Technical change and the small farmer in Hausaland, Northern Nigeria

TECHNICAL CHANGE AND THE SMALL FARMER
IN HAUSALAND, NORTHERN NIGERIA*

by

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,"
(AID/afr-C-1260).

**David W. Norman is Professor of Economics, Kansas State University,
Manhattan, Kansas.

***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

Chapter Page

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

Page

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

Table Page

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

Table Page

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

Figure Page

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

Figure Page

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

viii

ACKNOWLEDGMENTS

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

Experiment Station.

1. INTRODUCTION

1.1 Background

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.

2

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.

3

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.

4

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

Bauchi.

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

farming system.

1.3.1 Technical Factors

The technical factors include both physical factors of the

environment--water, soil, solar radiation, temperature, etc.--and

6

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

7

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

8

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

relevant.

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

9

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
[1957], 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.

10

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 [1972] 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
(persons/sq. km.)
Ecological
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 [1972]; Ministry of Economic Planning [1966].
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 [1972].
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.

13

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
2
of the population of Nigeria [Ministry of Economic Planning, 1966].

However, as Hill [1972] 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,

1964].

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

16

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 [1951]. 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

season.

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
maize.

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

Sokoto
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

Zaria
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

Bauchi
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
SUpland Fallow
IE'Uplond Bush
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

aWall
Rollway
- Main Road
r==== Bush Road

800 FT.

SCattle Track
..- Path
SRiver

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 [1972] 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
paper.

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].

20

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 [1979] in his recent study in one
part of Hausaland.

21

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

State government.

2.4 Layout of Paper

Due to the complexity of the farming systems, there were many
2
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

Type
of 1971 1972 1973 1974
Package Hectares Nos. farmers Hectares Nos. farmers Hectares Nos. farmers Hectares Nos. farmers

Maize:
Improved 8.2 19 8.8 20

Sorghum:
Improved 18.9 19 22.0 12
Indigenous 15.9 24 22.3 9

Cotton:
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

study areas.

3. A brief summary of the influence of village location, land

type, cattle ownership, and family structure on the performance of

farm families.

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 [1979] and Hill [1968] 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.

3.1 Land

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 [1972] 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

25

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

[Oluwasanmi, 1966]

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
2
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 [1972]

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
unit.

3Cleave [1974] has reported that in some parts of Africa, up to 30
percent of farmwork is spent in farmers' walking to and from fields.

Table 3.1.

CHARACTERISTICS OF LAND TENURE
IN THE THREE STUDY AREASa

Study Area
Overall
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 tenure:
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
average. Sample

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
second.
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.).

81

CI T
to eo s -oa o
j Ii

-- ---------~

A,,
AVOAVA ,.,A A>2v, ,)

ItE
VA i ,,. AA

L I'
o s ast

--i-i

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

28

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 [1978]
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].

29

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 Labor

3.2.1 Family Size and Organization

The average family consisted of six to seven individuals constituting

30

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

[1970] and Goddard [1969] 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 [1967]. 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
unit.

2
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.

Table 3.2

CHARACTERISTICS OF FARM FAMILIES
IN THE THREE STUDY AREAS

Study Area
Overall
Sokoto Zaria Bauchi Average

Family:
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
family farm

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
the second.
CFigures in parentheses reflect the average wage rate per man-hour in Lobo.

32

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

Kroeker, 1976].

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 [1977], 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.

33

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 [1972] 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

family unit.

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
cultivated hectares).

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

Relationship of

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)

Hectares cultivated:
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)
Dummy variables:c
(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

nutritional levels

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

37

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).

3.2.4 Seasonality

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 [1967] 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

Study Area
Overall
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 worked:
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.

39

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)

/\
1%\

(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

200-

On alarm (100 13.0)

e--* Off farm work (100 9.5)

-- Total (100 = 22.6)

200-

-~ --=.- *'-. '
100 :4 4 :2

'* % ..-. /

40-

(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)

200-

100 ,

40-

(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)
150-

60-

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)

I \
/
/

/ ..

