.13 0.32 0.35 0.42 0.24 0.25 56.8
Net return (N/ha), with
Not valued 30.74 38.94 36.79 59.48 30.74 33.76 34.9
Costing hired labor only 28.27 36.13 33.41 54.02 28.64 31.18 32.8
All costed 17.96 24.36 18.31 28.37 14.80 18.68 41.2
aThe weighting system used in deriving the figures used for comparison in the table is discussed
elsewhere [Norman 1974b].
bCassava was one of the sole-crop enterprises. Since it is also a fadama crop, it does not appear
as a constituent of any of the crop mixtures used in the analysis. It is a different type of crop from
other rainfed crops, and has a different labor distribution.
CThe labor figures exclude time travelling to and from fields and that also involved in threshing
or shelling the crop.
dpeak periods were June to August in Sokoto; June and July in Zaria; and July to September in
Table 3.10. VALUE OF PRODUCTION FROM SOLE AND MIXED CROPS
GROWN ON GONA LAND BY STUDY AREAa
Sokoto Zaria Bauchi
Value of Sole Crop Sole Crop Sole Crop
production (N) per Variable crops mixtures crops mixtures crops mixtures
Median 30.15 40.03 36.08 59.11 29.18 31.38
Hectare Interquartile.range 6.18-44.48 27.43-54.61 21.10-53.40 37.26-83.92 13.49-41.56 2.10-48.94
L or M 29 33 24 17 43 42
Median 0.06 0.10 0.07 0.09 0.06 0.07
Annual man-hour Interquartile range 0.01-0.11 0.07-0.16 0.03-0.17 0.07-0.14 0.03-0.08 0.04-0.10
L or M 13 33 32 48 44 38
Median 0.08 0.23 0.29 0.33 0.12 0.16
Peak period man-hour Interquartile range 0.02-0.20 0.12-0.36 0.09-0.43 0.20-0.65 0.07-0.20 0.09-0.29
L or M 14 33 44 40 40 40
aAbalu  has shown for the Zaria study area that crop mixtures contribute to income stability
bL = percentage of the crop mixture observations that were less than the median (50 percent observation) for sole crops. M =
percentage of the sole crop observations that were more than the median (50 percent observation) for crop mixtures. The L value
appears under sole crops and the M value under crop mixtures.
traditional to grow crops in mixtures. Probably the desire for
security in fact accounted for the traditional popularity of mixed
The question addressed in this section is how much justification
there is for the reasons given by farmers for growing crops in mixtures.
The following observations are derived from the results presented in
Tables 3.9 and 3.10.
1. A major input in traditional agriculture is labor. On an
average, the annual labor input per hectare from growing crops in mixtures
was 27 percent higher than that from growing crops in sole stands.
However, this differential was reduced to 10 percent when only labor
during the peak farming period was considered.
2. For areas in which crops were grown in both sole and mixed
stands the average decrease in yield of individual crops when grown
in mixtures varied from 26 percent to 43 percent. Possible reasons for
these lower yields include competition with other crops in the mixture
for water, light, and nutrients and the lower population density of an
individual crop when planted in mixtures.
3. When the yields of individual crops were expressed in terms of
a common denominator such as money, the average value per hectare of
crop mixtures was 35 percent higher than the value of sole crops. In
addition, although the annual labor input from growing crops in mixtures
lone deficiency in the analysis was lack of data ascertaining whether
there were significant differences in the soil feriliity of land devoted
to sole and mixed crops. Casual observation indicated that there was
no significant difference, but this was not verified by direct measurement.
was higher than sole crops, the return from growing crops in mixtures
per annual man-hour was 28 percent higher than from growing crops in
sole stands. Moreover, when labor applied during the labor bottleneck
period was considered separately the return per man-hour during the
labor bottleneck period was 50 percent higher for crop mixtures. It
appears, therefore, that mixed cropping helps alleviate labor bottlenecks.
Linear programming results provide additional empirical support for mixed
cropping [Ogunfowora and Norman, 1973].
4. The net return per hectare was 32 percent to 41 percent higher
for mixed cropping depending on how labor was costed.
5. Finally, the results indicate that growing crops in mixtures
gave more dependable return. This is not surprising since crop species
in a given mixture are likely to react differently to variations in
weather and insect and disease attacks.
In summary, the reasons given by farmers for growing crops in
mixtures were verified by the results presented above. The implications
for introducing improved technology, in the light of the above findings,
are important. Mixed cropping using indigenous technological methods
proves to be rational and well adapted to both the technical and the
human elements. Experimental evidence is accumulating which indicated
that mixed cropping under improved technological conditions may also
be more rational in terms of either a profit maximization or a security
goal, provided that a change in the power source is not envisaged
[American Society of Agronomy, 1976; Baker, 1974; Baker and Yusuf, 1976;
Kassam, 1973; Kass, 1978].
