• TABLE OF CONTENTS
HIDE
 Front Cover
 Title Page
 Acknowledgement
 Executive summary
 Table of Contents
 List of Tables
 List of Figures
 List of maps
 Introduction
 Conceptual framework
 Data collection methodology
 Data analysis methodology
 Characterization of agro-ecological...
 Typology of aridoculture farming...
 Targeting research domains
 Summary and conclusions
 Bibliography














Title: Aridoculture baseline study and farming systems typology report
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Permanent Link: http://ufdc.ufl.edu/UF00073322/00001
 Material Information
Title: Aridoculture baseline study and farming systems typology report
Physical Description: xvii, 244 p. : ill., map ; 28 cm.
Language: English
Creator: Moore, Keith M
Institut national de la recherche agronomique du Maroc
MidAmerica International Agricultural Consortium
Publisher: Centre Regional de la Recherche Agronomique de la Chaouia Abda et Doukkala
Place of Publication: Settat Morocco
Publication Date: 1993
 Subjects
Subject: Arid regions agriculture -- Research -- Morocco   ( lcsh )
Genre: government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
non-fiction   ( marcgt )
Spatial Coverage: Morocco
 Notes
Bibliography: Includes bibliographical references (p. 239-244).
Statement of Responsibility: Keith M. Moore.
General Note: Cover title.
General Note: "June 1993"
General Note: "USAID Project No. 608-0136"
 Record Information
Bibliographic ID: UF00073322
Volume ID: VID00001
Source Institution: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 78613678

Table of Contents
    Front Cover
        Front Cover
    Title Page
        Title Page 1
        Title Page 2
    Acknowledgement
        Page i
        Page ii
    Executive summary
        Page iii
        Page iv
        Page v
        Page vi
    Table of Contents
        Page vii
        Page viii
        Page ix
        Page x
    List of Tables
        Page xi
        Page xii
        Page xiii
        Page xiv
        Page xv
    List of Figures
        Page xvi
    List of maps
        Page xvii
        Page xviii
    Introduction
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
        Page 6
    Conceptual framework
        Page 7
        Page 8
        Page 9
        Page 10
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
        Page 18
    Data collection methodology
        Page 19
        Page 20
        Page 21
        Page 22
        Page 23
        Page 24
        Page 25
        Page 26
    Data analysis methodology
        Page 27
        Page 28
        Page 29
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        Page 31
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        Page 33
        Page 34
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    Characterization of agro-ecological zones
        Page 41
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    Typology of aridoculture farming systems
        Page 71
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        Page 192
    Targeting research domains
        Page 193
        Page 194
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    Summary and conclusions
        Page 229
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    Bibliography
        Page 239
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Full Text






-J- INRA
Institute National
de la
Recherche Agronomique


MidAmerica International
Agricultural Consortium


Centre Regional de la Recherche Agronomique
de la Chaouia Abda et Doukkala
B.P. 290 Settat, Maroc


1-3, /2>5


ARIDOCULTURE BASELINE STUDY


AND


FARMING SYSTEMS TYPOLOGY REPORT









Keith M. Moore
MIAC Rural Sociologist


June 1993



SAID PROJECT No 608 0136


I













ARIDOCULTURE BASELINE STUDY

AND

FARMING SYSTEMS TYPOLOGY REPORT







Keith M. Moore
MIAC Rural Sociologist

June 1993





Socio-Economics Sub-Program, Aridoculture Center, CRRA/Settat, Morocco
This work was carried out as part of the MIAC/INRA Morocco Dryland
Applied Agricultural Research Project under USAID Project No. 608-0136.












ACKNOWL EDGEMENTS


This report is the product of a major team effort on the part of the Socio-
Economics Sub-Program and has benefited from the contributions of INRA and MIAC
scientists throughout the Aridoculture Center. This work was carried out as
part of the MIAC Morocco Dryland Agriculture Applied Research Project under
USAID Project No. 608-0136 and was additionally supported by the Moroccan
Institute National de la Recherche Agronomique.

The completion of this report would not have been possible without the design
and implementation of the baseline survey. Drs. Fatima Nassif and Abdelilah
Sefrioui, with the assistance of Richard Riddle, played major roles in the
conception and conduct of that survey. Their leadership, as well as practical
and conceptual contributions, have continued to encourage and direct the
development of this report.

Both MIAC and INRA administrations assured the financial and logistical
arrangements which facilitated the completion of this report. The
encouragement and support of MIAC Team Leaders D. Keith and T. Gillard-Byers
and CRRA-Settat Center Directors M. Kamel and M. El Mourid are appreciated.

Comments and suggestions on the original project design were provided by R.
Stryker (USAID) and R. Tutwiler (ICARDA). The Direction Provinciale
d'Agriculture of Settat and Safi Provinces contributed to the initial survey
design. In particular, M. Abdelouafi (Settat) and A. Merraji (Safi) of their
respective Service des Enquates et des Etudes Economiques et Statistiques
provided important assistance in the sample design.

The data collection instruments and questionnaire items were reviewed by
Aridoculture Center scientists. The multidisciplinary nature of the survey was
enhanced by the critiques and suggestions of: A. Amri, A. Azzaoui, L.
Bashford, M. Boughlala, B. Boulanouar, M. Bouayad, L. Buschman, M.
Derkaoui, A. Herzenni, M. Kamel, A. Laamari, A. Lyamani, 0. Merkle, G.
Rafsnider, A. Selmani, and R. Zimdahl.

No survey can be completed without skilled interviewers. We were fortunate to
have a such a strong team. The enthusiasm and diligence of F. Aouzar, T.
Bsabsa, S. Chakib, F. Daghmoussi, M. Fadli, K. Jamhouri, M. Jraif, 0. Lahrache,
M. Ouahbi, K. Slib, and A. Wabir ensured a complete and reliable baseline
data set. Data entry and preparation were completed with the assistance of
Fatima Aouzar and Khadija Jamhouri.

The comments, criticism and suggestions of numerous MIAC and INRA reviewers
have aided in developing a report which addresses both broad issues of
agricultural technology research and transfer as well as specific disciplinary
concerns. A special note of appreciation is due Dr. Owen Merkle for his
counsel and support during the initial drafting of the report. The following
scientists have provided substantial feedback on all or part of the document:
R. Bansal, H. Benaouda, M. Bendaoud, R. Cartier, M. El Gharous, T. Gillard-
Byers, A. Herzenni, M. Karrou, M. Mahzar, M. Moussaoui, F. Nassif, A.
Sefrioui, J. Tiedeman, and T. Woldetatios. Despite all of this assistance,
the author is responsible for any errors or omissions which remain in this
report.













EXECUTE I VE SUMMARY


The development and transfer of technologies appropriate to the arid and semi-
arid (aridoculture) regions of Morocco requires a basic understanding of the
farm household population and the farming systems which sustain them. The
baseline data set and farming systems typology described in this report provide
the basis for that understanding. These tools can be used by policy makers,
researchers, and extension personnel for the planning and implementation of
technology development and transfer.

The. essential message is that improvements in production and incomes are
dependent upon the adequacy of correspondence between agricultural technology
research, the existing farming systems, and the policy and market environments.
Consequently, this report is not a stand-alone document. It is an integral
part of the Planning-by-Objective Aridoculture Program Strategy.

Farming systems research and development has guided the analysis and
presentation of findings in this report. The concept of "system" is central to
this approach. Farming operations have been examined as systemic wholes
involving farm household members allocating their resources to three
interdependent processes (crops, livestock, and off-farm activities) to attain
their culturally and materially defined goals. These processes were conceived
as sub-systems composing, with household resources, the farming system.

This report introduces a typology of aridoculture farming systems. It provides
a core for the systematic integration of information on farm households and
their productive activities. It is also a decision-making tool for the
development, research, targeting, evaluation, and dissemination of agricultural
innovations. The typology consists of 19 types of farming system across four
agro-ecological zones of the Aridoculture Center region. It is designed to
achieve three objectives:

(1) to highlight distinctive patterns and key components associated with
each farming system;

(2) to demonstrate interdependence between the level and form of farm
household resources and their mobilization within the sub-systems to
achieve farm household goals; and

(3) to provide a farming system focus on specific conditions which a
continuing dialog between farmers and researchers may improve.

The circumstances and practices associated with each type of farming system
have been described in detail. Reported statistics are indicative of and
proportional to population parameters.


-iii-












The typology targets potential research domains for researcher/farmer dialog
over problem diagnosis and solution development. The 19 types of farming
system have been grouped into six socio-economic categories to facilitate
specification of research domains for sondeos, in-depth surveys, and on-farm
diagnostic investigation. Simple criteria are provided for identifying these
categories within their agro-ecological zones. The discussion focuses on the
dominant characteristics of these farming systems and suggests directions for
further research.

The specification of a typology does not complete the targeting cycle. The
role of a typology is to focus investigation and dialog with farmers within
clearly-defined and homogeneous research domains. Only then, can targeted
recommendation domains be established.


Highlights

Sustainable farming operations in the context of highly erratic climatic
conditions require that farm households diversify productive and income-
generating options. These options must balance immediate returns with long-
term viability. In confronting the risks inherent at various stages of the
growing season, mixed farming systems are strategically designed to maximize
flexibility (as conditions develop) in minimizing losses and/or maximizing
outputs. Despite the heterogeneity of farming systems these strategies
produce, several prevailing trends and patterns have been identified.

The foundation of these farming systems is in cereal production combined with
livestock enterprises. Specialized crop production solely for the market is
limited to only a few parcels (often of non-cereal crops). Indeed, the
majority of all crop output is directed to feeding livestock and household
consumption.

Across the agro-ecological zones surveyed, land holdings are fragmented into
dispersed small parcels. Each parcel requires separate, but coordinated,
management. Pervasive similarities in these management practices are limited
to the hand-broadcasting of cereal seed and the grazing of harvested fields
and/or fallow. Custom-hired tractor services are the predominant source of
motorized traction, despite a continuing reliance on animal traction. A gender
and age-based division of household labor predominates. Most significant is
the importance of women in livestock production. Off-farm employment is common
throughout the region. Farm operations often farm land or raise livestock on
shares with urban-based partners to attain operational size.

Livestock production is pivotal to farm household livelihoods throughout the
region. In Zone I, a specialization in dairy production is increasing while
sheep are less important. Both cattle and sheep production are intensifying in
Zones II and III, particularly among the larger farming systems. In Zone IV,
sheep production is becoming increasingly concentrated while cattle production
is declining.

Although cereals are grown in all zones, durum wheat is more common in Zones II
and III, and bread wheat, in Zones I and IV. Barley, because of its role as an
animal feed, is pervasive throughout the region. Zone I is an exception where
forage crops predominate. Food legume crops and corn (bernichas) rotated with
cereals are most common in Zone II. Although often considered as cash crops,


-iv-











these bernichas play other roles. They improve soil fertility for the
following year through weeding and nitrogen mineralization, as well as produce
feed for animals and grain for human consumption.

Despite many similarities across these mixed farming systems, important
differences emerge. Particularly significant indicators of differentiation are
the size of the household labor force, the number of hectares operated, off-
farm employment, land tenure status, the hiring of full-time laborers, and non-
agricultural investments. The analysis of these characteristics emphasizes the
need to distinguish recurrent features of the population as a whole from the
specific roles those characteristics play in sustaining any particular farming
system.

Farming system resources are mobilized to achieve farm household objectives.
The goal of crop production varies between household consumption, market sales,
and livestock feed. These variations are reflected in the allocation and
intensity of household labor in crop production, as well as in the use of
purchased inputs. Of the six categories of farming systems, the market-
oriented Hired Labor, Medium Scale and Small. Scale Farming Systems are
relatively more mechanized and input intensive than the Off-Farm Employment
Dependent, Marginal, and Resource Deficient Farming Systems which tend to
consume more of their production.

Off-farm income, earned by men, may serve consumption needs or be invested in
either farm or non-farm activities. In either case, a distinguishing factor is
often whether these farming systems rely on salaried labor or on women and
children family members for the tending of livestock. The role played by off-
farm employment in the Off-Farm Employment Dependent Farming Systems is
essential to sustaining the farm household. In the Hired Labor Farming
Systems, off-farm activities provide a basis for increasing the wealth of the
farm household.

At the lowest resource levels, Resource Deficient Farming Systems have limited
options for diversification. Resources are, consequently, allocated to fewer
activities. The Off-Farm Employment Dependent and Marginal Farming Systems
have broadly similar resource levels. Each maintains diversity through
livestock production. However, one allocates its labor to off-farm employment
while the other concentrates on a higher intensity exploitation of limited
cropping resources.

As resource levels increase, farming systems increase the diversity of
activities allocating more resources to directly income-generating activities.
Small Scale Farming Systems maintain limited livestock production while
diversifying their crop production on limited holdings, and when possible,
engage in off-farm employment. Medium Scale Farming Systems with more
extensive holdings engage in expanded crop and livestock production generating
substantial surpluses. Hired Labor Farming Systems rely on salaried employees
to conduct large-scale crop and livestock production while engaging in off-farm
employment and the management of non-agricultural investments.

These differences in farming system constraints and practices must be taken
into account in the design and implementation of agricultural technology
research and development. The goal of this report has been to focus problem
diagnosis, testing and solution development on the the existing aridoculture
farming systems.













TABLE OF CONTENTS


Acknowledgements . . .
Executive Summary . .
Table of Contents . .
List of Tables . . .
List of Figures . . .
List of Maps . . .


. . . . . i
. . . . . iii
. . . . . vii
. . . . . xi
. . . . . xvi
. . . . . xvii


INTRODUCTION


Study Focus and Objectives
Concepts and Approach .
Overview of the Contents .


PART I


CHAPTER 1: CONCEPTUAL FRAMEWORK


The Concept of System . . . .
The Farming System and Its Environment . .
Targeting Domains . ... . . .
Farming System Typologies . . . .
Typology Construction: Concept Formation and Criteria

CHAPTER 2: DATA COLLECTION METHODOLOGY

Development of the Sampling Framework . .
Sample Selection . . . . .
Sample Representivity and Population Dynamics .
Survey Instrument Development: Interview Schedules
Survey Instrument Development: Interviewer Training .
Survey Instrument Development: Pre-Test . .
Data Collection: Farm Household Interviews . .
Data Entry and Cleaning Procedures . . .
Preparation for Data Analysis . . .


-vii-


. . . . . .














CHAPTER 3: DATA ANALYSIS METHODOLOGY

Typology Construction: A Systems-Appropriate Approach .
Indicator Identification and Construction . . .
Determining Co-variation Between Components at the
Sub-System and Resource Levels . . . .
Assigning Farm Households to Homogeneous Farming Systems
Confirming the Statistical Adequacy of Group Placement .




PART II


CHAPTER 4: CHARACTERIZATION OF AGRO-ECOLOGICAL ZONES

Establishing Agro-Ecological Zones . . .
Climatic Conditions for the Cropping Year 1989-90 . .
Description of the Agro-Ecological Zones . . .
Zone I: The Coastal Zone . . . .
Zone II: The More Favorable Zone .. .......
Zone III: The Less Favorable Zone ....
Zone IV: The Zone of Ahmar . . . .
Agricultural Conditions in the Survey Region . .

Appendix 4.1: Aggregate Farm Household Characteristics
for Each Agro-Ecological Zone . . . .
Appendix 4.2: Glossary of Terms Used in the Text . .

CHAPTER 5: TYPOLOGY OF ARIDOCULTURE FARMING SYSTEMS


Zone I: The Coastal Zone
Type I-1 . .
Type 1-2 . .
Type 1-3 . .
Zone II: The More Favorable
Type II-1 . .
Type II-2 . .
Type 11-3 .
Type 11-4 . .
Type 11-5 . .
Type 11-6 . .
Zone III: The Less Favorablt
Type III-1 . .
Type III-2 . .
Type 111-3 . .
Type 111-4 . .
Type III-5 . .
Type III-6 . .
Zone IV: The Zone of Ahmar
Type IV-1 . .
Type IV-2 . .
Type IV-3 . .
Type IV-4 . .


Zone
. .
* .





sZone
. .
. .
. .
. .
. .
* .
. .
. .
. .
. .