,-- -
'c r

r
r'"

,

Table 3.5. INDICATORS OF SEASONALITY OF WORK BY
STUDY AREA AND OVERALL AVERAGEa

Study Area
Overall
Sokoto Zaria Bauchi Average
Busy period:

Four busiest months:
Months
Percent of total man-hours on family farm

Peak month:
Month
Total man-hours on family farm
Percent of total man-hours contributed
by hired labor
Family male adults:
Hours per day worked on farm
Days: Farm
Off-farm
Total

June-Sept. May-Aug.
56.6 50.4

July
257.7

21.0

6.1
19.9
7.0
26.8

June
255.5

18.6

5.0
16.8
7.6
24.4

June-Sept.
53.2

July
210.0

12.1

5.3
19.2
6.5
25.7

Slack period:

Four slackest months:
Month
Percent of total man-hours of family farm

Slackest month:
Month
Total man-hours on family farm
Percent of total man-hours
contributed by hired labor
Family male adults:
Hours per day worked on farm
Day: Farm
Off-farm
Total

Average month:

Total man-hours on family farm
Hours per day worked by family
male adults on family farm

Dec.-Mar.
12.7

Jan.
31.5

7.6

3.5
5.9
12.9
18.8

130.5

5.0

Jan.-Apr. Dec.-Mar.
16.0 10.4

Mar.
34.5

19.2

2.7
4.1
9.2
13.3

150.0

4.4

Feb.
16.9

110.5

4.7

53.4

241.1

17.2

5.5
18.6
7.0
25.6

13.0

27.6

8.9

3.5
4.3
10.3
14.6

130.3

4.7

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

I Harvesting

ED Cultivating

EM Planting

F Fertilizer application

- Land preparation

Apr. May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar.

(a) Histogram

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.

(b) Barchart.

Figure 3.3 Composition of work on the family farm by month and operation, Sokoto study area.

land preparation

Fertilizer
application
Planting

Cultivating

Harnesting

45

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 [1978], 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.

47

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.

48

3.3 Capital

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

system.

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

Table 3.6.

NUMBER OF LIVESTOCK BY TYPE AND VALUE OF FARM
CAPITAL BY STUDY AREA AND OVERALL AVERAGE

Study Area
Overall
Sokoto Zaria Banchi Average

Number of livestock:

Cattle: 1.5 (38.2) 3.4 (15.8) 1.2 (12.7) 2.0 (22.2)

Other livestock:
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
Naira value
(a) Crop production (buildings,
tools, and equipment) 14.25 4.51 6.12 8.29
(b) Livestock
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
2
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.

Table 3.7.

COST OF USING CAPITAL IN CROP PRODUCTION AND
CASH COSTS PER FAMILY BY STUDY AREA
AND OVERALL AVERAGE

Study Area
Overall
Sokoto Zaria Bauchi Average

Estimated cost of using
capital during survey year (N)
Durable 4.13 1.44 3.16 2.91
Nondurable:
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

Cash costs:
Total (N) 15.86 25.04 8.33 16.41

Percent:
Capital:
Durable:
Land 3.8 4.4 3.6 3.9
Other durable 25.4 13.1 34.8 24.5
Nondurable:a
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

52

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

53

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 [1976] 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

54

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

1
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.

1000

750 -

Man-hours
er \ Total man-hours per hectare
per 1
cultivated 50
hectare

250 Family man-hours per hectare

II I II I I
I 2 3 4 5 6 7
S Number of cultivated hectares
b
(a) Nasarawa

500 \

Man-hours
per < Total man-hours per hectare
cultivated 250
hectare -_ -..-
Family man-hours per hectare
I II II 1 I
S 2 3 4 5 6 7
Number of cultivated hectares
c
(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.

56

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.

57

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

most dominant.

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

Study Area
Overall
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
Millet/sorghum/cowpeas/
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
rated equally.
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.