Traditionally, improved technology has been developed specifically
for sole cropping. The increasing research emphasis on mixed cropping
at the IAR is justified by the results presented in this paper. Additional
support for mixed cropping is provided by the findings of a recent
study in the Kano area by Edache . He strongly supports the
introduction of improved technologies incorporating mixed cropping into
the National Accelerated Food Production Program (NAFPP) recently
initiated in Nigeria [International Institute for Tropical Agriculture,
The average disposable income during the survey year was almost N2001
per farm family (Table 3.11).2 However, this should be regarded with
caution since there are many problems in measuring and interpreting
income figures.3 Bearing in mind the problems of measuring income, it
This income figure refers to the 1966-68 period. The composite price
index (1960 = 100) for low income families was 131 in 1966 and 348 in 1976.
Although attempts were made to estimate incomes derived by women in
independent economic activities, it is likely, because of the inability to
monitor their activities adequately, that these were underestimated [Norman,
Fine, Goddard, Pryor and Kroeker, 1976]. Simmons [1976b] in a later survey
found women in the Zaria area villages earned about N4.15 per month in cash.
Care needs to be taken in interpreting income figures particularly with
respect to differences among areas. The reasons for this include the
a. The studies in different areas were undertaken in different years;
therefore, the figures are not completely comparable.
b. The figures reflect income and do not indicate the cost of living.
For example, it is likely that the cost of living is higher in the Sokoto
area than in the Bauchi area, thereby accounting in part for the lower levels
of income in the latter area.
c. Much of the information necessary for calculating incomes must be
derived from variables that do not enter the market system (e.g., many
Table 3.11. INPUTS AND FARM INCOME
BY STUDY AREA AND
Sokoto Zaria Bauchi Average
Inputs per cultivated hectare:
Man-hoursa 572.8 716.6 582.2 623.7
Organic manure tonness) 3.71 2.71 0.53 2.31
Farm income (N):
Gross 160.68 199.11 87.88 149.23
Costs of production 23.60 34.42 13.19 23.74
Net farm income from crops 137.08 164.69 74.69 125.49
Other than cattle 3.28 3.08 1.35 2.57
Cattle 34.68 56.64 22.93 38.08
Net income from livestock 37.96 59.72 24.28 40.65
Total income (N):
Net income from crops and livestock 175.04 224.41 98.97 166.14
Other off-farm income 44.47 39.61 35.46 39.84
Taxes 6.49 8.27 6.68 7.15
Disposable income 213.02 255.75 127.75 198.83
Net farm income from crops (N) per:
Cultivated hectare 42.25 57.40 30.91 43.51
Man-hours of family labor 0.17 0.12 0.07 0.12
Disposable income (excluding taxes)
per consumer unit from (N):
Farm: Crops 34.15 27.74 17.87 26.59
Off-farm: Other than livestock 11.58 8.83 9.19 9.87
Livestock only 9.66 8.11 5.34 7.70
Return per man-day family labor (N):
Net farm income from crops 0.70 0.52 0.32 0.51
Off-farm income 0.44 0.37 0.34 0.38
aExcludes farmers' time travelling to and from fields.
Includes only that manure explicitly paid for.
CThat is, excluding livestock.
dThis figure represents a return to the farmer and the family for their labor, management, and
capital after taxes are paid.
Table 3.12. PRODUCTION FUNCTION FOR VALUE OF PRODUCTION
DERIVED FROM CROP PRODUCTION
IN ALL STUDY AREASa
(standard Estimated Opportunity
Independent Variables error) at MVP Costs
Gona Log X1 0.3407 7.18 79.29 11 to 75
Fadama Log X2 0.0797 0.64 208.08 35 to 238
Man-hours of work by:
Family Log X 0.2804 1294.7 0.36 0.00 to 0.37
Hired Labor Log X4 0.0759 266.2 0.48 0.37
Fixed costs Log X5 0.0616 42.70 2.40 1.05-1.70
Variable costsc Log X6 0.1833 113.70 2.69* 1.05-1.70
Sl X7 0.2046
52 X8 0.1231
VI X9 -0.0297
V2 X10 0.0953
R = 0.8937
Syx = 0.1586
aA Cobb-Douglas function was estimated. Where relevant the variables were estimated in shillings
(i.e., the currency at the time). 10 sh. = N1. The marginal value productivities were estimated at
the mean values derived for the overall sample. The value of the dependent variable estimated using
these means was 1671 shillings (i.e. N167.00). N = 340.
bFor the derivation of the opportunity costs, see Norman [1967-72] and Norman et. al. [1976c].
CExcludes time travelling to and from fields.
dThe variable costs excluded funds expended for hired labor which were accounted for in variable
eFor the definition of the dummy variables see Table 3.3, footnote b.
*Significantly different from the opportunity cost at the 5 percent level.
would appear from the results in Table 3.11 that on the average the
income derived from crop production amounted to 63 percent of the total
disposable income while livestock contributed 20 percent. However,
it is important to note that 94 percent of the livestock component
was contributed by cattle. Off-farm income also was significant,
amounting to 19 percent of the total disposable income.