-viii-


71
72
78
84
91
92
98
104
110
116
122
129
130
136
142
148
154
160
167
168
174
180
186


e












CHAPTER 6: TARGETING RESEARCH DOMAINS 193

Hired Labor Farming Systems . . . . ... ... 193
Medium Scale Farming Systems . . . .... .199
Small Scale Farming Systems .............. ... .... .204
Off-Farm Employment Dependent Farming Systems . . .... 210
Marginal Farming Systems . . . . ... .215
Resource Deficient Farming Systems . . . .... 221



CONCLUSION


CHAPTER 7: SUMMARY AND CONCLUSIONS 229

The Conceptual Basis of Targeted Research and Development . .. .229
Findings: Prevailing Trends and Patterns . . .... 230
Findings: Variations Across Agro-Ecological Zones . ... .231
Findings: Differentiation Among Farming Systems . ... .232
The Uses of the Baseline Data Set and Farming Systems Typology 234


Bibliography . . . . . . .... 239


-ix-












LIST OF TABLES


Table 3.1:

Table 3.2:


Table 3.3:


Table 3.4:


Table 3.5:


Table 3.6:


Table 3.7:


Table 3.8:


Table 3.9:



Table 4.1:

Table 4.2:
Table 4.3:
Table 4.4:
Table 4.5:
Table 4.6:
Table 4.7:
Table 4.8:
Table 4.9:
Table 4.10:
Table 4.11:
Table 4.12:
Table 4.13:
Table 4.14:

Table 4.15:

Table 4.16:


Ordinal Scale Means and Standard Deviations for the
29 Key Indicators by Sub-System and Resource Categories
Factor Score Coefficients of Farm Household Resource
Indicators for Each Dimension of Variation by
Agro-Ecological Zone . . . . .
Factor Score Coefficients of Cropping Sub-System
Indicators for Each Dimension of Variation by
Agro-Ecological Zone . . . . .
Factor Scroe Coefficients of Livestock Sub-System
Indicators for Each Dimension of Variation by
Agro-Ecological Zone . . . . .
Factor Scroe Coefficients of Non-Farm Sub-System
Indicators for Each Dimension of Variation by
Agro-Ecological Zone . . . . .
Rotated Standardized Canonical Discriminant Function


Coefficients by Order of Step-Wise Entry for Zone I Farming
System Types . . . . . .
Rotated Standardized Canonical Discriminant Function
Coefficients by Order of Step-Wise Entry for Zone II Farming
System Types . . . . . .
Rotated Standardized Canonical Discriminant Function
Coefficients by Order of Step-Wise Entry for Zone III Farming
System Types . . . . . .
Rotated Standardized Canonical Discriminant Function
Coefficients by Order of Step-Wise Entry for Zone IV Farming
System Types . . . . . .

1989-90 and Average Annual Precipitation Rates for
Various Locations in the Survey Region . . .
Distribution of Soil Types in Zone I . . .
Distribution of Crops Grown in Zone I . . .
Composition of Livestock in Zone I . . . .
Distribution of Soil Types in Zone II . . .
Distribution of Crops Grown in Zone II . . .
Composition of Livestock in Zone II . . . .
Distribution of Soil Types in Zone III . . .
Distribution of Crops Grown in Zone III . . .
Composition of Livestock in Zone III . . .
Distribution of Soil Types in Zone IV . . .
Distribution of Crops Grown in Zone IV . . .
Composition of Livestock in Zone IV . . . .
Average Cereal Grain Yields for the Largest Parcel of
Each Cereal Farm Households Grow by Agro-Ecological Zone
Average Cereal Straw Yields for the Largest Parcel of
Each Cereal Farm Households Grow by Agro-Ecological Zone
Percentage of Farm Households in Each Agro-Ecological
Zone by Hectares Operated . . . . .


-xi-


. .












Table 4.17:

Table 4.18:

Table 4.19:

Table 4.20:

Table 4.21:


Table 4.22:

Table 4.23:

Table 4.24:


Table 4.25:


Table 4.26:

Table 4.27:


Table 4.28:


Table 4.29:


Table 4.30:

Table 4.31:


Table 4.32:

Table 4.33:

Table 4.34:


Table 4.35:


Table 4.36:


Table 4.37:


Percentage of Farm Households in Each Agro-Ecological
Zone by Size of Household . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Age of Household Head . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Off-Farm Employment . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Off-Farm Employment Status of Household Head .
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Financial Remittances from Non-Resident


Family Members . . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone Owning Non-Agricultural Investments . .
Percentage of Farm Households in Each Agro-Ecological
Zone Owning a Tractor . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Predominant Mode of Seedbed Preparation for
Small Grains . . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Tractor-Powered Seed Covering for
Small Grains . . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Level ofCombine Harvesting of Small Grains
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Commercial Fertilizer Use for


Small Grain Production . .
Percentage of Farm Households in Each
Zone by Level of Certified Seed Use
Small Grain Production . .
Percentage of Farm Households in Each
Zone by Level of Chemical Herbicide


Agro-Ecological
for

Agro-Ecological
Use


* 62


. 62


. 62

* 62


. 63


. 63


Small Grain Production . . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone Purchasing Animal Feed . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Household Labor Allocated to
Crop Production . . . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Hired Labor Allocated to Crop Production .
Percentage of Farm Households in Each Agro-Ecological
Zone by Person-Days Labor Per Hectare of Crop Production
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Household Labor Allocated to
Livestock Production . . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Hired Labor Allocated to
Livestock Production . . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Harvested Grain and Produce Consumed
by Livestock . . . . . .
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Harvested Grain and Produce Sold . .


-xii-


. .












Table 4.38:


Table 4.39:



Table 5.1:

Table 5.2:


Table 5.3:

Table 5.4:
Table 5.5:

Table 5.6:


Table 5.7:

Table 5.8:
Table 5.9:

Table 5.10:


Table 5.11:

Table 5.12:
Table 5.13:

Table 5.14:


Table 5.15:

Table 5.16:
Table 5.17:

Table 5.18:


Table 5.19:

Table 5.20:
Table 5.21:

Table 5.22:


Table 5.23:


Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Harvested Grain and Produce Consumed
by the Household ............... .. ... 66
Percentage of Farm Households in Each Agro-Ecological
Zone by Level of Harvested Grain and Produce Stocked
for Later Use . . . . . 66

Summary of Type I-1 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .. 73
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type I-1 Farm Households Who
Possess Ruminants . . . . ... ... 75
Summary of Purchased Animal Feed for the 100 Percent
of Type I-1 Farm Households Who Purchased Feed . ... 75
Type I-1 Seasonal Sheep and Cattle Feeding Schedules .... 75
Summary of Type I-2 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .. 79
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type I-2 Farm Households Who
Possess Ruminants . . . . ... ... 81
Summary of Purchased Animal Feed for the 100 Percent
of Type I-2 Farm Households Who Purchased Feed ...... 81
Type I-2 Seasonal Sheep and Cattle Feeding Schedules .... 81
Summary of Type I-3 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .. 85
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type I-3 Farm Households Who
Possess Ruminants . . . . ... ... 87
Summary of Purchased Animal Feed for the 100 Percent
of Type 1-3 Farm Households Who Purchased Feed . 87
Type I-3 Seasonal Sheep and Cattle Feeding Schedules . 87
Summary of Type 11-1 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .. 93
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type 11-1 Farm Households Who
Possess Ruminants . . . . ... ... 95
Summary of Purchased Animal Feed for the 100 Percent
of Type 11-1 Farm Households Who Purchased Feed . .. 95
Type 11-1 Seasonal Sheep and Cattle Feeding Schedules . 95
Summary of Type 11-2 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated .. ... 99
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type 11-2 Farm Households Who
Possess Ruminants . . . . ... ... 101
Summary of Purchased Animal Feed for the 100 Percent
of Type 11-2 Farm Households Who Purchased Feed . ... 101
Type 11-2 Seasonal Sheep and Cattle Feeding Schedules . 101
Summary of Type 11-3 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .. .105
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type 11-3 Farm Households Who
Possess Ruminants . . . . ... ... 107
Summary of Purchased Animal Feed for the 100 Percent
of Type 11-3 Farm Households Who Purchased Feed . .. .107


-xiii-












Table 5.24:
Table 5.25:

Table 5.26:


Table 5.27:

Table 5.28:
Table 5.29:

Table 5.30:


Table 5.31:

Table 5.32:
Table 5.33:

Table 5.34:


Table 5.35:

Table 5.36:
Table 5.37:

Table 5.38:


Table 5.39:

Table 5.40:
Table 5.41:

Table 5.42:


Table 5.43:

Table 5.44:
Table 5.45:

Table 5.46:


Table 5.47:

Table 5.48:
Table 5.49:

Table 5.50:


Type II-3 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type II-4 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type II-4 Farm Households Who
Possess Ruminants .. ........ . .
Summary of Purchased Animal Feed for the 100 Percent
of Type II-4 Farm Households Who Purchased Feed . .
Type II-4 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type II-5 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type II-5 Farm Households Who
Possess Ruminants .. ......... . ...
Summary of Purchased Animal Feed for the 100 Percent
of Type II-5 Farm Households Who Purchased Feed . .
Type II-5 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type II-6 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type II-6 Farm Households Who
Possess Ruminants .. . ...........
Summary of Purchased Animal Feed for the 100 Percent
of Type II-6 Farm Households Who Purchased Feed . .
Type II-6 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type III-1 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type III-1 Farm Households Who
Possess Ruminants .. ........ . .
Summary of Purchased Animal Feed for the 100 Percent
of Type III-1 Farm Households Who Purchased Feed . .
Type III-1 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type III-2 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type III-2 Farm Households Who
Possess Ruminants .. . . ...........
Summary of Purchased Animal Feed for the 100 Percent
of Type III-2 Farm Households Who Purchased Feed . .
Type III-2 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type 111-3 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type III-3 Farm Households Who
Possess Ruminants .. . . ...........
Summary of Purchased Animal Feed for the 100 Percent
of Type III-3 Farm Households Who Purchased Feed . .
Type 111-3 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type III-4 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type III-4 Farm Households Who
Possess Ruminants . . . . . .


-xiv-












Table 5.51:

Table 5.52:
Table 5.53:

Table 5.54:


Table 5.55:

Table 5.56:
Table 5.57:

Table 5.58:


Table 5.59:

Table 5.60:
Table 5.61:

Table 5.62:


Table 5.63:

Table 5.64:
Table 5.65:

Table 5.66:


Table 5.67:

Table 5.68:
Table 5.69:

Table 5.70:


Table 5.71:

Table 5.72:
Table 5.73:

Table 5.74:


Table 5.75:

Table 5.76:


Summary of Purchased Animal Feed for the 100 Percent
of Type 111-4 Farm Households Who Purchased Feed . .
Type 111-4 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type 111-5 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type 111-5 Farm Households Who
Possess Ruminants . . . . .
Summary of Purchased Animal Feed for the 100 Percent
of Type 111-5 Farm Households Who Purchased Feed . .
Type 111-5 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type 111-6 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type 111-6 Farm Households Who
Possess Ruminants . . . . . .
Summary of Purchased Animal Feed for the 100 Percent
of Type 111-6 Farm Households Who Purchased Feed . .
Type 111-6 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type IV-1 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type IV-1 Farm Households Who
Possess Ruminants . . . . .
Summary of Purchased Animal Feed for the 100 Percent
of Type IV-1 Farm Households Who Purchased Feed . .
Type IV-1 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type IV-2 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type IV-2 Farm Households Who
Possess Ruminants . . . . . .
Summary of Purchased Animal Feed for the 100 Percent
of Type IV-2 Farm Households Who Purchased Feed . .
Type IV-2 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type IV-3 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type IV-3 Farm Households Who
Possess Ruminants . . . . .
Summary of Purchased Animal Feed for the 100 Percent
of Type IV-3 Farm Households Who Purchased Feed . .
Type IV-3 Seasonal Sheep and Cattle Feeding Schedules .
Summary of Type IV-4 Cropping Practices for Cereals Based
on the Largest Parcel of Each Cereal Cultivated . .
Summary of Harvested Crops Fed to Livestock for the
100 Percent of Type IV-4 Farm Households Who
Possess Ruminants . . . . .
Summary of Purchased Animal Feed for the 100 Percent
of Type IV-4 Farm Households Who Purchased Feed . .
Type IV-4 Seasonal Sheep and Cattle Feeding Schedules .


-xv-


151
151

155


157

157
157

161


163

163
163

169


171

171
171

175


177

177
177

181


183

183
183

187


189

189
189













LIST OF FIGURES


Figure 1: Overview of Study Approach . .


Hierarchy of Agricultural Systems .
The Farming System . . .


Figure 4: Cumulative Rainfall Probabilities for
Figure 5: Cumulative Rainfall Probabilities for


Settat Province: 1989-90


Ahmar Region:


1989-90 .


Figure 6:
Figure 7:
Figure 8:
Figure 9:
Figure 10:
Figure 11:
Figure 12:
Figure 13:
Figure 14:
Figure 15:
Figure 16:
Figure 17:
Figure 18:
Figure 19:
Figure 20:
Figure 21:
Figure 22:
Figure 23:
Figure 24:

Figure 25:
SFigure 26:

Figure 27:

Figure 28:

Figure 29:
Figure 30:

Figure 31:

Figure 32:

Figure 33:

Figure 34:


Type I-1 Farming System .
Type I-2 Farming System .
Type I-3 Farming System .
Type II-1 Farming System
Type II-2 Farming System .
Type II-3 Farming System .
Type II-4 Farming System .
Type II-5 Farming System .
Type II-6 Farming System .
Type III-1 Farming System
Type III-2 Farming System
Type III-3 Farming System
Type III-4 Farming System
Type III-5 Farming System
Type III-6 Farming System
Type IV-1 Farming System .
Type IV-2 Farming System .
Type IV-3 Farming System .
Type IV-4 Farming System .


. . . . .
. . . . .
. . . . .
. . . . .
. . . o . .
. . . . .

. . . . .
. . . . .
. . . . .
.e . . .e .
. .e . .e . .
. . . . .
. .o .e e . .
. ... .o . . .
. . . . .
. .o .e . .o .


Repartition of Land Uses for Hired Labor Farming Systems .
End Use of Harvested Grains and Produce for Hired Labor
Farming Systems . . . . . .
Animal Feed Composition by Source for Hired Labor
Farming Systems . . . . . .
Labor Allocations by Sub-System and Category for
Hired Labor Farming Systems . . . .
Repartition of Land Uses for Medium Scale Farming Systems .
End Use of Harvested Grains and Produce for Medium Scale
Farming Systems . . . . . .
Animal Feed Composition by Source for Medium Scale
Farming Systems . . . . . .
Labor Allocations by Sub-System and Category for
Medium Scale Farming Systems . . . .
Repartition of Land Uses for Resource Small Scale
Farming Systems . . . . . .
End Use of Harvested Grains and Produce for Small Scale
Farming Systems . . . . . .


-xvi-


Figure 2:
Figure 3:


76
82
88
96
102
108
114
120
126
134
140
146
152
158
164
172
178
184
190

194

195

196

197
200

201

202

203

206

207












Figure 35:

Figure 36:

Figure 37:

Figure 38:

Figure 39:

Figure 40:

Figure 41:
Figure 42:

Figure 43:

Figure 44:

Figure 45:

Figure 46:

Figure 47:

Figure 48:


Figure 49:


Animal Feed Composition by Source for Small Scale
Farming Systems . . . . . .
Labor Allocations by Sub-System and Category for
Small Scale Farming Systems . . . .
Repartition of Land Uses for Off-Farm Employment Dependent
Farming Systems .. . . . . .
End Use of Harvested Grains and Produce for Off-Farm
Employment Dependent Farming Systems . . .
Animal Feed Composition by Source for Off-Farm Employment
Dependent Farming Systems . . . . .
Labor Allocations by Sub-System and Category for Off-Farm
Employment Dependent Farming Systems . . .
Repartition of Land Uses for Marginal Farming Systems .
End Use of Harvested Grains and Produce for Marginal
Farming Systems . . . . . .
Animal Feed Composition by Source for Marginal Farming
Systems . . . . . . .
Labor Allocations by Sub-System and Category for Marginal
Farming Systems . . . . . .
Repartition of Land Uses for Resource Deficient
Farming Systems . . . . . .
End Use of Harvested Grains and Produce for Resource
Defiecient Farming Systems . . . . .
Animal Feed Composition by Source for Resource Deficient
Farming Systems . . . . . .
Labor Allocations by Sub-System and Category for Resource
Deficient Farming Systems . . . . .

Baseline Utilization Process . . . . .