59

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

60
Table 3.9. COMPARISON OF SOLE AND MIXED CROPS ON
GONA LAND BY STUDY AREA

Sokoto Zaria Bauchi
Average Percent
Soleb Crop Sole Crop Sole Crop change from Sole
Crops Mixtures Crops Mixtures Crops Mixtures to Crop Mixtures

Labor (man-hours/hectare):c
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

Yield (kg/ha):
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)
per:
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
labor:
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
Bauchi.

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 [1976] 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.

62

traditional to grow crops in mixtures. Probably the desire for

security in fact accounted for the traditional popularity of mixed

cropping.

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].

64

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 [1978]. 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,

1977].

3.7 Income

3.7.1 Introduction

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
following:
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
OVERALL AVERAGE

Study Area
Overall
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):
Crops:
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

Livestock:
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

Mean
Coefficient Value
(standard Estimated Opportunity
Independent Variables error) at MVP Costs
Constant 1.4481
Cultivated (hectares):
Gona Log X1 0.3407 7.18 79.29 11 to 75
(0.0379)
Fadama Log X2 0.0797 0.64 208.08 35 to 238
So (0.0266)
Man-hours of work by:

Family Log X 0.2804 1294.7 0.36 0.00 to 0.37
3 (0.0368)
Hired Labor Log X4 0.0759 266.2 0.48 0.37
(0.0098)
Capital (S.R.):
Fixed costs Log X5 0.0616 42.70 2.40 1.05-1.70
(0.0314)
Variable costsc Log X6 0.1833 113.70 2.69* 1.05-1.70
(0.0312)
Dummy variables:
Sl X7 0.2046
(0.0257)
52 X8 0.1231
(0.0234)
VI X9 -0.0297
(0.0228)
V2 X10 0.0953
(0.0248)
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
X4.
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
2
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
cultivated hectare

Independent Variables

Dependent Variable
Net farm income per man-
hour of work onbthe
family farm

Constant 114.6481 0.8823
Total man-hours per b
cultivated hectare XI 0.2798* (0.0581) -0.0010* (0.0001)
Dummy variables:

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 [1979] 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

Study Area
Overall
Sokoto Zaria Bauchi Average

Degree of self-sufficiency in
cereals--percent of farmers:

Not self-sufficient 17.1 61.9 32.1 37.0

Producing:
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
marketed:b
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 [1975].

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

75

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[1977] 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.

76

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

3.2 (e)).1

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

Yield (kg/stand):
Millet
Sorghum
Cowpeas

Yield (kg/ha):
Millet
Sorghum
Cowpeas

Value of production (N) per:
Hectare
Annual man-hour

Millet/sorghum

Sokoto Zaria

505.1 611.1

10,625 22,506

1.0:0.9 1.0:2.0

0.16
0.04

892
186

49.94
0.11

0.05
0.05

370
768

66.05
0.12

Millet/sorghum/cowpeas

Sokoto Zaria

558.5 734.4

16,272 28,620

1.0:0.5:0.4 1.0:2.0:1.0

0.09
0.03
0.02

772
124
63

46.26
0.13

0.05
0.05
0.02

400
714
167

76.33
0.13

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
2
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.
2
Lagemann [1977] 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
Nigeria.

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

among areas.

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,
2
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
consumer unit.

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.

81

livestock supplying the manure for crops and an alternative source of
1
income.

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 [1977] 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
season.
2
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
observations.

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
2
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 [1973]

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).

85

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

5.1 Introduction

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 [1972] and Swift [1978] have written convincingly about
the value of indigenous knowledge, the experimentation undertaken by
farmers and changes brought about through its application.

87

activities that require year-round commitment, including off-farm

income-earning activities, as well as caring for cattle owned by the

family.1

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

farming systems.

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].

88

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

be maintained.2

Although this has not been empirically verified, the various
strategies that farmers employ appear to give indirect evidence of such
a goal.

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
[1976] and Hays and Raheja [1977], 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 [1976] and Asuquo have studied oxen. The
results of much of this work remain to be published.

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