3.7.2 Production Function Analysis and Net Farm Income
The results for the Cobb-Douglas production function estimation of
the income derived from crop production (Table 3.12) show that farmers
in general were allocating resources to crop production in a manner
consistent with the goal of profit maximization. The marginal value
products of hired labor and fixed costs, although higher, were not
significantly different from their opportunity costs. However, in the
case of variable costs, the results appear to indicate that too little
of the inputs are provided from farm or family sources, while much of the
product produced is consumer in the house).
d. Special problems with respect to c include organic fertilizer
and money from off-farm occupations. In the case of organic fertilizer,
it was difficult to obtain accurate measurements of the quantities used;
in addition, there is no established market price. It is obvious that
the utilization of organic fertilizer becomes much more significant as
population density increases and the potential for fallowing land decreases.
Organic fertilizer other than that which was purchased with cash or
payment in kind was therefore omitted from the income figures. This
results in a distortion in income figures, particularly when different
areas are compared. Money earned from off-farm occupations also proved
to be difficult to measure. Therefore, the income figures in this paper
should be treated with caution.
1Disposable income in this discussion refers to income before the
subtraction of taxes (Table 3.11).
2The conventional approach has been used in estimating and analyzing
the production function, although we recognize that its validity can
be questioned (e.g., no farmer exists who has levels of resource utilization
at the mean levels given in Table 3.12).
non-durable capital was in fact being utilized since the marginal
value product was significantly higher than the opportunity cost.
Unfortunately, in our research we did not record the cash flows of
farming families in detail throughout the agricultural cycle. However,
evidence from other studies undertaken in Hausaland has indicated
that there is indeed a lack of adequate cash, particularly during the
period between the onset of the rains and the harvest of the first
crop (usually millet) [King, 1976; Matlon, 1979]. The results for
the production function estimation indicate that the returns to scale
were virtually constant (i.e., 1.02).
An attempt was made to ascertain the determinants of net farm
income per cultivated hectare and per man-hour of work on the family
farm. The results of the models are given in Table 3.13. When dummy
variables reflecting location within an area and study area were
incorporated, the major determinant of the dependent variables was
the intensity with which land was being farmed. Results indicated that
a higher level of man-hours per cultivated hectare resulted in a higher
net farm income per cultivated hectare. However, in the case of net
farm income per man-hour work on the family farm, there was a negative
relationship to the number of man-hours per cultivated hectare. This
is consistent with expectations, since one would expect progressively
decreasing marginal productivity of labor on a given piece of land
as the intensity of labor input increases.1
Alternative models with variables reflecting the quality of land
(i.e., manure input and the proportion of the land that was fadama)
and total cultivated hectares were also estimated. With these variables,
inferior but consistent results were achieved. This is not surprising
since the variables were earlier found to be strongly related to the
man-hours spent per cultivated hectare on farm work (Table A.4).
Table 3.13. DETERMINANTS OF THE RETURN PER CULTIVATED
ACRE AND PER MAN-HOUR FROM CROP PRODUCTIO)Na
Net farm income per
Net farm income per man-
hour of work onbthe
Constant 114.6481 0.8823
Total man-hours per b
cultivated hectare XI 0.2798* (0.0581) -0.0010* (0.0001)
Sl X2 224.3380 (23.5953) 0.3743* (0.0531)
S2 X3 114.3639* (24.7253) 0.2981* (0.0556)
V1 X4 -15.0069 (24.8847) -0.1437* (0.0560)
V2 X5 54.8731 (12.4039) 0.0272 (0.0279)
R 0.7087* 0.5319*
Sy 181.0422 0.4074
aThe income figure was expressed in shillings, in the currency at the time of the survey. N = 10
shillings. For the definition of the dummy variables see Table 3.3. N = 340.
bExcludes time travelling to and from fields and in threshing.
3.7.3 Market Orientation
Conventional wisdom is that farmers in an area such as Hausaland
will have as one of their major goals production of the food necessary
for subsistence. Only when this goal is met are they likely to devote
additional surplus resources to enterprises that result in products
that can be sold on the market. The results in Table 3.14, however,
indicate that an average of only 63 percent of the farmers were self-
sufficient in cereal production, which is the major consumption item
[Simmons, 1976a]. If allowance is made for errors of estimation, it
would appear that perhaps up to 50 percent of the farmers were not
self-sufficient in cereal production. At the same time, however,
there was considerable variation in terms of the relative degree of
self-sufficiency, so that, on the average, a farming household would
appear to produce 1,800 pounds of surplus grains.
It was estimated that on the average 24 percent of the total value
production of products produced on the farm was in fact marketed.