208

209

211

212

213

214
216

217

218

219

222

223

224

225

235


LIST OF MAPS




Map 1: Location of Agro-Ecological Zones Covered in the Farming
Systems Study . . . . . . .
Map 2: Location of Survey Douars for the Three Agro-Ecological
Zones in Settat Province . . . . ...
Map 3: Location of Survey Douars for the Agro-Ecological Zone
of Ahmar in Safi Province . . . . .


-xvii-












INTRODUCTION


The development and transfer of technologies appropriate to the arid and semi-
arid (aridoculture) regions of Morocco requires a basic understanding of the
farm household population and the farming systems which sustain them. The
baseline information contained in this report has been organized to provide a
benchmark and comprehensive frame of reference for that understanding within
four agro-ecological zones of Settat and Safi Provinces. The purpose of this
report is to introduce a typology of farming systems which provides both a core
for the systematic integration of information and analyses on the target
population and their productive activities, and a decision-making tool for
research, development, targeting, evaluation, and dissemination of agricultural
innovations.

This report is not a stand-alone document. It is an integral part of the
research agenda development phase of the Aridoculture Program Strategy feeding
into and receiving feedback from the downstream phases of diagnostic and
evaluation trials as well as the diffusion of technological innovations. The
early work of Pascon (1986), the Department of Rural Development (IAV) Chaouia
Project Study (Benatya et.al., 1983), and the work of the Rural Sociology and
Agricultural Economics Sections of the CRRA-Settat Aridoculture Center
(Rafsnider and Laamari, 1990; Nassif, 1993) provided the information base to
initiate this study and aided in the interpretation of findings. On-going and
future research has been designed to build upon and complement this work.


Study Focus and Objectives

Low, erratic rainfall and frequent droughts are the primary constraints faced
by all farm households in the region creating a situation of random climatic
conditions (Benatya et al, 1983; Watts and El Mourid, 1988). Within the
context of these constraints, sustainability of the farming population depends
on a diversification of crop and livestock production and a plurality of
combined subsistence and income-generating activities, balancing immediate
needs with long-term viability. In this regard, the pivotal role of livestock
and the importance of non-farm income must be placed in the context of a
foundation in crop production. The integration of these activities at the farm
household level constitutes the mixed farming systems of this dryland region.

The coordination of household members and their activities is a matter of
strategic decision-making in the allocation of household resources. Not all
households are equally endowed, nor do they have the same goals. Limitations
and opportunities in terms of the agro-ecological features of the farm
operation, household composition, and the possession of resources affect the











range and form of strategic choices. These choices are filtered through the
cultural context which provides social definition to the goals pursued.
Despite many similarities among mixed farming systems, important differences
emerge. These differences emphasize the need to distinguish recurrent features
from the roles specific activities and resources play in sustaining any
particular farming system.

Previous research has provided an accounting of some of these recurrent
features for specific cropping practices (Benatya et al, 1983; Primov, Said and
Herzenni, 1987a; Riddle and Moore, 1990) and livestock production techniques
(Primov, Herzenni and Said, 1987b). Other in-depth work has investigated the
interaction of various farming system components at the farm and local levels
(Herzenni, 1988; Sefrioui, El Mourid, and Herzenni, 1989; Pascon, Benatya and
Zagdouni, no date). With the exception of Herzenni (1989) and Akhrass and
Sefrioui (1990), little attention has been paid to systematic patterns which
differentiate farm households and their farming systems. None have provided a
comprehensive framework which integrates these findings, nor has it been
possible to extrapolate findings to the population as a whole. Consequently,
technology research and transfer efforts have not been efficiently targeted.

Recognizing the need to consolidate and build on this knowledge, the multiple
forms and contexts in which agricultural production is conducted, and,
consequently, the variety of technological needs and capacities, the objectives
of this study are:

1. to identify and categorize the various farming systems of the region;

2. to develop operational descriptions of the internal organization of
each farming system; and

3. to provide a benchmark of useful and accessible information to inform
on-going technology research and transfer for the region.


Concepts and Approach

The conceptual framework and approach elaborated to achieve these objectives
are derived from the farming systems research and development/extension
tradition which has evolved over the past two decades. The conceptual
framework is founded on the premise that farm operations are integrated wholes
composed of household members and their resources engaged in productive
processes. The concept of farming system is used to highlight the systematic
nature and interdependence of these processes. It further recognizes that
farming systems exist within agro-ecological environments. The concept of
agro-ecological zone is used to delimit bio-physical and climatic production
potentials.

The overall design and methodological approach of this study draws on Capillon
(1986) and Jamtgaard (1989), and has been adapted to Moroccan aridoculture
research and farming conditions as a part of an on-going process of dialog
between researchers and farmers. Building on the farming systems and typology
construction literature, as well as the research experiences of the Center, a
conceptual framework was developed and four agro-ecological zones identified
for study. Farm and household questionnaires were elaborated by an
interdisciplinary and interagency group to address the full range of
quantifiable farm household circumstances and activities as found in the









i- I Aridoculture Region


Conceptual Framework -


Questionnaire
Development

I


Agro-Ecological Zones


Farms Stratified by
Douars and Hectarage


Interviewer Training


S pro-test .


Sample of Farms


Farm Household
Surveys


Compare and
Group Farms


Typology of
Farming Systems


SCharacterization and
Frequency of Each Type


Targeting Specific
Research Domains


Figure 1: Overview of Study Approach


Identification Criteria
and Implications


I I


t, J


I W-


.04












region. A two-stage cluster and proportionally representative sample was drawn
for a cross-sectional survey. Interviewer training and pre-tests were then
conducted. The survey involved man/woman teams interviewing the farmer and his
spouse, respectively, at their residence. The pre-coded data were computerized
and statistical analyses performed to establish the typology, identify simple
criteria for classification of farms by type, and estimate the frequency of
each type within the farm population. Subsequent analyses determined the
implications for technology research and transfer.


Overview of the Contents

This report is organized into two parts. Part I begins with a chapter defining
terminology and presenting the conceptual framework for the study. Chapter 2
elaborates the data collection methodology including sampling procedures,
questionnaire design and development, and survey interview methodology.
Chapter 3 introduces and describes the data analysis techniques and methods for
developing the typology. Part II presents the findings. Chapter 4 provides an
overall description of agriculture in the agro-ecological zones where the
survey took place and the climatic conditions for the year of the survey. An
appendix of summary tables by agro-ecological zone and a glossary of
terminology used in this report accompany this chapter. Chapter 5 introduces
the typology containing detailed descriptions of each type of farming system
which highlight sub-system components and their interactions. Chapter 6
compares and contrasts the types, describes criteria for their identification,
and discusses the implications of these findings for targeting research
development and transfer of technological innovations. Chapter 7 concludes
this report with a summary of findings and recommendations for the use of the
baseline data set and farming systems typology.


















PART I

















CHAPTER 1 :


CONCEPTUAL FRAMEWORK




Targeting technology research and development of the Aridoculture Center for
its farming clientele requires a clear set of conceptual tools. Two issues are
involved in elaborating these tools: (1) the definition and interrelationship
of concepts; and (2) the way in which they contribute to an understanding of
the farming population. Working toward that understanding requires an
iterative process of dialog between the conceptual framework and actual farming
conditions. As a first step in building that understanding, this chapter will
introduce a set of concepts and the logic of their application.


The Concept of System

Central to the Farming Systems Research and Development Approach is the concept
of system (Shaner et. al., 1982; Marcotte and Swanson, 1987; FAO, 1989;).
However, its familiarity as a concept within different disciplinary traditions
and applied contexts has lead to confusion within the literature and between
colleagues (Gibbs, 1985; Brossier, 1987). In order to clarify the role that
"system" plays in the approach elaborated here, it is useful to recall the
generic properties of systems: (1) systems are holistic integration of
interrelated elements; and (2) although set within given boundaries, they are
open to influences from their environments (Shaner, 1982; Conway, 1986; FAO,
1989).

The popularity of the systems concept among agricultural scientists stems,
pragmatically, from the desire to effectively grasp the dynamic totality of
agricultural development. The interrelationships between the factors which
combine to generate agricultural products and sustain the lives of those
producing them are complex. They cannot be simply reduced to a few causal
relations between variables or a function of one of its components (Baker and
Norman, 1990). Indeed, the whole is greater than the sum of its parts.
Agricultural production depends on the interaction of all physical, biological
and socio-economic factors (Shaner, 1982; Brossier, 1987). A change in one
element leads to changes in others and the combined effect reverberates
throughout the whole. The concept of system provides a general framework in
which to account for this dynamism and complexity.

Agricultural systems, however, have been conceived at many levels. These
systems can be understood as hierarchically-nested in one another from the
physical and biological systems at the field level nested in a cropping system
to a farm household system nested in community and infrastructural systems
which compose the national agricultural system. In fact, a multiplicity of











agricultural system hierarchies have been specified (e.g., Hart, 1982; Gibbs,
1985; and Conway, 1986). This multiplicity has led to confusion between
different approaches (Gibbs, 1985). The system level (and, consequently, the
hierarchy in which it is nested) which becomes the object of study and
intervention is often dependent on the disciplinary background of the
researcher (Gibbs, 1985) and the technological biases inherent in the
agricultural development approach (Oasa and Swanson, 1986). The open
relationship between systems and their environments is the conceptual root of
this problem.

In their review of farming systems literature, Brush and Turner (1987) suggest
two caveats to systems analysis which provide guidance in the clarification of
the systems approach elaborated here.

"First, systems must be artificially limited if analysis is to
proceed, and second, described systems are, therefore,
heuristic and artificial analytical devices rather than
natural phenomena. Although the systems approach tends to
push the investigator toward descriptive holism, effective
limits on the scope of system modeling are imposed by problems
of scale. The larger the descriptive model the weaker it
becomes for describing specific behavior. Finer-grained
models, on the other hand, may lack the explanatory power of
more general ones. It needs to be emphasized that the systems
approach is descriptive rather than explanatory. It helps
identify what processes exist and how sets of interrelated
components function together." (Brush and Turner, 1987:27;
emphasis in original)

The point is that systems are useful, but artificial constructs. As such,
choices must be made to limit the scope of the work for heuristic purposes.
Furthermore, the consequent product of this study will be descriptive of the
targeted farming systems in the region, rather than an elaboration of causal
models or policy prescriptions (Whyte, 1983). It is expected that policy
decisions and causal modeling will be informed by and build out of this core of
farming system descriptions. The following sections will describe the choices
and the conceptual limits which have been imposed in this work.


The Farming System and Its Environment

Since the core of confusion in farming systems research involves the level at
which the farming system is defined, it is useful to introduce a hierarchy of
agricultural systems. Hierarchical presentations are founded on the idea that
systems are nested in one another, each higher level often assumed to encompass
a larger geographical space or range of phenomena. This simplifying assumption
allows for greater facility in presenting relationships between agricultural
systems, but involves an explicit choice in how real world conditions are to be
conceived and described.

Two issues are involved. First, as one ascends a systems hierarchy, a
progressive centralization of system functions occurs. The consequence of this
is reductionist analyses (Marcotte and Swanson, 1987) or, simply, less capacity
to describe specific behavior (Brush and Turner, 1987). This has important
implications for analysis since system' functioning at one level (the farm
household) may be seriously affected by system components at a much higher











level (national agricultural policy), yet the interrelationships have been
obscured by the nested hierarchy. Second, there is no one hierarchy. For
example, agricultural systems have been conceived not only with reference to
productive units as in traditional farming systems research, but also with
reference to specific commodities as in commodity systems research (Friedland
et. al., 1981); agro-ecological zones (Conway, 1986); or land-use patterns as
in the INRA Aridoculture Program Strategy (Centre Aridoculture, 1993) and in
ICARDA's Strategic Plan (ICARDA, 1989). Each of these highlights certain types
of interdependencies and obscures others.

The agricultural systems hierarchy presented in Figure 2 does not resolve these
issues. Rather, it provides an overarching frame of reference for the farming
systems approach presented here and a means to integrate analyses across system
levels. Since the objective of this work is to characterize the systemic
conditions which influence the adoption or rejection of new agricultural
technologies, a focal point at the level of the farm household has been chosen.
This focus assumes household units of production and consumption, where all
decisions concerning the allocation of human and material resources must
ultimately be reconciled. Norman and Gilbert (1982) provide a concise working
definition for the farming systems described in this work.

"The specific farming system adopted by a given farming
household results from its members, with their managerial
know-how, allocating the three factors of production (land,
labor, and capital) to three processes (crops, livestock, and
off-farm enterprises) in a manner which, with the knowledge
they possess, will maximize the attainment of their goalss)"
(Norman and Gilbert, 1982:17)

This conception of a farming system is open to both the socio-economic
environment, usually considered in terms of higher system levels, and the bio-
physical environment. The bio-physical environment can be broadly-defined in
terms of agro-ecological zones at higher system levels and more narrowly-
defined in terms of biological and physical interactions at lower levels.
Although higher system levels are not systematically developed, they are
referred to contextually and provide a backdrop for the analysis. The lower
system levels involving the management of crops, livestock, and non-farm
activities are developed as nested in the farming system and referred to for
purposes of clarity as sub-systems (see Figure 3). Household resources of
land, labor and capital, although presumably allocated across sub-systems as
the wheels of production are set in motion, are kept analytically distinct.
Physical and biological systems at the field level are not systematically
developed. Emphasis is placed on the interactions and interdependencies at the
farm household level between components of the sub-systems and the resources
allocated to them.

Conceptual models (Shaner et.al., 1982) or flow diagrams (Garrett, 1984) are
often presented with more detailed specification of sub-system components and
their linkages. This is an important way of suggesting interdisciplinary data
needs and ultimately leads to the quantification of material flows between
components. However, as Brush and Turner (1987) point out, it is easier to
describe the structure of the system than the flow of materials that makes it
function. Although many of these flow diagrams have been referred to in the
development of criteria for the typology in this study, such detailed
specification must remain open until specific types of farming systems have
been fully identified and described.








































Biological and Physical
Environments


Figure 2:


Hierarchy of Agricultural Systems


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Figure 3: The Farming System


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Cropping
Sub-System .



Farm
Household
Resources



A


. . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . .











Targeting Domains


Applying system concepts in agricultural development requires an intermediary
set of conceptual tools which provide a bridge from the theoretical
understanding of farming systems to the applied targeting of technology
research and transfer.

As with the system concepts, considerable confusion also exists concerning the
specification of targeting concepts (Wotowiec et. al., 1988). Common practice
within the farming systems research and extension tradition has been to quickly
survey a region and establish recommendation domains on the basis of easily
identifiable characteristics (Simmonds, 1985). Recommendation domains are also
specified at other stages of the technology research and transfer process.
Consequently, Wotowiec et. al. (1988:77) note, the term "'recommendation
domain' has been stretched to cover too many situations and too many different
purposes". Such hastily conceived recommendation domains are founded on the
assumption of homogeneity among both farmers and farming conditions. They
further note that homogeneous groups should not be identified until
"researchers have an adequate understanding of the variability inherent in
local farming systems, usually not accomplished early in the work in an area"
(Wotowiec et. al., 1988:74).

The following specification of targeting domain concepts is based on Wotowiec,
et. al., (1988). This refinement of the concept of domains allows for clear
differentiation between targeting applications, while not loosing sight of the
diversity of farm households and farming systems in homogenously targeted
groups. They stress three points: (1) the definition of a domain should
recognize a problem focus; (2) the concept of domain should be linked to the
farming system research and development sequence; and (3) socio-economic
considerations should be included. Three types of domains are offered in order
of their application in the farming system research and development sequence:
research domains; recommendation domains; and diffusion domains.

Research Domains: Research domains refer to environmental (agro-ecological and
socio-economic) ranges defined in terms of a particular problem to be
addressed. They set the initial context for research and development
activities, often specified in terms of bio-physical factors. Broadly-defined,
the research domain for the Aridoculture Center is the improvement of
production and incomes of farm households in the arid and semi-arid regions of
Morocco. For purposes of this study, the research domain has been geo-
poltically limited to the province of Settat and the cercle of Ahmar in Safi
province.

Within this geo-political area, further refinement of the research domain has
been specified in terms of geo-social location, soil types and cropping
potentials which distinguish four agro-ecological zones (see Chapter 4). It
should be noted at this point, that while geographic boundaries facilitate
conceptualization, they are not absolute. There is a degree of interspersion
among these research sub-domains. Further, the problem focus for each sub-
domain has been narrowed in that the particular bio-physical constraints of
each agro-ecological zone studied limit the range of possible solutions. It
should be noted that the research domains defined as systems in the
Aridoculture Program Strategy (Centre Aridoculture, 1993) parallel the logic
defining agro-ecological zones in this study. However, their unit of analysis
is limited to the level of a farmer's field, as opposed to a whole farm system.