The degree to which each crop produced is marketed of course depends
on whether it is a subsistence crop (e.g., millet, sorghum) or
primarily a cash crop (e.g., cotton, sugarcane, calabash).2 Because of
variations in technical elements it is likely also that certain crops
1Matlon  has found that occasionally this goal has to be
modified due to economic necessity.
In the northern Nigerian context, the term "cash crop" has
traditionally meant those crops that are marketed through marketing boards
and usually are destined for the export market. Groundnuts and cotton
are in this category. However, sugarcane and calabash, which are usually
marketed, can, for the purpose of this paper, also be considered cash crops.
Tabl e 3.14.
ESTIMATES OF SELF-SUFFICIENCY AND
PERCENTAGE OF CEREALS MARKETED BY
STUDY AREA AND OVERALL AVERAGE
Sokoto Zaria Bauchi Average
Degree of self-sufficiency in
cereals--percent of farmers:
Not self-sufficient 17.1 61.9 32.1 37.0
Less than 75%-of needs 13.8 35.3 20.5 20.5
75-125% of needs 13.9 40.2 28.8 28.8
125-200% of needs 28.7 17.9 23.5 23.6
More than 200% of needs 43.6 6.6 27.2 27.2
Average quantity cereals produced
above consumption needs (kg.) 1427 268 740 812
Estimated proportion of production
All products 10.1 38.8 NA 24.4
Cereals: millet 4.9 9.3 NA 7.1
sorghum 3.8 8.6 NA 6.2
Cereals as a percentage of total
value of production 76.0 51.2 79.6 68.9
alt was assumed that
was about 180 kg./capita.
[1976a] plus an allowance
the .total domestic consumption of cereals
This is based on figures found by Simmons
for wastage and seed.
bConsiderable problems were encountered in obtaining accurate
estimates of production marketed [Norman, 1967-72; Norman et al.,
1976a]. It is likely that the production marketed was underestimated.
More accurate estimates of the proportion of cereal production marketed
in the Zaria study area are given in a study by Hays .
in some areas may be both a cash and a food crop (e.g., millet and
sorghum in the Sokoto area). The revenue obtained from selling farm
products on the market is available for the purchase of food and other
items and for investment in the farming system. However, another important
source of cash is income derived from off-farm activities. One
interesting point in Table 3.11 is that the return per man-day of family
labor spent on the farm was greater than from that spent in off-farm
activities. This is not surprising since, as was indicated earlier,
much off-farm employment takes place at times of the year when the
opportunity cost of labor is low, especially during the dry season.
3.7.4 Income Distribution
The Gini coefficients given in Table A.5 for net farm income, when
compared with those derived for land distribution in Table A.1, indicate
that the former is generally more unequally distributed than the latter.
This implies that the land is being used at varying degrees of intensity,
as confirmed by findings discussed in other parts of the paper (see Table
A.4). The implication is that a relative shortage of land can be offset
to some extent by increasing the proportion of other traditional inputs,
particularly labor. This is possible even using the indigenous or
traditional types of technology. The potential for this, of course,
increases greatly with the introduction of land-intensive improved technology.
However, in the case of villages where fadama land was more dominant, it
As mentioned earlier (Section 3.7.1), a major limitation of the
studies reported in this paper is that it proved impossible to derive
a complete record of earnings achieved by women. Since these earnings
are primarily from off-farm activities, the figures on off-farm incomes
are likely to be correspondingly underestimated.
appeared that land was more unequally distributed than net farm
income. In results presented elsewhere [Norman, Fine, Goddard, Pryor
and Kroeker, 1976c] it was shown that there is less variation in the
manpower input per cultivated hectare of fadama land than for gona
land. Also, there is likely to be less variation in the quality of
such land than there would be for gona. It appears that these factors,
plus the necessity of a much higher initial input of labor per cultivated
hectare of fadama compared with gona, means that the potential for farming
fadama land at differing degrees of intensity is more limited.
The results in Table A.5 also indicate that the distribution of
disposable income including that derived from off-farm work, but
excluding that from cattle, is more equal than that from crop production.
This gives rise to the interesting implication that off-farm income
earnings can and do compensate to some extent for low net farm incomes.
This emphasizes even more the rationality of farmers in Hausaland who
have recognized the complementarities of off-farm and farm work.
4. COMPARATIVE ANALYSIS OF TRADITIONAL FARMING
IN THE SOKOTO, ZARIA, AND BAUCHI AREAS OF HAUSALAND
Since the nine villages in the three areas were not randomly selected,
it was not possible to do any meaningful statistical analysis of the
differences in farming in the three areas. Therefore, the observations
in this section should be considered as preliminary and as possible trends
rather than as statistically verified conclusions. In addition, it is
difficult to measure the effects of the various factors, such as population,
location, and climate on the productivity and profitability of farming.
For example, the population density is lower in Bauchi than in Sokoto,
while the climate, particularly as it affects the growing season, is harsher
in Sokoto than in either Zaria or Bauchi. Therefore, the following analysis
is advanced with a great deal of caution.
4.1 Effect of Population Density on Farming
Perhaps somewhat surprisingly we found that the farm size in the
three study areas was very similar (Table 3.1). However, the proportion
of fallow land differed greatly, decreasing as population density increased.
Two factors may account for this phenomenon. It appeared in general that
all farm land and cultivated land were more evenly distributed in the
more densely populated areas (see Table A.1). This implies that the
opportunity cost of leaving land fallow in such areas was relatively
high, encouraging farmers who have surplus land to surrender the
usufructuary rights to those who are short of land. The figures
in Table 3.1 indicate that, although much of the land in all three
areas was still inherited, more mobile types of tenure were apparently
being used in the Sokoto and Zaria areas than in Bauchi, contributing
to more even land distribution.
Although the land per resident and per consumer unit was on the
average highest in Sokoto (Table 3.2), it appeared that the lower
proportion of fallow land was apparently due to the poor fertility of
the soil. Therefore, the period of fallowing is progressively shortened
because of increasing pressure to produce food and, because of the
technical problem of fertility. Attempts were made to maintain the
fertility of the land through more intensive applications of manure
per cultivated hectare (Table 3.11). Unfortunately, it was not possible
to ascertain whether the manure application completely offset the decrease
in the length of the fallow period.2
4.2 Effect of Climate on Farming
In Sokoto where the growing season is much shorter, larger areas
were cultivated per consumer unit compared to the other two areas. Thus,
more days were spent on farm work and longer hours per day by male adults
(Table 3.4). This work, when expressed in man-hours, was more concentrated
1Lagemann obtained empirical evidence, in a study in eastern
Nigeria, that yields and length of fallow were positively related.
Figures indicating the net farm income per cultivated hectare in
Table 3.11 are not good indicators of this relationship. The reasons are
that there are complications due to differences in climate, crops grown,
costs, prices and labor inputs. Also, there was a lack of reliable,
field-specific knowledge on soil fertility and sometimes length of fallow
period, when the field was last fallowed, etc.
seasonally than in the other areas (Tables 3.5 and A.2). As a
result, off-farm employment was also more unevenly distributed, being
concentrated primarily in the dry season. Off-farm employment in
Sokoto was higher in part because an average of 45 percent of the
days worked off-farm were spent in work undertaken outside the village
during short-term migration (cin rani). As would be expected, this
short-term migration took place primarily during the dry season (Figure
In comparing the cropping systems in the three areas, it is
apparent that they are adapted to differences in the technical element.
Millet, sorghum, cowpeas, and groundnuts were common in all three
areas (Table 3.8). However, millet and cowpeas, which require a shorter
growing season, were dominant in the harsher climate of Sokoto, while
sorghum and groundnuts were relatively more important in the more
favorable areas of Zaria and Bauchi. Farmers grow more, millet and practice
more mixed cropping in Sokoto in order to offset the risk of crop failure.
In fact, millet was included in 66 percent of the crop enterprises
identified in the Sokoto area compared to 30 percent of the crop enterprises
in the Zaria area.
The two most common mixtures in all three areas were millet/sorghum,
especially in the Bauchi and Zaria areas, which are similar climatically,
and millet/sorghum/cowpeas, which was more dominant in the Sokoto area.
1The relatively higher population density in the Sokoto and Zaria
areas, together with the practice of cin rani in the former area [Norman,
Fine, Goddard, Pryor and Kroeker, 1976], accounted for the higher proportion
of modern services, which are better remunerated, in those areas than
in Bauchi (Table 3.4).
Table 4.1. COMPARISONS BETWEEN TWO MIXED-CROPPING
ENTERPRISES IN THE SOKOTO
AND ZARIA AREAS
Man-hours per hectarea
Number of stands/hectare
Ratio of millet to other stands
Value of production (N) per:
aExcludes time travelling to and from fields and for threshing.
A comparison of these two crop mixtures in the Sokoto and Zaria areas
is in Table 4.1 which reveals the following:
1. In the drier area of Sokoto, the average number of plant
stands per hectare was much lower than the other two areas. This
reduction shows how farmers in Sokoto adapt to low soil moisture and
greater variability of rainfall at the beginning and the end of the
growing season. It may also indicate a response to the lower fertility
of soil compared with the Zaria area.
2. Farmers in Sokoto grow a much higher proportion of millet in
crop mixtures than in the other areas. This presumably is due to the
comparative advantage that millet enjoys in the Sokoto area compared
with Zaria, where other crops can be grown more satisfactorily.
3. The yield per stand of millet was much higher in the Sokoto
area, while the yields per stand of other crops were correspondingly
lower. However, although the grain yield of other crops in the mixture
might be lower, these crops can still have considerable economic value
to the farmer. This applies particularly to cowpeas, the haulm of
which provides food for livestock.3
The number of stands was measured rather than the number of plants.
The number of plants per stand varied according to species and location.
Competition between plants in one stand is likely to be great, therefore
reducing the yield per plant compared with the yield if there were only
one plant per stand.