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Research domains correspond to the diagnosis phase of farming systems research
and development, that is, the research agenda development phase in the
Aridoculture Program Strategy. The object of this diagnostic study of the
research domain specified above is to provide an adequate understanding of the
variability in regional farming systems in order to facilitate the targeting of
homogeneous farming systems. As more is learned about farming systems in the
region, research domains can be more narrowly defined for purposes of targeted
sondeos and on-farm diagnostic trials leading to the identification of
recommendation domains.

Recommendation Domains: A recommendation domain is a group of farm households
or farming systems with a common specific problem for whom a tested solution
may be adapted to their needs and potentials. It assumes a homogeneity of the
group, both socio-economically and agro-ecologically, with reference to the
problem and to the solution. A recommendation domain cannot be established
prior to problem diagnosis and solution development. Recommendation domains
are both tentative and variable in nature depending on problem definition and
the evolution in the adaptive research process which defines the solution.
They can be modified over time as new information is gathered and improved
understanding of the problems achieved. This usually evolves as researchers
and farmers develop shared perceptions of problems and their potential
solutions. Since a recommendation domain is limited to a specific problem and
its solution, a farm household may be involved in several recommendation
domains depending on the number of problems and solutions addressed.

There is no recommendation domain if no solution is proposed for a specific
problem. This study will not specify recommendation domains since it is beyond
the scope of this work to propose solutions for specific farming system
problems. However, it will focus efforts in that direction by identifying
potential problem areas and suggesting potential solutions for detailed
diagnostic investigation, i.e., narrowing the range of a research domain which
when diagnosed with farmers may directly lead to a recommendation domain.

Diffusion Domains: A diffusion domain is a network of interpersonal
communication through which information and knowledge about solutions to
problems faced by farm households and their farming systems circulates.
Although a diffusion domain is logically most active at the end of the farming
systems research and development sequence, early identification of these
networks can facilitate the targeting of these information flows. Who, whether
men, women or children, is involved in which communication networks is
important. This study will aid in the identification of the relevant target
groups. However, it is beyond the scope of this study to identify the networks
themselves.


Farming System Typologies

The objective of this study is to identify, categorize and describe farming
systems in order to provide the basis for the development of recommendation
domains. To achieve this task, a typology is developed which identifies
homogeneous groups of farming systems. The characteristics described for each
type of farming system will enable precise specification of more narrowly-
defined research domains and focus the dialog between researchers and farmers
concerning problem diagnosis and solution development.


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A typology is a multi-dimensional classification based on relations of
contiguity or similarity. Like a system, it is an artificially constructed
conceptual tool which facilitates comprehension and analysis. As such, it has
similar limitations. However, these tools have different, although
interrelated, purposes. On the one hand, the systems approach provides a way
of thinking about the empirical world. Typologies, on the other hand, aid in
the application of those modes of thinking to the diversity of observable
conditions. Nevertheless, "the process of typology construction and use
includes both concept formation and data analysis" (Bailey, 1973:31).

The use of typologies is gaining credence among farming system practioners.
Increasing recognition of important differences between farm operations within
the same area (Garrett, 1984; Wotowiec et. al, 1988; Bagchee, 1990) has led
practioners to develop methods which reduce the heterogeneity of farming
systems into relatively homogeneous types (Capillon, 1986; Low, 1986a;
Jamtgaard, 1989; Marz, 1990). Farming system typologies have been most
frequently developed on a disciplinary root which attempts to incorporate an
interdisciplinary perspective. Early typological frameworks building on
Ruthenberg (1980) were essentially agronomic, closely linked.to the technical
aspects of agricultural technologies. Typologies building on their work tend
to remain at the cropping system level with socio-economic parameters as
exogenous, when considered. More recent typologies, such as those of Capillon
(1986), Low (1986a), and Marz (1990) have had a more economic focus. However,
most typologies developed within the farming systems tradition exhibit little
conceptual development. Typically, when practioners incorporate a socio-
economic element they simply take a single indicator (usually a size referent)
and designate two or three groups (Lopez-Pereira et.al., 1990; Kabay and
Zepeda, 1991; Langworthy, 1991; Franzel, 1992). Although large and small
provide provide a basis for obvious differences between farms, no systemic
logic is maintained by such designations.

In an assessment of the farming systems research and extension approach,
Bagchee notes that "there is very little guidance in the literature on what
exactly one can take as the factors or dimensions that differentiate one
farming system from another" (Bagchee, 1990:129). Although typologies can be
derived theoretically or empirically, their use in farming systems research and
development has evolved because of initially poor pre-conceptions of existing
farming systems. Consequently, farming system typology construction
constitutes an inductive refinement in the operationalization of the farming
systems conceptual framework. A fully interdisciplinary theory of farming
systems has yet to be developed.


Typology Construction: Concept Formation and Criteria

In moving from our systems theoretic framework to the identification of
empirical farming systems, several questions are posed. How many types are
there? What are the differentiating criteria? How many criteria are
necessary? How will farm households be assigned to different groups? These
questions cannot be completely answered at this stage. The answers are
ultimately a product of the dialog with existing conditions. However, previous
research at the Center, the farming systems literature, and literature on
typology construction involving farm households can be combined to provide
sufficient guidance to elaborate a conceptual framework to initiate that
dialog.


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Although the following set of concepts and criteria which inform the conceptual
development of the typology constitute tentative working hypotheses, they are
not formulated for explicit testing. Rather, they are suggestive of data
collection and analyses needs to ensure the development of a useful farming
systems typology. Farming system typologies provide a context for hypothesis
testing (i.e., clearly specified research domains); working hypotheses simply
guide the process of typology development (research domain specification).

Typology development requires a specification of the scope of the phenomena to
be classified, a conception of the criteria to be used in classification, and a
method to apply the criteria in the identification of different types. The
scope of the phenomena has been defined above. Farming systems by their nature
are composed of complex interdependencies centering on the farm household. The
range of potential diversity in farming systems requires that criteria for
classification focus on multiple dimensions of the components involved in those
interdependencies. Although a single criterion may serve to distinguish
different farming systems in a specific place at a specific time, it is not
sufficient to discern the complexity of interdependencies which define those
different farming systems. At the outset, then, a listing of potentially
relevant criteria must be specified.

Before developing the list of conceptual criteria, a clarification should be
made. The most common criteria used to distinguish between farming systems has
been that of environmental or agro-ecological zones (Winkelmann and Moscardi,
1982; Hart, 1982; Wotowiec et.al., 1988; Jamtgaard, 1989; Gillard-Byers and
Blackie, 1990; Bagchee, 1990). The conflation of the concepts of zone and
system is at the heart of most bio-physical typologies. These typologies
assume that the homogeneity of a farm household level system is a direct
consequence of bio-physical factors of its environment. Agro-ecological zones
have a place in the hierarchy of agricultural systems (see above), however,
they are not a substitute for the farm household level. For example, Low
(1986a) found that there was greater variation between types within homogeneous
agro-ecological regions than between regions. In this study, agro-ecological
zones provide the environmental context in which farming systems at the farm
household level are defined.

Interdependent farming system components are individually best understood
within their respective disciplinary fields. The following consideration of
criteria for differentiating farming systems at the farm household level,
therefore, draws heavily on social scientific literature outside of the bio-
physically-driven farming systems tradition. Although a vast literature exists
on the processes of agricultural development and differentiation of peasant
households (Garrett, 1984), this review is limited to recent literature
focusing on farming systems and farm household typologies. Realizing the non-
observability and historical nature of causal mechanisms differentiating
farming systems (Whatmore et.al., 1987a; White, 1989), the focus will be on
farm household and related sub-system indicators frequently linked to those
processes of development and differentiation.

The most frequently mentioned type of criteria is farm household labor. Social
scientists have focused on this key component of peasant farming since Lenin
(1960) and Chayanov (1966) described the dynamics of agricultural development
and differentiation early in this century. Indeed, Chayanov called these farm
households, family labor farms. The single most identified distinguishing
factor influencing farm household behavior has been off-farm wage labor
activity (Moore, 1984; Pascon, 1986; Low, 1986b; Shand, 1986; Crummett, 1987;


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Whatmore et.al., 1987b; Eboli and Turri, 1988; Tully, 1990; Hebinck, 1990).
Consequently, the concept of farm household member opportunity costs is gaining
wide acceptance (Low, 1986b; Tully, 1990). In addition, labor criteria should
also include: (1) a specification of the gender and age division of labor; (2)
the types of tasks assigned to these different categories; (3) the duration and
seasonality of labor allocations; (4) the intensity of the labor process; and
(5) the amountand timing of non-household labor hired or exchanged (Coughenour
and Swanson, 1983; Moore, 1984; Moock, 1986; Poats et.al, 1988; Singh, 1988;
Hebinck, 1990; Marz, 1990; Tully, 1990).

Wealth is often suggested as an important indicator, of differentiation. The
level of productive and consumptive durable goods indicates the cumulation of
advantages and disadvantages which the dynamics of the agricultural system
elicits over time within farm households (White, 1989). This capital has come
to predominate over land ownership as the key dimension of rural social
stratification. The advantage of these factors as indicators is that they
provide a measure of the potential for capitalization from non-farm (i.e., from
wage labor and non-agricultural activities) as well as farm sources. These
criteria should include: (1) the possession of farm equipment from tractors to
hoes; (2) debt or credit levels; (3) non-agricultural investments in businesses
or housing; and (4) the possession of consumption durables from radios and
televisions to cars (DeWalt and DeWalt, 1980; Garrett, 1984; Low, 1986b; White,
1989; Hebinck, 1990; Tully, 1990).

Land has been a standard criteria for distinguishing between farm households.
As the fundamental environmental resource involved in all agricultural systems
it is often included in the design of typologies with the designations of
small, medium and'large. Due to such overly'simplistic usage, it is coming
into increasing disrepute (Whatmore et.al., 1987a; White, 1989; Bagchee, 1990).
Size categories on their own do not account for land quality in terms of soil
type, water availability or slope. Tenure status is also an important
dimension indicating the social relations governing the use and output of
accessible land. However, different forms of tenure status, in themselves, do
not necessarily indicate the value of the land to each of the partners in the
relationship (Whatmore et.al., 1987a). In addition, intra-household relations
concerning the onwership and use of land must also be taken into account
(Moock, 1986). Criteria indicating the land resources available to each
household should include: (1) the amount of land operated; (2) the quality of
that land; and the social relations governing the use of the land (Garrett,
1984; Pascon, 1986; Moock, 1986; Hebinck, 1990).

Household demographic composition has gained increasing attention as a key
criteria distinguishing farm households and their modes of functioning. It has
long been recognized that stage in the family life cycle and generational
composition have an important impact on how the farm is run (Chayanov, 1966).
The number, age and gender of household members has a direct impact on both
labor resources and consumption needs (Pascon, 1986). Since the farm household
is the primary unit of analysis in this study, it is important to specify what
constitutes a household. Household studies continue to point out not only that
family may not be an adequate concept, but that household is also an ambiguous
concept (Netting et.al., 1984; Wilk, 1989; White, 1989). Here, we define the
household as composed of all persons resident in the producing and consuming
unit. This may include non-family related persons. Non-resident family
members may also be significant contributors/consumers. These need to be
accounted for separately. Criteria should include: (1) the number, age and
gender of all household and non-resident family members; and (2) their family


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(or non-family) relationships (Netting et.al., 1984; Moock, 1986; Crummett,
1987; Eboli and Turri, 1988; Poats et.al., 1988; Wilk, 1989; White, 1989).

Farm and family goals are frequently mentioned as key determinants of farming
system dynamics. These goals provide the ultimate motivation for engaging in
agricultural production. They involve values attached to rural residence and
lifestyle as well as particular productive activities (Long, 1984). Like other
causal mechanisms, such values are difficult to observe directly. Although the
frequently hypothesized dichotomy between self-sufficient peasants and market-
oriented producers does not exist, production objectives often serve as useful
indicators for household goals. Criteria to indicate these goals include: (1)
the types of production pursued; and (2) the level and form of self-sufficiency
(Capillon, 1986; Eboli and Turri, 1988; Long and van der Ploeg, n.d.).

Closely linked with farm and family goals is the historical trajectory of the
farm operation. Although seldom used in typology development because of the
retrospective data collection and complex analyses involved, it is important to
the placement of the farming system on an evolutionary path and thus mapping
the dynamics of system development (Capillon, 1986; Diaz et.al., 1990).
Criteria for indicating such trajectories could include all of the criteria
mentioned here for previous stages of farming system development.

Crop composition and production techniques are the most frequent criteria used
to differentiate farming systems. Land use patterns, whether perennial, annual
or seasonal provide important information on how a farming system is managed
(Ruthenberg, 1980). The types of crops grown further indicate the objectives
of production whether for home consumption, animal feed or sale. Crop
production techniques and the use of purchased inputs are often distinctive of
different farming systems. Crop yields, although essentially a performance
indicator, are sometimes used to differentiate types. The criteria should
include: (1) the area committed to each crop produced; (2) the rotation
patterns of these crops; (3) land preparation techniques; (4) the use of
purchased inputs; (5) cultivation practices; (6) harvesting techniques; (7)
crop yields; and (8) the end use of each crop (Ruthenberg, 1980; Hart, 1982).

Livestock composition and production techniques are less often mentioned, but
equally important criteria (McDowell and Hildebrand, 1980). Herd size and
stocking rate indicate the scale and intensity of livestock activities. Animal
purchases and sales distinguish between the market and subsistence purposes of
animal production. The combination of types of animals may be suggestive of
multiple purposes. The distinction between local and improved breeds, the use
of purchased feeds, and the use of veterinary services are indicative of
production techniques. An important additional criteria often missed is the
extent and form of herd ownership. Criteria should include: (1) the number and
type of livestock; (2) stocking rate; (3) purchases and sales of livestock; (4)
the feeding schedule; (5) the use of purchased feeds and veterinary services;
and (6) the social relations concerning the ownership and management of
livestock.


This discussion of conceptual tools has provided the theoretical basis for the
approach elaborated in this report. The operationalization of these concepts
and selection of criteria for the construction of a systems-appropriated
typology in the aridoculture region is the subject of the next two chapters.


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CHAPTER 2:

DATA COLLECTION METHODOLOGY




The operationalization of the concepts and criteria suggested in the preceding
chapter involved selection of a sample of farm households and development of
survey instruments for structuring the dialog with the farming population.
Following the logic of our dialectical approach, these two parallel activities
were undertaken with the aid of a broad range of disciplinary and local
expertise. The chapter begins with the formulation of the sampling framework
specifying sample selection procedures. Sample representivity is discussed.
Then, building on existing knowledge of the region and the conceptual
framework, the processes involved in elaborating the survey instruments
(questionnaires, interviewers, and Arabic script) are described. These two
activities are drawn together at the point of contact between researchers and
the farming population with a discussion of survey procedures. The chapter
concludes with a specification of the procedures involved in data entry,
cleaning, and preparation of files for analysis.


Development of the Sampling Framework

The sampling framework was developed in collaboration with the Service des
Enquetes et des Etudes Economiques et Statistiques (SEEES) of the Settat and
Safi Direction Provinciale de 1'Agriculture (DPAs). Two issues had to be
resolved: (1) the areas to be surveyed; and (2) the size of the sample.

Earlier farm-level work at the Aridoculture Center had primarily focused on
narrowly defined areas of study (Benatya et.al., 1983; Rafsnider and Laamari,
1988) within Settat and Safi Provinces. More regionally based studies had only
been conducted in Safi Province for the region of Abda (Primov et.al., 1987a
and 1987b) and the Abda plain and Sahel (Akhrass and Sefrioui, 1990). In order
to complement work already completed and increase the range of coverage to the
zones of action of our agricultural development partners, four areas were
identified on the basis of rainfall, soil types, land uses, and geo-political
considerations: three comprising Settat Province, following a decreasing
rainfall gradient from the coast in the northwest of the province to the
southeast; and one in Safi Province, the Cercle of Ahmar. Detailed
specification of these regions in terms of their agro-ecological zonage is
presented in Chapter 4.

Sample size determination involved the consideration of several factors.
Collecting the amount and quality of information necessary at the farm
household level would be time-consuming, and costly. Sample size had to be
restricted to that which was manageable within a few months. Stratification


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criteria which cross-cut the diversity of conditions were necessary for the
efficient representation of the broadest range of circumstances. Although the
four specified regions were considered broadly homogeneous, the SEEES of the
two DPAs provided a more refined zonage of these regions accounting for major
bio-physical and socio-economic features. This first level of stratification
ensured that a wide variety of location-specific conditions would be included.

In order to facilitate survey logistics, reducing the time and costs of data
collection, it was decided to use villages (douars) as a basis for clustering
sampled farm households. However, the population of douars, even within the
same area, offer a variety of contextual (bio-physical and socio-economic)
characteristics. It was decided that cluster stratification would be necessary
to ensure coverage of this source of diversity. The number of households in a
douar who were actually operating farms was selected as the douar-level cluster
stratification criterion since it was the most relevant douar-level
characteristic available for the population of douars.

Surveying all farm households within the stratified douars was not possible.
Initially, farm households were to be selected according to their proportion of
the population in each cluster on a systematic basis by the size of farm
household operation measured in hectares. However, since the ultimate concern
in typology development is an adequate representation of diversity in farm
household circumstances, further sample stratification and weighting were
required in order to ensure sufficient numbers of cases across all size
categories (see Step 5, Stage Two below). Weighting allows for over-sampling
of certain population segments (clusters or strata) according to the
probabilities of any particular case being selected. This induced sample bias
can be corrected through a probability-based calculation (re-weighting) in
order to determine estimates of population parameters.

Although it was not possible at the outset to determine how many types of
farming systems would be found in each zone, it was assumed that from three to
six types could be potentially identified. Considering that 15 is the standard
minimum number of cases for the stabilization of group means and that 30 is
considered optimal, 120 farm households was determined to be a reasonable goal
for each zone. Under conditions of proportional random sampling within the
specified clusters and stratification criteria (i.e., rounding up to whole
units), a sample size of 396 for the Province of Settat and 126 for the region
of Ahmar was determined. This represents an estimated 0.8 percent of the 1989
population of farm operations in each region based on extrapolations from the
1977 Census of Agriculture and the 1982 Census of Population (Ministere du
Plan, 1977 and 1983).


Sample Selection

Sample selection involved a two-stage cluster and proportional sampling
procedure with a weighting adjustment (Babbie, 1986). The following specifies
the steps followed in that process.

Stage One:

1. The Settat and Safi DPA's provided lists of douars by rural commune and
organized into bio-physical and socio-economic areas (11 for Settat and
3 for Ahmar) containing the number of farm operations in each douar
according to the 1977 Agricultural Census.


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2. Within each of the 11 areas in Settat, all douars were divided into
three equal groups according to the number of farm operations (i.e.,
small-, medium-, and large-size douars, each group containing one-third
of all douars in that area). One douar was selected randomly from each
group (using a random number table), totaling three douars for each
area and 33 for the province as a whole.

3. In Ahmar, two of the areas were much larger and the third much smaller
than those of Settat. Within each of the two large areas, douars were
divided into five equal groups according to the number of farm
operations within each douar (i.e., each group contained one-fifth of
all douars). Again, one douar was selected randomly from each group
(using a random number table). Within the small zone, one douar was
selected at random. Thus, 11 douars were selected for this region.

Stage Two:

4. Lists of current farm operators and their hectares operated were
compiled with the aide of the local authorities (caid, sheikh, and
moqaddem) and verified with them for each of the selected douars. Two
of the douars included on the lists established in Stage One were not
found, having either been renamed or incorporated into another douar
since the 1977 Agricultural Census. These douars were randomly
replaced by equivalent douars from the douar lists.

5. Preliminary analysis of these lists showed that the distribution of
hectares operated is highly skewed. Roughly half of these farm
operations are less than 5 hectares. Given limited resources which
restricted the ultimate sample size and in order to ensure an adequate
number of cases of medium-sized farm operations, it was decided that
the sample should be weighted in favor of those 5 hectares and above to
provide adequate cases for typology construction. The sample was
weighted as follows: farm operations of less than 5 hectares composed
27 percent; and farm operations of 5 hectares or more, 73 percent.

6. The number of farm operations selected from each douar was based on the
proportion of farms in that douar's area and size category (according
to the 1977 Agricultural Census). Farm operations were then drawn
systematically (using a random number table to provide a starting
point) from lists ranked according to their hectares operated within
each douar and hectare size category.

This sampling procedure provided each farm operator with an equal chance of
being selected within his/her respective hectarage categories. With re-
weighting, conclusions can be drawn about the population as a whole. In
addition, sufficient cases are available across size categories for the
construction and analysis of representative farming systems.


Sample Representivity and Population Dynamics

At the end of the interview process, questionnaires had been completed for 503
farm households of the original 522 in the sample framework. This represents
the extremely high response rates of 97 percent for the province of Settat and
95 percent for the region of Ahmar. Few refusals were encountered, but 51 of


-21-











the listed farm operations did not exist as operating farms. These listed
operators either had died and their families left the area or had rented out
their land and moved. When equivalent farm households could be identified from
the original douar lists (i.e., the next farm operation on the ranked list),
they were replaced, otherwise the case was dropped.

Some- observations concerning population dynamics and official population
statistics are in order at this point. The most recent agricultural census was
conducted in 1977 and the most recent population census in 1982. These census
documents served as the basis for sampling the population of douars. This
posed some problems for establishing a representative sample in 1990.
Nevertheless, taking into account recent population estimates from the
Ministere du Plan (1986), sample douars were identified and assigned sample
sizes. It was found that two of those douars (5%) no longer exist, and one
douar is on its way to disintegration, the majority of inhabitants having moved
to town. Although the rural population grew between 1971 and 1982, study of
aggregated commune level data from the two censuses demonstrate that migration
from rural areas had occurred between 1977 and 1982 (Minist&re du Plan, 1977
and 1983). It is reasonable to assume that this trend continued at a
diminished rate since the end of the drought in the early 1980s.

Out migration varied according to the sample areas, however, population
proportions shifted no more than three percent in any one area over the period
1977-1982. The assumption of proportionality used in sample selection is based
on the farm operator population distribution of 1977. Although farm households
from these sample areas have been regrouped into agro-ecological zones where
proportional variations are counterbalanced, population estimates in this
report may be affected by this source of error.

In order to compensate for the dated sampling information in the selection of
douars, current farm operator lists were obtained during the winter of 1990
from local officials at the douar level. These lists posed another source of
potential error. As noted above, 51 of the farm operators listed by the local
officials (10% of the sample list) were not farming in the cropping year 1989-
90. Rather than constituting lists of operating farms, the local officials
apparently had provided lists of those having access to land in the douar, not
all of which were exploited by the listed operator. In fact, the officials
were not always cognisant of which inheritor was operating the land of a
deceased farm operator. The replacement of 32 of these listed farm operations
during the course of the survey constitutes an important correction factor for
the reliability of population estimates.

The lists of farm operators also included the local official's estimate of the
size of operation which provided the basis for a systematic sampling of all
farm sizes. It was on the basis of these lists that a weighting of the sample
was affected to ensure sufficient cases of medium-sized farm operations.
Comparing local officials' estimates of farm size and the actual data collected
on farm size, one notes a discrepancy. The correlation between officials'
estimates and the collected data is .81 (i.e., 65% of the variance is
explained). On average, the local officials correctly placed only 77 percent
of the cases in the two categories of more and less than 5 hectares.

Given that the land exploited by a farm operator often changes from one year to
the next, these estimates of the local officials are remarkably accurate. For
purposes of this survey, however, it is important to note if there are sources
of variation in the sampling criteria which could affect population estimates.


-22-











This does not appear to be the case. According to the officials, farm
operations averaged 11.6 hectares, whereas, the data collected show the average
to be 16.8 hectares. In fact, in each of the areas sampled, the local
officials had underestimated the average hectares exploited by farm operators.
This underestimation is probably related to the continuing out migration of
farm families and the consequent consolidation of holdings. It does not pose a
serious problem for overall sample representivity of current conditions,
although the underestimation of local officials should be noted.

Only in one douar were serious representivity problems posed. Not only did the
local official seriously miss classify operations by hectarage category (only
59% correctly placed with a correlation of .51), but it is also this same douar
where the greatest concentration of interview refusals were encountered. These
refusals were from very large operators and contribute to an underestimation of
the level of differentiation. This douar is in the coastal agro-ecological
zone (Zone I) specified in Chapter 4.

Overall, the size of operation criteria was systematically underestimated by
local officials. Although the diversity of small operations may not be as
fully represented as originally expected, re-weighting of the two officially-
defined size categories for the analyses and computation of population
estimates in this report, with the exception of Zone I, adequately compensates
for this bias.


Survey Instrument Development: Interview Schedules

Following Collinson (1980), the development of the interview schedules was
initiated with the circulation of a list of data points (drawing on the
conceptual framework outlined in the previous chapter) among Center scientists
from various disciplines and program specialists of the Settat DPA. Comments,
suggestions of additional items, and modifications to account for local
practices and conditions were elicited. This knowledge base provided a wide
array of detailed and distinctive farming system characteristics relevant to
particular disciplinary concerns and local conditions.

Given the amount of interview time necessary to inventory all farm household
resources, allocations, and production practices, decision-rules were needed
for prioritizing the data to be collected. Since the objective of the data
collection effort was to distinguish different types of farming systems, the
detailed precision needed was considered to be subordinate to collecting a
broad range of qualitatively distinct characteristics. Sufficient detail to
describe qualitative differences or orders of magnitude should be collected to
determine the extent of interdependencies between system components. Detailed
specification was limited to the sequence of cropping practices for the largest
parcel of each of the four cereals and two food legume crops. It was
considered less important for the purposes of this study to precisely quantify
all distinctive characteristics or explain why they existed. Such refined data
collection could be pursued in future studies (with greater confidence) within
the context of specific types of farming systems.

Taking these considerations and collaborator suggestions into account, the
initial set of close-ended interview schedules were drafted in French. Three
questionnaires were designed: one for the farm operator focusing on farm
resources and their allocation; one for the spouse focusing on household
resources, allocation and consumption; and one for post-harvest data collection


-23-











on production. After drafting, they were circulated among our collaborators
for further feedback and refinement. A revised draft of the survey instruments
was then developed.

In order to ensure the quality of data collected, a great deal of care was
taken in the design of the questionnaires and questionnaire items. The
objective was to facilitate responses of farm operators and their spouses and
the recording of those responses by interviewers. This was achieved through
either avoiding questions of a sensitive nature when possible or attempting to
build confidence in the interviewer and the survey situation through item
ordering. Many questions were formulated in a disaggregated fashion (i.e., by
parcel or individual household member), leaving for later the reconstitution of
specific indicators. In addition, items were pre-coded for computer entry and
analysis.


Survey Instrument Development: Interviewer Training

As important as the interview schedules are the interviewers themselves. These
are the living instruments which actualize the data collection process. A
competitive recruitment was instituted. Although agriculture students were
predominantly selected, interpersonal communication skills was the prime
selection criteria. Interviewer training began with a brief overview of the
research objectives and the important role which the interviewers play in the
completion of the study. This was followed with an introduction to the draft
survey instruments which interviewers were expected to master. Interviewer
training lasted about two months.

Emphasis was placed on the quality of the data collected, that is, the meaning
and importance of data reliability and validity. This objective guided the
training process which involved two major thrusts: (1) developing an
understanding of the information sought; and (2) standardizing the data
collection process. Center researchers from several different disciplines were
involved in developing the interviewers' understanding of technical
agricultural terms, concepts and practices applicable to the region. In order
to standardize the data collection process, interviewers were encouraged to
develop one mind as to the meaning and import of each question. Not only was
it expected that each question would be presented in the same manner by each
interviewer, but also that their coding of responses would be identical.
Ambiguous or out-of-coding-range responses were to be noted on the
questionnaires.

The questionnaires were studied in detail with discussions of each item
focusing on what it was designed to measure and why. The training also
involved the reasoning behind each question and the logic of the coding
systems, including the specifications for various types of missing values.
Sessions were also held on proper interview procedures. Training and practice
included contact establishment, humility, probing without leading, and cross-
checking techniques.

Under the guidance of Sociology section researchers, interviewers were given
the task of preparing the Arabic script containing the actual form of how the
questions were to be asked. This process led to further revision of the survey
instruments as questions and responses became formulated in the local lexicon
and adapted to local circumstances. Interviewers practiced interviewing each
other to aid in memorization as well as to standardize styles and


-24-












understandings. In addition, a glossary of local terminology was developed
including equivalents for the local calendar and units of measure.


Survey Instrument Development: Pre-Test

The next step in questionnaire, interview script, and interviewer development
was the pre-test. Under the direct supervision of section researchers, the
interviewers applied the questionnaires to a set of ten farm operators and
their spouses. These interviews were conducted in pairs with a researcher
present. While a number of problems were encountered, on the whole the survey
instruments were found to be sound. De-briefings were conducted involving all
researchers and interviewers resulting in revisions to both the questionnaires
and the script. Coding problems were also discussed and resolved. A copy of
the finalized questionnaires and the interview script can be found in the
Appendix.


Data Collection: Farm Household Interviews

Farm household interviews began in Settat Province just before the beginning of
the harvest season. This was designed to facilitate respondent recall for a
full cropping year. Harvesting is a pressing and time consuming activity.
Household members could not be expected to have sufficient time for interviews
during that period. A second passage was executed for the collection of
harvest data and was delayed until mid-summer when all crops had been
harvested. Douars in the drier interior of the province were surveyed first as
harvesting begins earlier in that area. Interviews in Ahmar were completed in
their entirety during one post-harvest passage, between the first and second
passages in Settat Province.

A preliminary schedule of douar visits was established and Rural Sociology
section researchers contacted local officials and inhabitants, informing them
of the day on which the team would visit the douar. Once in the douars on the
day of the survey, researchers verified respondents and supervised the survey
process. The data collection team was organized in five pairs of interviewers,
one man and one woman each. The man interviewed the farm operator (if a male)
and the woman interviewed the operator's wife. Pairs were rotated daily so
that each worked with a different partner each day of the week. This built
another check on reliability into the interview process itself.

Once teams had been assigned to farm households, researchers often participated
in the interviews. In addition, on-site verification of survey schedules was
performed by researchers. This involved a review of coding procedures and
interviewer notes, as well as clarification of apparently anamolous or
contradictory responses. Occasionally, interviewers returned to talk with
farmers and their wives for additional information.

The presence of women interviewers tended to transform the interview situation
into a family event. This provided greater access to the household. Often tea
was served and invitations to dinner extended. Interviews lasted about an hour
and a half. The more detailed questions on cropping practices prolonged the
farm operator's interview beyond that of the spouse's. An average of 10 survey
schedules were completed each day. The shorter post-harvest survey in Settat
Province lasted about a half hour and more were completed on a daily basis.


-25-












It was often not possible to contact all farm households on the first day in a
douar and return visits were necessary. Those farm operators with non-farm
activities were the most frequent source of scheduling difficulties.
Fortunately, during the survey period, a religious holiday allowed for contact
with many non-resident household heads. However, in a few cases when a farm
operator was a non-resident, data was collected from his responsible son or
brother.


Data Entry and Cleaning Procedures

The pre-coded survey schedules provided the basis for computer entry of the
collected data. This process began shortly after the interviews began and
lasted until a month after the post-harvested survey was completed. Each data
item was given a variable name and a file structure developed. A Lotus 1-2-3
spreadsheet format was used to facilitate data entry and cleaning procedures.
These files were later translated into SPSSPC+ (Statistical Package for the
Social Sciences, Personal Computer Version 3.0) files for consolidation, data
aggregation, documentation, and analysis.

Once all data had been entered onto spreadsheets, these were printed and
reviewed by researchers on a variable-by-variable basis for each case. When
out-of-range codes or inconsistencies between responses were detected, the
original survey schedule was checked and appropriate corrections made.
Routine, but minimal, typing errors were identified and corrected. However,
the primary source of errors was found to be inconsistent missing value
specifications. These were standardized. A few variables required a
standardization of coding as a consequence of modifications made in the field
due to the incorporation of new codes for unforeseen categories in the original
pre-coding. The resulting 30 corrected Lotus 1-2-3 files constitute the
original raw data base for this study.