Lagemann  expressed a different point of view, concluding
that in the much wetter area of Eastern Nigeria, farmers tend to plant
more densely when the soil fertility declines. However, the potential
for soil moisture stress is much higher in Hausaland than in eastern
Since it was impossible to measure the quantity of forage produced,
estimates of the value of this product were omitted from both the analysis
of individual crop enterprises and from the income figures.
4. The higher number of stands per hectare combined with the
greater yield per stand resulted in a higher yield of millet per
hectare in Sokoto compared with Zaria.
5. The overall value of production per hectare was lower in the
Sokoto area than in the Zaria area, although the return per man-hour
was similar in the two areas, partly because the man-hour input per
hectare was lower in Sokoto.
4.3 Self-Sufficiency and Incomes Among Areas
On a per family basis, the results indicated that families in the
Zaria area derived the highest incomes from farming and had the highest
disposable incomes, while families in Bauchi had the lowest incomes
(Table 3.11). There are two reasons that may contribute to the low
incomes in the Bauchi area. First, market prices for crops were lower
than in the other areas. For example, the average market price for
millet and sorghum in the Bauchi area was 24 percent less than in the
Sokoto area and 40 percent less than in the Zaria area. Therefore, it
is not surprising that the gross income derived from crop production in
the Bauchi area was much less than in the other two areas; this was also
reflected in lower net farm incomes. The lower prices of millet and
sorghum would also presumably reflect lower costs of living in this
area compared with the other two areas. Second, there was a much lower
proportion of cultivated land inBauchi devoted to established cash-crop
production than in the similar ecological zone of Zaria (Table 3.8).
1However, for reasons mentioned in Section 3.7.1, caution needs
to be observed in interpreting comments in this section.
Farmers in the Sokoto area were in general the most self-sufficient
in terms of cereal-crop production. In the Sokoto area, cereals are
used as both a subsistence and a cash crop, while in the Zaria area
other crops are produced for the market. It is apparent that Zaria
farmers market more of their products, partly as a result of the established
guaranteed prices for some of the cash crops (Table 3.14). Unfortunately,
the data collected did not permit a detailed analysis of the relative
significance of subsistence and profit maximization in the three areas.
What is apparent is that both goals are relevant, although there may be
some marginal differences in the weights attached to each within and
When incomes were expressed in return per consumer unit, farming
families in the Zaria area appeared to be poorer economically than those
in the Sokoto area (Table 3.11), because of the larger families in
Zaria (Table 3.2). Livestock, when expressed in terms of income per
consumer unit, was more prominent in the Sokoto area. We suggest that
with the increase in the use of manure as population density increases,
livestock will likely become more important in the rural economy with
1The relative dominance of livestock was not maintained when income
from livestock was expressed as a percentage of the total income per
2Caution must be used in interpreting comparative figures. For
example, net farm income per consumer unit derived from crops when
costing only manure that was paid for resulted in a ratio for Sokoto,
Zaria, and Bauchi of 1:0.81:0.52 (Table 3.11). However, when all manure
was costed, the ratio was 1:0.97:0.61, which represented a narrowing of
the ratios. This is likely to continue as time goes on because of
increasing population density. Indeed, more recent casual observation
in the Sokoto area has indicated that manure is becoming an economic good,
which is likely to have, in the absence of land-intensive improved
technology, an increasingly adverse effect on incomes in the Sokoto area
compared to other areas.
livestock supplying the manure for crops and an alternative source of
Income derived from off-farm sources was highest in the Sokoto
area no matter how off-farm income was expressed (i.e., as total off-farm
income per consumer unit or as off-farm income per man-day of family
labor). Although there is no question that climate plays an important
part in encouraging more time to be spent in off-farm work in the Sokoto
area (Table 3.4), it is likely that income derived from these sources
will become more significant as population density increases. The
practice of cin rani (i.e., seasonal migration) in the Sokoto area is
no doubt partly a response to both the harsh climate and the increasing
population pressure. This practice is not nearly so common in the other
areas and may partially account for the high income per man-day derived
from off-farm employment in Sokoto. This is significant, for with such
a practice, the location of the village is no longer so important in
determining the potential for highly remunerative off-farm employment
(e.g., in the modern services sector).
4.4 Influence of Fadama Land2
The villages that were intermediate in terms of access to the urban
areas had the highest proportion of fadama land. The fadama land permitted
1Lagemann  reports that in Eastern Nigeria this relationship
is a result of increasing population density and decreasing fertility of
land. The major problems, of course, in developing the complementary
relationships will be the availability of capital to invest in livestock
and the provision of food for the livestock, especially during the dry
The remaining parts of Section 4 consider analysis at levels below
that of the study area. Because of space limitations only highlights of
such analysis are presented. As a result, it was also not possible to
present tables providing detailed empirical verification for some of the
crop production to be more of a year-round activity. Family male adults,
who provide most of the labor on the family farm, worked more days on
the farm in these villages.