Preparation for Data Analysis

The Lotus files were translated and consolidated into 10 SPSSPC+ system files
with variable and value labels for all 3,210 raw variables. At this point,
frequencies for all variables were calculated and examined. On the basis of
this analysis, indicators were constructed. These indicators include data
aggregations, such as: total hectares owned, shared, rented and operated; total
number of parcels; hectares by soil type; number of household members by age
and gender categories; total labor allocations by type and category; livestock
herd size and number owned and shared; etc. In addition, new indicators were
constructed to be consistent with and reflect actual frequency distributions
and for cross-tabular and magnitude analyses of continuous variables. These
new variables constitute 4 additional SPSSPC+ system files complete with
variable and value labels.

Data on cropping practices for the largest parcel of bread wheat, durum wheat,
barley, corn, faba beans, chick peas, lentils, peas, and horse beans were
combined with data from the post-harvest survey concerning those parcels into 9
additional SPSSPC+. system files. Each of these files contains complete
information, including basic aggregations, on cropping practices and their
resulting yields to facilitate crop specific analyses.


-26-

















CHAPTER 3 =


DATA ANALYST I S METHODOLOGY




Development of a farming systems typology requires that the methodology
reconstruct the nested nature of farming system components while taking into
account limitations imposed by the agro-ecological environment. The analytical
tools and techniques described below have been applied in such a way as to
achieve that goal. The analysis involves three steps: (1) the identification
and construction of key indicators reflecting sub-system components; (2) the
classification of farm households according to systematic variations in those
components; and (3) the statistical validation of the resulting typology.


Typology Construction: A Systems-Appropriate Approach

A typology is simply a tool to classify objects of inquiry according to one's
priorities and needs. It is dependent on one's preconceptions of the existing
conditions at a specified time and place. These preconceptions have been
specified at the outset of this dialectical inquiry. However, it is the
existing conditions which the typology must expose and clarify. Consequently,
the methodological approach is inductive. The data are explored statistically
to identify and match underlying systemic characteristics of regional farming
systems. The analysis builds from lower system levels to higher within each
agro-ecological zone to determine integral relationships between activities and
resources which distinguish different types of farming systems at the farm
household level.

It is useful at the outset to specify the assumptions which underlie these
typology development procedures: (1) farm households are intentional and
functionally-independent decision-making units; (2) each of these units
organizes its components into an integrated whole; (3) quantifiable cross-
sectional survey data provide sufficient indicators of systemic
characteristics; and (4) co-variation between indicators of system components
provide an adequate basis for identifying farm households with similar systemic
properties.

Although a detailed specification of procedures is elaborated below, they build
on the work of Dowling (1987) and Jamtgaard (1989) and can be briefly
summarized as follows. Exploratory data analyses led to the identification of
29 key indicators reflecting a range of significant variation in farm household
characteristics. These indicators were grouped into the three sub-system
categories (crop, livestock, and non-farm) and one household resource category.
Factor analyses were conducted within each of these categories for each agro-


-27-











ecological zone eliciting dimensions of variation between sub-system
characteristics. This established the key sub-system components (factors) for
each agro-ecological zone. These factors were then entered into an iterative
series of cluster analyses. The goal of these analyses was to determine the
optimal number of clusters of relatively homogeneous farm households and to
place each case within a cluster. Interpretive judgements were necessary. In
order to determine the extent to which the types so classified could be
empirically verified, and indeed, were sensically placed, discriminant analysis
using an enlarged set of 45 indicators (including the original 29) as
independent variables was conducted.

The resulting product of these procedures and assumptions is an "empirical,
polythetic typology" (Bailey, 1973), that is, a typology identified through the
empirical measurement of a set of characteristics which are used for
classification. It should be noted that no one characteristic, or sub-set of
characteristics, is necessarily unique to a particular type. Nor are farm
households within a particular type identical in all respects. Furthermore,
the typology of farming systems in the Aridoculture Region (Chapter 5) is
descriptive of a range of diversity in farming systems rather than explanatory,
dependent on the quantifiable structural characteristics identified in the
course of the study. This description provides a more refined basis for dialog
between researchers and farmers as they work together in the specification of
recommendation domains.


Indicator Identification and Construction

Analyses of variance were conducted individually for all variables reflecting
the criteria suggested in Chapter 1: farm household land resources and
allocations, demographic composition, labor allocations, productive and
consumptive durable goods, livestock resources and management, cropping
practices, levels of production, consumption and wealth, and goals. These, and
all subsequent analyses in this report, were conducted on population weighted
variables. Variables which did not demonstrate a sufficient level of variation
to warrant further analysis were dropped. Categorical variables in which some
categories were statistically insignificant were collapsed into more meaningful
and statistically-sound distributions. Many continuous variables had highly
skewed distributions. The range of others was quite considerable. In order to
reduce skewing effects and produce indicators capable of highlighting major
effects, all remaining variables were recorded into ordinal scale indicators.
For example, hectares operated and herd size variables were reduced to seven
and six order of magnitude categories, respectively.

Analysis of these indicators provided a first overview of the farming
population and suggested several key dimensions of variation in farming
systems. Cross-tabular and correlation analyses highlighted important
differences in livestock herds, hectares operated, family size, crop sales,
off-farm work, hired labor, labor intensities, seedbed preparation, fertilizer
use, and differing crop combinations. These analyses provided a basis for
further reducing the number of indicators. Many indicators were highly
correlated, appearing to measure the same phenomenon, and thus only one was
retained for further analysis.

Various combinations of two or three key indicators were used to test cross-
tabular typologies suggested in the literature. This procedure occasionally
isolated certain groups as distinctive (often the most wealthy), but left the


-28-











majority of farm households an undifferentiated mass with respect to other
important characteristics. Neither could patterns be found (other than land
and chemical input use) which coincided with agro-ecological zones. While such
typologies are useful for testing hypotheses directly related to those
criteria, they were deemed unsatisfactory for the identification of distinctive
and coherent farming systems since they provide little indication of systematic
interdependencies with other farming system components.

The methodology developed here addressed this latter concern directly. The
preceding analyses had identified 29 key indicators (see Table 3.1). These
indicators were selected because they demonstrated distinct variability and
because of their overall importance in adequately reflecting the range of
characteristics necessary to statitistically reconstruct the three sub-systems
(crop, livestock, and non-farm) and household resources. They refer to
conditions for the cropping year 1989-90 described in Chapter 4. Indicators
for cropping practices were based on data from each farm household's largest
parcel of each cereal grown.

From this point on, all analyses were conducted separately for each agro-
ecological zone. Although the analyses were conducted on the population
weighted data, the total number of actual cases involved in each zone was: 37
in Zone I, 175 in Zone II, 171 in Zone III, and 120 in Zone IV.


Determining Co-variation Between Components at the Sub-System
and Resource Levels

Factor analyses were conducted by zone for each sub-system and resource
category to establish underlying dimensions of variation between indicators.
Standard SPSSPC+ Version 3.0 defaults were used in these analyses: minimum
eigen = 1.0; principal components analysis for extraction; and varimax
rotation. The strongest and most readily interpretable factors were selected
from the solutions examined for each category. Factor scores were calculated
using the default regression method yielding standardized scores with a mean of
zero and a standard deviation of one.

Tables 3.2 through Table 3.5 present the factor score coefficients for the
major dimensions of variation at the sub-system and resource levels resulting
from the factor analyses. Although each zone has essentially the same set of
broad dimensions of variation, what is important to note is how the indicators
combine differentially within each zone. Indeed, the importance of certain
characteristics is negligable in some zones.


Assigning Farm Households to Homogeneous Farming Systems

In order to assign farm households to particular types of farming systems,
cluster analyses were employed. The factors representing dimensions of
variation in sub-system and resource category indicators provided the input for
these analyses. Cluster analysis groups cases (farm households) according to
similarities in their patterns of variation across these input variables.

The SPSSPC+ Version 3.0 Quick Cluster procedure was used for these analyses.
Quick Cluster uses the squared Euclidian distance measure as its algorithm. It
requires that the number of clusters be determined at the outset of an
analysis. Because the data matrix is not stored in memory (saving RAM),


-29-












Table 3.1: Ordinal Scale Means and Standard Deviations for the 29 Key
Indicators by Sub-System and Resource Categories

Mean Standard
Value Deviation
FARM HOUSEHOLD RESOURCES

1. Number of household members 2.84 1.16
2. Age of the household head 2.52 1.12
3. Hectares of land fully-owned 1.44 1.37
4. Land owned as a percent of hectares operated 1.70 1.56


CROPPING SUB-SYSTEM

5. Person-days labor per hectare operated 3.50 1.44
6. Person-days household labor for crop production 3.04 1.42
7. Person-days hired labor for crop production 1.96 1.22
8. Tractor ownership .07 .26
9. Number of hectares operated 3.19 1.64
10. Percentage of crop production sold 2.45 1.32
11. Cultivation of all crops designated fundamental
to the agro-ecological zone (dummy variable) .77 .42
12. Number of different crops grown 2.49 .93
13. Number of seedbed preparation passages 2.30 1.11
14. Indicator of the use of purchased fertilizer 1.94 1.20
15. Indicator of the use of certified seed varieties 2.44 .90
16. Indicator of the use of motor-driven traction
for seed covering 2.29 .86
17. Indicator of the use of chemical pesticides 1.39 .68


LIVESTOCK SUB-SYSTEM

18. Person-days household labor for livestock production 2.06 .68
19. Person-days hired labor for livestock production 1.17 .45
20. Size of sheep flock 2.86 1.49
21. Number of sheep sold 1.02 1.01
22. Size of cattle herd 2.67 1.11
23. Number of cattle sold .40 .75
24. Indicator of the use of purchased livestock feed 1.60 1.06


NON-FARM SUB-SYSTEM

25. Off-farm employment status of the household head .73 1.21
26. Person days household labor for off-farm employment 1.89 1.15
27. Level of non-agricultural investments .26 .57
28. Financial assistance from non-resident family members .47 .64
29. Standard of living indicator (consumer durables) 2.57 1.44


-30-













Table 3.2: Factor Score Coefficients of Farm Household Resource Indicators for
Each Dimension of Variation by Agro-Ecological Zone

Indicators*
Eigen % variance
1 2 3 4 Value explained
ZONE I


Land Tenure Status


ZONE II

Land Tenure Status

Family Structure


-.02


.97 -.16


.33 -.04


ZONE III


Land Tenure Status

Family Structure


.86 -.60


.13 -.05


ZONE IV


Land Tenure Status

Family Structure


.83 -.66


-.05 -.05


* Indicators are numbered according to their
coefficients or factor loadings signify the
indicator in the constitution of each dimension


listing in Table 3.1. The
relative importance of each
of variation (Land Tenure


Status or Family Structure, read horizontally). A negative value of a
coefficient denotes an inverse relationship with indicators having positive
values.


-31-


2.17


1.91

1.07


2.02

1.12


1.97

1.11















Table 3.3: Factor Score Coefficients of Cropping Sub-System Indicators for Each Dimension of Variation
by Agro-EcoLogicaL Zone

Indicators*
Eigen % variance
5 6 7 8 9 10 11 12 13 14 15 16 17 Value explained
ZONE I

Energy Intensive Inputs .16 .12 .12 .03 .13 -.08 .73 .39 .91 .71 .84 .89 .20 4.78 37

Scale of Operations -.21 .21 .79 .81 .94 .61 .18 .67 .29 .05 -.01 .06 .02 2.54 20

Intensity of Labor .84 .84 .12 -.12 -.09 .24 .33 .09 .03 .11 .21 -.06 -.15 1.54 12

Cash Crop Orientation -.04 -.00 .11 -.12 -.01 .55 -.13 .24 .02 .48 .24 .06 .87 1.16 9



ZONE II

Scale of Operations .03 .65 .71 .37 .86 .56 -- .81 .06 .40 .47 -.08 .21 4.10 34

Energy Intensive Inputs .04 .01 .24 .24 .16 .16 -- .07 .76 .74 .46 .51 .71 1.96 16

Intensity of Labor -.86 -.54 .21 .61 .33 -.03 -- -.19 .13 -.11 .15 .43 -.02 1.24 10



ZONE III

Scale of Operations -.36 .19 .81 .66 .86 -.04 .34 .49 .21 .33 .64 .04 -- 3.83 32

Intensity of Labor .76 .84 .05 -.19 -.09 -.15 .40 .20 -.63 -.26 .01 -.76 -- 2.48 21

Cash Crop Orientation .04 .12 .07 .08 .16 .77 .54 .60 .49 .53 .31 .19 -- 1.17 10



ZONE IV

Scale of Operations -.54 .29 .40 -- .84 -- .72 .76 -- -- -- .11 -- 2.47 35

Intensity of Labor .66 .83 -.29 -.10 -- -.00 .05 -- .59 -- 1.44 21

* Indicators are numbered according to their listing in Table 3.1. The coefficients or factor Loadings
signify the relative importance of each indicator in the constitution of each dimension of variation (Energy
Intensive Inputs, Scale of Operations, Intensity of Labor or Cash Crop Orientation, read horizontally). A
negative value of a coefficient denotes an inverse relationship with indicators having positive values.


-32-













Table 3.4: Factor Score Coefficients of Livestock Sub-System Indicators for
Each Dimension of Variation by Agro-Ecological Zone

Indicators*
Eigen % variance
18 19 20 21 22 23 24 Value explained
ZONE I


External Input Dependent

Scale of Operations


-.78 .88 .58

.08 .11 .73


.06 .70 -.09 .82

.92 .28 .87 -.03


ZONE II


Scale of Operations

Salaried Labor Dependent


.27 .47 .84

.90 -.78 -.06


.78 .69 --- .68


.03 .04


--- -.08 1.40


ZONE III


Scale of Operations


Salaried Labor Dependent


.27 .40 .80

-.88 .78 .05


.83 .78 .60 .79

.04 .02 .28 -.14


ZONE IV


Scale of Operations

Salaried Labor Dependent


.71 .09 .81


-.50 .95 -.05 -.10


.72 2.78

- .13 1.16


* Indicators are numbered according to their listing in Table 3.1
coefficients or factor loadings signify the relative importance of
indicator in the constitution of each dimension of variation (External
Dependence, Scale of Operations or Salaried Labor Dependent,
horizontally). A negative value of a coefficient denotes an i
relationship with indicators having positive values.


-33-


3.25

1.88


2.58


3.20

1.46


. The
each
Input
read
inverse













Table 3.5: Factor Score Coefficients of Non-Farm Sub-System Indicators for
Each Dimension of Variation by Agro-Ecological Zone

Indicators*
Eigen % variance
25 26 27 28 29 Value explained
ZONE I


Non-Agricultural Wealth

Off-Farm Employment


-.09


.88 -.58


.74 .86 -.12


ZONE II


Off-Farm Employment

Non-Agricultural Wealth


.83

.07


.20 .10 -.17

.78 .70 .56


ZONE III


Non-Agricultural Wealth

Off-Farm Employment

Migrant Aide


-.08


.86 .86

.11 -.05


.37 -.24 -.06


ZONE IV


Off-Farm Employment

Non-Agricultural Wealth


.19 -.40


.37 -.04


* Indicators are numbered according to their listing in Table 3.1. The
coefficients or factor loadings signify the relative importance of each
indicator in the constitution of each dimension of variation (Non-
Agricultural Wealth, Off-Farm Employment or Migrant Aide, read
horizontally). A negative value of a coefficient denotes an inverse
relationship with indicators having positive values.


-34-


1.91

1.62


1.69

1.29


1.59

1.32

1.08


1.94

1.21












repeated entries of calculated cluster centers are required for achieving the
optimum cluster solution. Although more labor intensive, this method allows
for greater control over the clustering process.

The goal of the cluster analyses was to determine both the optimum number of
clusters, and ultimately, cluster membership. In order to determine the number
of types in each zone, analyses were examined for separate solutions of from 3
to 9 clusters. Once having established these cluster solutions, cluster
memberships of each case for each group-number solution were listed and
compared. Fifty to 60 percent of the cases were consistently aligned in the
same cluster across solutions. This procedure lead to the determination of the
number of distinct and relatively homogeneous types. Cluster centers were
calculated for these homogeneous groups and reentered into the analysis to
determine optimum group membership for all cases.