The higher quality of fadama land compared to gona land resulted in
higher levels of labor input per cultivated hectare and also in higher
net farm incomes both per cultivated hectare and per family man-hour
spent on the farm. It is, therefore, not surprising to find that income
derived from crop and livestock production accounted for a relatively
higher proportion of total disposable income in the villages where fadama
was dominant compared with villages where gona was more dominant. However,
the former villages proved to be less self-sufficient in cereal production,
for, apart from rice, the major crops grown on fadama land were cash
crops such as sugarcane and calabash. This implies that, in order to
surrender the goal of self-sufficiency in cereal production it was essential
for farmers in fadama-dominated villages to have ready access to urban
markets. Ease of access to the market was important because most crops
produced on fadama land were of low value per unit weight and were therefore
expensive to transport.
4.5 Influence of Cattle Ownership
Most of the cattle found in Hausaland are owned by nomadic Fulani
[Van Raay, 1969]. However, as population density increases in the area,
it is likely that nomadic pastoral life will decline and cattle will become
further integrated into a crop/livestock farming system. In our studies
we observed that sedentary farmers who owned cattle considered them as
serving three functions: as a source of milk rather than as a source of
meat production; as a form of savings and perhaps a status symbol; and as
having a complementary relationship with crop production through the
provision of manure. Cattle were not regarded as a power source.
The gandu family structure (i.e., complex family units) was
relatively more common in cattle-owning families, resulting in larger-
sized family units. The larger size of family provided labor for
time-consuming herding activities. Also, a larger proportion of such
families were self-sufficient in cereal production. The income level
of families owning cattle was also higher. The higher income levels
were achieved not only directly through ownership of cattle but also
indirectly through crop production in which the lower labor inputs per
cultivated hectare were compensated for by more intensive use of manure
per cultivated hectare. The result was similar net farm incomes per
cultivated hectare but higher total net incomes from crop production
on the larger farms of cattle owners.
4.6 Changing Family Structure
The tendency of the gandu family structure to give way to simple
family units (iyali) has already been mentioned (Section 3.2.1). This
trend has a number of implications for agricultural development, two of
which are discussed below.
1The concept of cattle being a source of manure as well as a means
of power was recognized in the 1930s through the introduction of a mixed
farming scheme [Alkali, 1970]. This scheme encouraged crop farmers to
purchase oxen and equipment through credit programs. The scheme has met
with some success in parts of Hausaland, but, as in the Sahelian countries,
successful performance has been linked to the presence of a profitable
cash crop [Institut d'Economie Rurale, 1977; Lele, 1975].
2This same trend is occurring throughout the Sahelian countries.
1. Gandu families in general possessed higher levels of resources
(i.e., land and labor) and they achieved higher levels of income. Thus,
it is possible that different types of improved technology will be
needed for gandu and iyali families. For example, Tiffen's 
research in another part of Hausaland found that where oxen and equipment
had been introduced, they were mainly controlled by gandu families who
owned cattle and had larger farms. On the other hand, it is possible
that technology which promotes land intensification would be more relevant
for iyali families.
2. Managerial ability is obviously an important characteristic in
determining the success of the farming enterprise. Since formal education
was almost completely lacking in the villages in the three areas studied,
the differences in observed managerial ability are likely to be primarily
a function of the individual characteristics of the family head enhanced
by experience that he had gained over time. However, the breakup of
the traditional gandu family structure results in younger decision makers
with a modest resource base and young children. As a result, these young
decision makers will require new technology with a low risk factor.
Fortunately, land-intensification technology is "divisible" (i.e., it
can be added in small amounts) and it appears to be relevant for the
younger heads of iyali households.
In the Sahelian countries, in contrast to northern Nigeria, the
use of oxen for providing manure as well as for plowing is seen as a
means not only of augmenting the productivity of labor but of land as
well (i.e., a technology of land intensification).
4.7 Influence of Access to Urban Areas
In each area, three villages were selected which differed in
their accessibility to the main city. Since degree of accessibility
was found to be positively related to population density, it was not
surprising to observe trends analogous to those discussed under Section
4.1. With easier access to urban areas the size of farms and proportion
of fallow land decreased. As a result, land was farmed more intensively,
requiring increased applications of manure to maintain soil fertility.
The increased pressure on the land resource base to provide a source of
income appeared to be compensated for by an increase in the significance
of livestock enterprises and in incomes derived from off-farm sources.
There was some evidence that such off-farm activities as trading increased
in relative importance as the villages became more inaccessible [Norman, 1977a].