Interpretive judgements were necessary. In the first instance, they were based
on the repeated coincidence of group placement under differing solutions, and
in the second, on the minimization of within-group variances for key
distinguishing factors. Cases involving these latter judgements were
ultimately classified according to their factor scores for those factor
components most strongly associated with one cluster or another. That is,
matching an outlier's factor score with a factor score mean for a cluster which
significantly deviated from the overall mean on that factor.

The cluster analysis was then re-run with the revised mean scores until an
optimum solution was obtained. This often meant stopping the iterative process
before the statistically-optimum solution was achieved. The statistical
heterogeneity of clusters was increased, but all cases were thereby included in
an interpretively optimal way. While the core of distinctive farming systems'
interdependencies was maintained, this procedure allowed for the integration of
the heterogeneity of actual circumstances. Once two or three key
distinguishing factors were identified for a cluster, determination of cluster
membership was facilitated as mean values for other factors were often well
within the selected cluster's standard deviation.


Confirming the Statistical Adequacy of Group Placement

The underlying rationale for the proceeding procedures was that by capturing
the key dimensions of variation at the sub-system level through factor analysis
and then utilizing those factors in the cluster analyses, cases would be
grouped according to characteristic interdependencies reflecting the nesting of
resources and sub-systems within the whole farm system. In this way, sources
of variation in one sub-system would be interconnected with sources of
variation in another, thereby emphasizing the interdependent nature of sub-
system components and reducing overall variability within groups.

In order to determine the extent to which the types so classified could be
empirically verified, and indeed, were sensically placed, discriminant analysis
using a pool of 45 indicators (including the original 29 key indicators) as
independent variables and the zone-level classifications as dependent variables
was conducted. SPSSPC+ Version 3.0 Discriminant Analysis was used with step-
wise entry based on those indicators which minimized the overall Wilks' lambda.
The analyses were limited to only the first three, ten, or eight variables
qualifying to enter (depending on the sample size of the zone).


-35-












Correct classification (ranging from 86-91%) and all cannonical correlations
significant at the .001 probability level were obtained for each of the agro-
ecological zones. Those cases either mis-classified in the discriminant
analysis or not entered in previous analyses due to missing values were
investigated for consistency with their own and other groups' means. Cases
determined to be inconsistent with a group classification were re-placed in a
group which better conformed to their pattern of distinctive characteristics.

Nineteen types of farming systems across the four zones have been so identified
and farm households classified, providing the basis for the farming system
descriptions in Chapter 5: 3 in Zone I; 6 each in Zones II and III; and 4 in
Zone IV.

At this point, final discriminant analyses were conducted. Tables 3.6 through
3.9 present the results of these analyses. All cannonical correlations were
strong and significant at the .001 probability level. Discriminant
classification improved only slightly as many classification adjustments were
ultimately based on interpretive judgements rather than statistical criteria.
Nevertheless, the integrity of the underlying rationale was maintained as it
formed the basis for case-by-case interpretations. Correct classification for
Zone I was 91 percent with three indicators; for Zone II, 91 percent with 10
indicators; for Zone III, 92 percent with 10 indicators; and for Zone IV, 91
percent with eight indicators.

Although the full description and method for identifying these farming system
types will be presented in the following chapters, it is useful to highlight
the key findings of these analyses for later reference. Off-farm employment of
household members, the number of hectares operated and the number of household
members are the most important indicators for discriminating between farming
system types across all zones. Hectares of land fully-owned, the use of hired
labor for livestock production, the off-farm employment of the household head,
non-agricultural investments, and tractor ownership also play important roles.


-36-













Table 3.6:


Rotated Standardized Canonical Discriminant Function Coefficients
by Order of Step-Wise Entry for Zone I Farming System Types


Function 1 Function 2

Non-Agricultural Investments .95* -.22
Non-Resident Financial Assistance .13 1.00*
Number of Household Members .63* .76*

Percent of Variance Explained 52 48

Eigen Value 3.0 2.8

*Coefficients with a strong impact on the discriminant function.


Table 3.7:


Rotated Standardized Canonical Discriminant Function Coefficients
by Order of Step-Wise Entry for Zone II Farming System Types


Functions


Hired labor for Livestock Production
Number of Hectares Operated
Household Labor Employed Off-Farm
Household Labor for Livestock Production
Hectares of Fully-Owned Land
Seedbed Preparation Passages
Number of Household Members
Motor-Driven Traction for Seed Covering
Tractor Ownership
Use of Certified Seed Varieties


Percent of Variance Explained


Eigen Value


28 23 19 19 12


2.2


-37-


-.90*
.24
-.28
.10
.19
.23
.08
-.06
-.A6
.11


.03
.52*
.07
-.38
.65*
-.09
.08
-.09
.36
-.00


.38
.35
-.06
.73*
-.15
-.08
.61*
-.26
-.00
.07


-.17
.01
.93*
-.02
.12
-.35
.02
.28
.09
-.48


.29
-.32
.13
.41
.45
.53*
-.15
.53*
-.14
.20


SCoefficients with a strong impact on the discriminant function.













Table 3.8:


Rotated Standardized Canonical Discriminant Function Coefficients
by Order of Step-Wise Entry for Zone III Farming System Types


Functions


Off-Farm Employment of Household Head
Ownership of a Car or Truck
Household Labor Employed Off-Farm
Hectares of Fully-Owned Land
Number of Household Members
Number of Hectares Operated
Age of Household Head
Hired labor for Livestock Production
Non-Agricultural Investments
Tractor Ownership


Percent of Variance Explained


32 22 19 17 11


Eigen Value


* Coefficients with a strong impact on the discriminant function.







Table 3.9: Rotated Standardized Canonical Discriminant Function Coefficients
by Order of Step-wise Entry for Zone IV Farming System Types


Function 1 Function 2 Function 3

Household Labor Employed Off-Farm .95* -.06 .01
Number of Hectares Operated .08 .52* .17
Cultivation of Base Crops .19 .64* -.32
Household Labor for Crop Production -.21 .34 -.66*
Standard of Living -.17 .21 .64*
Off-Farm Employment of Household Head .42 .02 -.05
Size of Sheep Flock -.14 .39 .44
Tractor Ownership .25 -.41 -.34

Percent of Variance Explained 59 28 13

Eigen Value 3.8 1.8 .8

* Coefficients with a strong impact on the discriminant function.


-38-


.96*
-.28
.10
-.03
.03
.23
-.10
.04
.16
-.06


.10
.77*
.03
-.01
-.29
.08
.19
.36
.23
.37


-.23
.08
.30
.38
.71*
.37
-.57*'
-.19
.25
-.10


.67*
.13
-1.15*
-.04
.29
.23
-.15
-.20
-.08
-.10


.00
.03
.10
.86'
-.38
.36
.26
.13
-.29
-.09















PART II
















CHAPTER 4

CHARACTER IZATI ON OF
AGRO-ECOLOGI CAL ZONES




The description and analysis of farming systems contained in this report have
been structured in the context of the agro-ecological features of the arid and
semi-arid regions of Morocco in which they exist. This chapter delineates
those features into four distinct zones and describes the climatic conditions
influencing farm household practices during the cropping year (1989-90) for
which the survey data were collected. Aggregate farm household characteristics
for each zone are then characterized.


Establishing Agro-Ecological Zones

The objective in defining agro-ecological zones is to obtain a measure of
homogeneity in the bio-physical and climatic conditions which limit the range
of potential production options faced by farm households. Although the broad
bio-physical and climatic parameters which shape production constraints in the
region are generally well-known, they have not as yet been mapped at the local
level. The SEEES of the Settat and Safi DPAs provided the first cut at this
zonage by structuring the sample framework according to broad agro-ecological
features at the level of rural communes. This offered the opportunity to
sample across a wide range of bio-physical conditions. During the survey,
however, it was realized that the heterogeneity of the terrain did not coincide
with these administrative boundaries.

In order to better understand the specificity of the conditions surveyed, a
more precise designation of agro-ecological zones was needed. Since no
information was available at the douar-level on precipitation patterns and
individual farm households have sufficiently different resource combinations to
distort identification of climatic and pedological similarities related to
general location, this was accomplished by characterizing conditions at the
douar level and re-grouping the surveyed douars according to their collective
agro-ecological features.

To this end, farm household data on soil types, crops and livestock were
aggregated at the douar level (44 douars, 33 in Settat Province and 11 in
Ahmar). Soil type is a clear indicator of agro-ecological conditions. Crops
grown and livestock raised, on the other hand, contain an element of managerial
choice. Nevertheless, at the douar level, the predominant combinations of
crops and livestock were considered to be reflective of underlying ecological
limits, particularly rainfall. In order to compensate for the effects of douar
size, percentages of each soil type (defined in local terms, see the Glossary


-41-











at the end of this chapter) and crop grown were calculated. Livestock were
measured in terms of the number of head per hectare operated.

Considerable heterogeneity was evident. The predominant soil types (tirs,
hamri, biad, hrash, and rmel) are interspersed across the region and their
combinations at any particular location are highly variable. Indices of soil
quality, based on local names of soil types, suggested by Aridoculture Center
soil scientists demonstrated that the only distinction clearly justified was
between the higher quality tirs and the others. Thus, having at least 50
percent tirs soils was retained as a key determinant of agro-ecological zone.

Although crop combinations are highly complex, some patterns did emerge. Small
cereal grains are pervasive throughout the region, however, there appears to be
a trade-off between the type of small cereal grain grown. The percentage of
durum wheat was negatively related to that of barley and bread wheat. Barley,
a more drought tolerant crop, was negatively related to corn and chick peas.
The presence of corn and food legumes is generally considered suggestive of
higher rainfall conditions and/or soil moisture retention capacities. This
distinction became the second key determinant of agro-ecological zone
operationalized in terms of a minimum of 10 percent of hectares in corn.

Livestock were also found to be pervasive across the region. However, the type
or number of head per hectare provided no clear-cut distinction between groups
of douars. These indicators were of little use in defining agro-ecological
zones.

The population of douars was first divided according to whether or not 50
percent of their soils were tirs. Those douars with a minimum of 10 percent of
total hectares in corn and additional hectares in food legumes were added to
the predominantly tirs group of douars. This became what has been labled the
More Favorable Zone (Zone II). Two douars in the remaining group had
relatively large areas in vegetable and forage crops, relatively high numbers
of cattle, and predominantly hrash and rmel soils with poor water retention
capacities. These two douars are located near the coast in Settat Province.
They were clearly distinctive and have been labled the Coastal Zone (Zone I).
The remaining douars with few, if any, pockets of tirs were largely located in
the south of Settat Province and in the region of Ahmar. Given the clear
regional rainfall differential for the region of Ahmar (see next section), this
group was divided into two zones: the Less Favorable Zone (Zone III) of Settat
Province; and the Ahmar Zone (Zone IV).

Map 1 provides a schematic overview of the general location of these four zones
within the Aridoculture Region. This zonage conforms to the general features
of that recently developed by the Settat DPA (Laklalech et.al, 1991). The
actual location of each douar is presented in Maps 2 and 3 along with the
overview of farm household characteristics within each zone. It should be
noted that while Map 1 assigns geographic boundaries to outline these zones,
Maps 2 and 3 do not include such boundaries. The heterogeneity of the terrain
is such that, there is an interspersion of agro-ecological conditions,
particularly along the frontiers of these zones.


Climatic Conditions for the Cropping Year 1989-90

Rainfall in the region is highly erratic, characterized by wide swings in total
annual precipitation, as well as throughout the growing season (Watts and El


-42-









Map 1:


Location of Agro-Ecological Zones Covered


in the Farming Systems Study


F777/


Zone I


Zone II


Zone III


Zone IV


-43-












Mourid, 1988). Average annual precipitation rates, therefore, are only rough
approximations of rainfall patterns. Nevertheless, they provide a means to
gauge the relative character of the 1989-90 growing season. Monthly rainfall
provides a better measure of water availability throughout the growing season.

Table 4.1 presents average annual precipitation rates and the precipitation
rates for 1989-90 for various locations in the survey region. Average annual
precipitation for the region of Ahmar is not available, so the adjacent and
similar rainfall region of Ben Guerir has been substituted. There is a clear
gradient from Zone I through Zone III of Settat Province continuing on to the
region of Ahmar (Zone IV). A comparison of these averages with the 1989-90
precipitation rates shows that the survey year was about average across these
zones.

The distribution of precipitation during the year was regular for most of the
region with relatively stable and low temperatures (El Mourid and Boutfirass,
1990). Cumulative rainfall distributions for the year are graphed against
cumulative rainfall probabilities for Settat Province in Figure 1. Crop
establishment conditions in Settat during the months of October and November
1989 were good, totaling about 30-40 percent of total annual precipitation.
With the exception of El Borouj where annual rainfall averages tend to be
lowest, conditions continued above average until the end of January when a
month long drought began. When the rains returned for grain filling and corn
production, Berrechid retained its above average year, while the rest of the
province settled into an average year.

Conditions in Ahmar were not as favorable (Figure 2). Rainfall levels were
below average in November and December but sufficient for crop establishment.
However, a two-month drought set in drying out much of the crop. Significant
rains followed during early March, aiding those who planted corn, but doing
little for those whose small grain crops were too weak to head.



Table 4.1: 1989-90 and Average Annual Precipitation Rates for Various
Locations in the Survey Region (millimeters)

Settat Province Ahmar

Berrechid Ouled Said Ben Ahmed El Borouj Chemaia


Average 1970-84 388 373 378 2991 n.a.


Long Term Averages ------------- 386, S.D.= 127 ------------- 227, S.D.= 79
67 years2 46 years3

1989-90 439 355 367 320 216

Sources: Centres de Travaux de Berrechid, Ouled Said, Ben Ahmed, El Borouj and
Chemaia; Watts and El Mourid, 1988; El Mourid and Boutfirass, 1990.
1. Average only for 1970-1980.
2. Data for Settat.
3. Data for Ben Guerir.


-44-













Figure 4: Cumulative Rainfall Probabilities
for Settat Province: 1989/90
Cumulative rainfall (mm)
500
500 Ben Ahmed
d7
Berrechid
400 O.SaTd d5
El Borouj x
X-X
300--
d3


200--



100-


0
0- ^y "!-:I-- -----!--- -
Sep Oct Nov Dec Jan Feb Mar Apr Ma
di= number of years in 10 cumulative rainfall is less




Figure 5: Cumulative Rainfall Probabilities
for Ahmar Region: 1989/90
Cumulative rainfall (mm)
300 Ben Guerir

X- Chemafa
250- d!


200 d


150- d


100-

50
50- -'


Sep Oct Nov Dec Jan Feb Mar Apr May
di= number of years in 10 cumulative rainfall is less


-45-


7


y











Map 2:


Location of Survey Douars for the Three

Agro-Ecological Zones in Settat Province


Casablanca


*
Berrechid


Settat

Fouled Said
A Ouled Said


A
A


El Borouj
0


* Zone I Douars
A Zone II Douars
r Zone III Douars


Ben Ahmed
E *











Description of the Agro-Ecological Zones


The following sections describe the characteristics of each agro-ecological
zone. As elsewhere in this report, all statistics have been weighted to
represent population parameters. Tables 4.2 through 4.13 provide information
on the distribution of soils, crops and livestock and are presented with the
text. Tables 4.14 through 4.38 (found in Appendix 4.1 of this Chapter) compare
aggregate farm household characteristics across zones, providing an expanded
basis for the zone level descriptions of semi-arid and arid farming in Morocco.
Yield data are based on each farm household's largest parcel for each specified
crop. The characterization of cropping practices is limited to the predominant
cereal crops.


Zone I: The Coastal Zone

The Coastal Zone (Zone I) is situated on a narrow strip along the coast of
Settat Province. The sample for this zone contains 37 farm households located
in 2 douars. Two types of soils predominate, shallow stony soils (hrash) and
sandy soils (rmel). Occasional shallow pockets of black clay (tirs) and well-
textured red (hamri) soils are also found. For the most part, soils in this
zone have poor water retention capacities. However, the humid coastal climate
and higher rainfall regime, in part, compensates for low moisture retention.

Corn (often irrigated) and bread wheat are the most frequently grown crops.
However, the zone is distinctive for increasing vegetable production, often
conducted in greenhouses with more than one crop per year, and forage crops.
Average wheat yields for the largest parcels of each cultivated are relatively
high. Barley yields, however, are extremely poor. Fifty-three percent of farm
households keep 15 percent of the land in fallow. Corn grain yields and all
straw production, with the exception of barley, exceed those of other zones.
Cattle production is pervasive and becoming more intensive on larger
operations. Milk production for sale is common. Forty-three percent have
sheep. Donkeys are kept by 61 percent and draft animals (mules, horses and
camels) by 63 percent of these households.