5. ANALYSIS OF IMPROVED TECHNOLOGY PACKAGES IN
DAUDAWA VILLAGE IN THE ZARIA AREA
In the preceding two sections, we analyzed the farming systems in
different parts of Hausaland. What has emerged is a mosaic of the way
the farmer has adapted his system to the realities of the technical
and human elements. However, the increase in population density required
researchers to develop and extend relevant technology to facilitate
the adaptation process. The relevancy of the technology is defined in
terms of its compatibility with both the technical and human elements.1
Based on our analysis, it is possible to define three particular
problems in Hausaland:
1. In areas of low population density, the peak demand period for
labor is likely to be the most constraining factor on expanded output.
2. In areas of transition to high population densities, it is
possible that both a labor and a land constraint will emerge. The peak
demand period for labor will be a constraining influence and land will
emerge as a problem because soil fertility will decline under population
pressure. The possible dual nature of these constraints will be exacerbated
by the increasing necessity for farm families to spend more time in
Johnson  and Swift  have written convincingly about
the value of indigenous knowledge, the experimentation undertaken by
farmers and changes brought about through its application.
activities that require year-round commitment, including off-farm
income-earning activities, as well as caring for cattle owned by the
3. In areas of very high population density, land is likely to
be the most constraining factor.
The above scenario of problems can be reduced to two basic constraints
whose relative significance will depend to a large extent on the
seasonality of agriculture and population pressure. These are improving
the productivity of labor--particularly at bottleneck periods--and the
productivity of land. Improved technology development needs to address
these issues in order to increase the productivity of the existing
1. Increasing seasonal labor productivity. Seasonal labor
productivity could be increased directly by supplanting hand labor with
some type of mechanization such as oxen plus equipment or by using
chemical technology such as herbicides. Such types of technology
increase the amounts of land that can be handled by the farming family.
Indirectly, labor productivity could be increased through biological-
chemical technologies such as improved seed, inorganic fertilizer, or
insecticides which would avoid an increase in labor requirements during
the bottleneck period. However, this technology is unlikely to be
1As land becomes more of a constraint their value in contributing to
maintaining soil fertility becomes greater. However, the problem of feeding
them also becomes greater, usually involving a change to more labor intensive
methods. In terms of allocative efficiency, results reported elsewhere
indicate that the potential for increasing incomes and productivity through
recombining resources and enterprises currently in use is limited [Norman, 1977a].
feasible in Hausaland for most crops due to the relatively short
rainy season that allows for little flexibility in planting dates.
Biological-chemical technologies are likely to result in increased
weeding. Therefore, it is necessary to insure that the costs of increased
labor input during the labor bottleneck period is more than offset by
the increase in returns from its application. Further complications
arise if improved technology for a cash crop rather than a food crop
is being considered. Farmers would be reluctant to increase labor
requirements for cash crops during the labor-bottleneck period since,
all other things being equal, they would give priority to food crops
during that period. It has been suggested elsewhere that farmers have
a security orientation until food requirements are met, and then
their goal is profit maximization [Norman, 1977a].1
2. Land intensification technology. Both mechanical and biological-
chemical types of improved technology are relevant in areas where there
is a relatively high land-labor ratio [Spencer and Byerlee, 1977].
However, as land becomes scarcer, mechanization in the form of oxen plus
equipment is likely to become less economically viable unless owners
undertake contract work for other farmers. In addition, as land becomes
scarcer the attainment of greater output per unit of land will require
the increased use of biological-chemical technology. However, if such
technology is used, there must be assurance that soil fertility will
Although this has not been empirically verified, the various
strategies that farmers employ appear to give indirect evidence of such
Short-run private returns from the biological-chemical types of
improved technology should not be achieved at the expense of long-run
5.2 Testing Improved Technological Packages
The improved technological packages considered in this section
were basically biological-chemical in composition and they were
introduced in Daudawa village in the Zaria area. They consisted of
improved packages for sole-cropped sorghum, maize, and cotton, which
were developed in order to increase land productivity.1 These packages
consisted of improved varieties, planting in sole stands, application
of fertilizer, and, in the case of cotton, spraying.
The number of farmers involved in the testing of the packages
over a four-year period (1971-75) are given in Table 2.2. Since the
results presented in this section include oxen farmers, it is also
possible to examine the interaction of biological-chemical and mechanical
types of technology. Unlike the years during which the other studies
mentioned in previous sections were undertaken, the period during which
field testing of the improved technological packages was carried out at
Daudawa was not typical. For example, only half of the usual amount of
rain fell in 1973 and the growing season was considerably shortened
(Table 5.1). This, however, provided a good test of the stability of
the improved technological packages.
societal costs of declining soil fertility. Because of the complexities
of thinking in terms of both the short and long run, much agronomic
work in savanna areas has tended to separate these two aspects into
different research projects. This is regrettable.
1Many other improved technological packages have been developed
at the IAR. However, they are beyond the scope of this paper. Abalu
 and Hays and Raheja , for example, have field tested new
technology for groundnuts and cowpeas, respectively. With reference
to labor-augmentation types of technology, Ogunbile has done some work
with herbicides while Tiffen  and Asuquo have studied oxen. The
results of much of this work remain to be published.