While farm production techniques tend to be relatively advanced, there is a
high level of differentiation between a few highly capitalized farm households
and the majority who have managed to retain control of some small parcels of
land. Although average farm size is 8.4 hectare, three-quarters are less than
8 hectares and 43 percent less than 3.5 hectares. A quarter of the land is
rented or shared, principally involving urban dwellers holding land in the
douar.

Two-thirds of households heads are over 55 years of age, 39 percent over 65.
This skewed age distribution suggests that the zone may soon experience a rapid
transition in structure as farms are transferred between generations. Some
households (19%) are headed by younger women. Overall, there are more
households of 1 to 3 members than in other zones.

An important characteristic of this zone is its proximity to Casablanca. Over
60 percent of farm households engage in off-farm employment, over a quarter
working for more than 200 days per year. Thirty-six percent of household heads
work off the farm during the year. Nearly a third receive financial assistance
from non-residents, and another 39 percent possess non-agricultural investments
in addition to their farm assets.


-47-












Distribution of Soil Types in Zone I


Farm Households by Soil Type
Percent of Hectares
Percent Average Hectares in the Zone

Tirs 22.8 3.92 10.7
Hrash 68.5 4.68 38.3
Biad 8.0 2.64 2.5
Hamri 25.2 4.76 14.3
Rmel 54.3 5.26 34.1
Other 0.0 na na



Table 4.3: Distribution of Crops Grown in Zone I

Farm Households by Crop Grown
Average Percent of Hectares
Percent Hectares Change* in the Zone

Bread Wheat 73.7 2.95 ++ 26.0
Durum Wheat 25.2 2.34 -7.1
Barley 15.6 1.50 -- 2.8
Corn 81.7 2.33 ++/- 22.8
Faba Beans 3.5 .58 -- .2
Chick Peas .0 na na na
Peas .0 na na na
Lentils .0 na na na
Horse Beans .0 na na na
Fallow 56.1 1.88 ++/- 12.6
Forage 53.3 2.36 + 15.1
Vegetables 40.1 2.41 ++/- 11.5
Other 8.6 1.90 ++/- 2.0



Table 4.4: Composition of Livestock in Zone I

Farm Households by Livestock
Average Head per Hectare
Percent Number Change* in the Zone

Sheep 42.9 30.3 -- 2.63
Cattle 100.0 7.8 ++/- 1.40
Goats 1.7 1.0 +/- .03
Donkeys 60.6 1.1 -.39
Horses 28.7 1.3 + .20
Mules 37.7 1.0 -.17
Camels 1.7 1.0 +/- .03


* Change in production since early 1980s: +/-- selective decrease;
-- general decrease; slight decrease; +/- stability; + slight increase;
++ general increase; ++/- selective increase (concentration).


-48-


Table 4.2:












These farm households demonstrate the highest levels of mechanization. A high
of 14 percent own a tractor. Eighty percent use tractor-powered implements for
seedbed preparation and seed covering on all of their largest cereal parcels.
For the vast majority, these operations are conducted by custom-hire services
and involve the use of a cover-crop. Nevertheless, hand-broadcasting is the
pervasive seeding technique. Custom-hired combine harvesting is also common,
nearly three-quarters of all farm households use a combine for harvesting.

The use of purchased inputs is also very common. Nearly two-thirds of these
farm households apply commercial fertilizer and 40 percent use certified
varieties on their largest small grain parcels. However, only 40 percent apply
herbicides to any of their largest parcels. Eight-four percent purchase animal
feed.

Half of all farm households allocate less than the average 90 days of household
labor to crop production. Hired labor allocated to crop production averages 89
days, although half of all households hire less than 30 days. Labor intensity
per hectare is high with over a quarter allocating 45 days or more. This high
rate is associated with intensive vegetable production using hired labor. In
addition, the majority of farm households allocate over a full person-year of
household labor to livestock production. Although the vast majority hire
little or no labor for livestock tending, over 20 percent have at least one
full-time hired shepherd.

More Zone I households (17%) sell over 50 percent of their harvested grain and
produce than those in other zones. In contrast, nearly half sell little or
none. The median household directs about a third to human consumption and
nearly half stock over a third. A majority direct less than 15 percent to
animal consumption.


Zone II: The More Favorable Zone

The More Favorable Zone (Zone II) is situated on the coastal plain and adjacent
hills of Settat Province. The sample for this zone contains 175 farm
households located in 16 douars. The zone covers a large portion of the
Chaouia region (so named for the herding of livestock), lower Chaouia and
pockets of tirs soils in the adjacent hills of Upper Chaouia. Ninety percent
of farm households possess parcels of deep black clay soils (tirs) which cover
half of the zone. These soils are intermixed with a variety of shallower soft
chalky (biad) and silty red (hamri) soils, both having some water retention
capacity, and some shallow stony soils (hrash). The climate is dry for most of
the year with the majority of the annual rainfall (ranging from 250 to 500mm)
coming during November/December and February/March.

Barley, durum wheat, faba beans and corn are the most frequently grown crops
accounting for 60 percent of the hectares operated. Given the favorable soil
moisture retention capacities of the zone, a wide range of additional crops are
grown, significant among them are bread wheat, lentils, peas and chick peas.
Eighty-one percent of farm households keep 15 percent of the land in fallow.
Average cereal grain and straw yields are among the highest in the region.
Cattle and sheep are raised by the vast majority. Donkeys, for transportation,
are also pervasive with an average of 2 per household. Although diminishing in
importance, half own draft animals (mules, horses and camels) used for food
legume production and transport.


-49-













Distribution of Soil Types in Zone II


Farm Households by Soil Type
Percent of Hectares
Percent Average Hectares in the Zone

Tirs 89.5 7.99 52.0
Hrash 34.8 5.26 13.3
Biad 36.7 6.62 17.7
Hamri 42.4 5.23 16.1
Rmel 1.6 5.53 .6
Other .9 5.26 .4



Table 4.6: Distribution of Crops Grown in Zone II

Farm Households by Crop Grown
Average Percent of Hectares
Percent Hectares Change* in the Zone

Bread Wheat 38.2 2.95 -- 8.2
Durum Wheat 86.0 3.65 ++ 22.9
Barley 89.2 3.07 ++/- 19.9
Corn 55.6 2.62 ++/- 10.6
Faba Beans 57.7 1.55 ++ 6.5
Chick Peas 30.8 1.92 ++/- 4.3
Peas 34.7 1.18 -- 3.0
Lentils 38.2 .85 -- 2.4
Horse Beans 10.7 1.93 + 1.5
Fallow 80.5 2.58 +/- 15.1
Forage 12.8 3.87 +/- 3.6
Vegetables 16.4 1.38 + 1.6
Other 12.0 .60 + .5



Table 4.7: Composition of Livestock in Zone II

Farm Households by Livestock
Average Head per Hectare
Percent Number Change* in the Zone

Sheep 84.6 31.9 ++ 2.49
Cattle 93.6 5.9 ++ .56
Goats 15.4 4.1 -- .25
Donkeys 91.4 2.0 ++/- .33
Horses 27.4 1.3 -- .10
Mules 39.6 1.3 -- .10
Camels 6.9 1.8 -- .11


* Change in production since early 1980s: +/-- selective decrease;
-- general decrease; slight decrease; +/- stability; + slight increase;
++ general increase; ++/- selective increase (concentration).


-50-


Table 4.5:












The favorable conditions in this zone make it an important cereal growing
region. Recently, many large operations (over 100 hectares) have been
established and pivot irrigation systems installed on level areas of the plain
and plateau. These are frequently operated through long-term rental agreements
with entire douars. These operations do not constitute farm households and so
were not included in the survey, although one douar was so transformed during
the year following the survey. This transformation in the structure of
agriculture has important implications for the future of farm households in
this zone. As it now stands, the average farm household operates 13.8
hectares, with half operating between 3.5 and 13 hectares. About a third of
the land is shared or rented, principally involving urban dwellers still
holding land in the douar.

The average farm household has 7.9 members, nearly two-thirds with 4 to 9
members. Over half of all household heads are between 45 and 64 years of age.
These distributions suggest a full range of family life cycle stages are
represented.

Nearly half of all farm households engage in off-farm employment. Two types of
off-farm employment can be discerned, occasional and full-time. The latter
accounts for nearly a quarter of all operations. Thirty percent of household
heads work off the farm. In addition, over a third of all farm households
receive financial assistance from non-resident family members. Nineteen
percent possess non-agricultural investments as well as their farm assets.

Farm mechanization is relatively advanced in the zone. Nine percent of farm
households possess a tractor and a cover-crop (off-set disk), although none own
a seed drill. Over three-quarters conduct seedbed preparation of their largest
small grain parcels with tractor-powered implements, primarily through custom
services. However, only just over half use tractor-power for seed covering
after hand-broadcasting. Custom-hired combine harvesting is also common, over
60 percent doing so on all of their largest parcels.

The use of purchased inputs for small grain production is sporadic. Although a
third apply commercial fertilizer to all of their largest parcels, nearly half
do so only on some of these parcels. Nearly three-quarters use certified seed
on some of their parcels. Certified barley seed is rarely encountered and many
apply manure rather than commercial fertilizer to their barley parcels.
Herbicides are consistently used by under a quarter of farm households. Nearly
half use none at all. Eighty-six percent purchase animal feed.

Half of all farm households allocate under 150 days of household labor per year
to crop production. The median for hired labor is just over 30 days. Labor
intensity is relatively high, with 60 percent of farm households allocating
from 11 to 30 person-days of labor per hectare. Food legume production
accounts for the majority of this labor intensity. Eighty-four percent of farm
households allocate over a full person-year of household labor to livestock
production. Only 14 percent hire at least one full-time shepherd.

Harvested grain and produce are predominantly directed to sales and animal
consumption in this zone. Sixty percent of these farm households sell between
15 and 50 percent, only 10 percent sell over 50 percent. Sixty-four percent
feed from 15 to 50 percent to their livestock. Three-quarters direct less than
30 percent, respectively, to human consumption and to stocks.


-51-












Distribution of Soil Types in Zone III


Farm Households by Soil Type
Percent of Hectares
Percent Average Hectares in the Zone

Tirs 25.4 8.18 14.0
Hrash 47.5 5.63 18.1
Biad 53.5 6.13 22.1
Hamri 52.0 8.39 29.4
Rmel 25.2 8.00 13.6
Other 7.3 5.59 2.8



Table 4.9: Distribution of Crops Grown in Zone III

Farm Households by Crop Grown
Average Percent of Hectares
Percent Hectares Change* in the Zone

Bread Wheat 58.9 5.27 ++/- 20.8
Durum Wheat 85.2 4.69 + 26.8
Barley 89.9 3.98 + 24.0
Corn 1.8 1.89 .2
Faba Beans 18.3 .68 .8
Chick Peas 3.5 .84 .2
Peas 14.8 1.40 1.4
Lentils 44.5 1.27 ++/- 3.8
Horse Beans 1.7 .50 + .1
Fallow 69.3 4.36 ++/- 20.3
Forage 11.0 2.03 -1.5
Vegetables 3.0 .52 ++/- .1
Other 5.1 .34 ++/- .1



Table 4.10: Composition of Livestock in Zone III

Farm Households by Livestock
Average Head per Hectare
Percent Number Change* in the Zone

Sheep 77.0 63.9 ++/- 3.46
Cattle 75.4 5.2 ++/- .46
Goats 28.1 4.4 -- .56
Donkeys 86.7 1.7 +/- .24
Horses 30.7 1.3 -- .12
Mules 23.7 1.4 -- .19
Camels .4 1.0 -- .09


* Change in
-- general
++ general


-52-


production since early 1980s: +/-- selective decrease;
decrease; slight decrease; +/- stability; + slight increase;
increase; ++/- selective increase (concentration).


Table 4.8:











Zone III: The Less Favorable Zone


The Less Favorable Zone (Zone III) is located on the southern plateaus and
along the escarpment traversing Settat Province. The sample for this zone
contains 171 farm households located in 15 douars. The zone covers parts of
Upper Chaouia, the Phosphate Plateau and the region of Beni Meskine. A variety
of shallow soft chalky (biad) and silty red (hamri) soils, both having some
water retention capacity, are interspersed throughout the region and account
for just over 50 percent of the hectares. Although pockets of shallow clay
black soils (tirs) can be found, the remainder of the land is composed of poor
water retaining shallow stony (hrash) or sandy (rmel) soils. The climate is
dry for most of the year with the majority of the annual rainfall (ranging from
200 to 450mm) coming during November/December and February/March.

Barley, durum wheat and bread wheat are the most frequently grown crops
accounting for nearly three-quarters of the hectares operated. Although food
legume crops are occasionally grown, lentils are the only additional crop of
importance. Sixty-nine percent of farm households keep 20 percent of the land
in fallow. Small grain yields of both grain and straw are low, averaging less
than half of those in the More Favorable Zone. Sheep are raised by over three-
quarters of these farm households with the highest number of head per hectare
found in any zone. Cattle are also raised by another three-quarters of farm
households. Donkeys are kept for transportation on 87 percent of these
operations. Draft animals (horses, mules and camels) are found on 46 percent.

With the median farm operation at about 10 hectares and the average 14.9, farm
size varies across a considerable range with 81 percent less than 18 hectares.
A quarter of the land is rented or shared, principally involving urban dwellers
holding land in the douar.

Households tend to be larger than in other zones, 64 percent having 7 or more
members, averaging 7.9 per household. Over a quarter of household heads are
less than 45 years of age, although all age ranges are well represented.

Over half of these farm households engage in off-farm employment, two types of
which can be discerned, occasional and full-time. The latter accounts for
nearly a quarter of all operations and frequently involves a non-resident head
of household. For many, this non-resident head of household employment began
during the drought of the early 1980s. Forty percent of household heads work
off the farm. Forty-seven percent of farm households receive financial
assistance from non-resident family members, including non-resident household
heads. Twenty-one percent possess non-agricultural investments in addition to
their farm assets.

Tractor-powered mechanization has been incorporated into many of these farm
operations, however, many still do not employ tractors. Only 6 percent own a
tractor. Less than half prepare the seedbed for their largest cereal parcels
by custom tractor services. In fact, many farm households hand-broadcast seed
without prior seedbed preparation. Fifty-eight percent use tractor-power for
seed covering on all of their parcels, while nearly a third use only animal
traction. Thirty-eight percent do not rent a combine harvester for harvesting,
another 38 percent do so for all of their parcels. Hand-harvesting is common,
but some fields are simply grazed.

The use of purchased inputs is minimal. Only 21 percent apply commercial
fertilizer to at least one of their small grain parcels. Eighty percent,


-53-









Map 3:


Location of Survey Douars for the Agro-
Ecological Zone of Ahmar in Safi Province


Safi


Chemaia
0


o Zone IV Douars


-54-












however, use certified seed on one of their cereal parcels. Certified barley
seed is rarely encountered. Only 18 percent use herbicides on at least one of
their parcels. Purchased animal feed is more common, with 88 percent making
such purchases.

Median days of household labor allocated to crop production is about 90 with an
average of 121 days. Median days of hired labor is just over 30 with an
average of 68 days. Consequently, labor intensity per hectare varies
considerably with 31 percent allocating less than 10 person-days per hectare
and 26 percent allocating over 31 days per hectare. The level of household
labor allocated to livestock production is also varied. Twenty-nine percent
allocate very little, while 26 percent allocate over two person-years.
Seventeen percent hire at least one full-time shepherd.

Harvested grains and produce are allocated first and foremost to livestock with
60 percent of farm households allocating over 30 percent of that production.
Another 60 percent allocate between 15 and 50 percent of harvested grains and
produce to human consumption. Sixty-percent sell less than 15 percent. Stocks
are built up to varying degrees, 18 percent allocate nothing to stocks and 29
percent allocate over 30 percent.


Zone IV: The Zone of Ahmar

The Zone of Ahmar (Zone IV) is located in the low rainfall interior of Safi
Province. The sample for this zone contains 120 farm households located in 11
douars. The zone is contiguous, by definition, with the Cercle of Ahmar,
covering an area from the escarpment in the west down across the valley of the
Tensift in the south. Soils in this zone have poor water retention capacities.
Shallow soft chalky (biad) and stony (hrash) soils are are interspersed with
shallow silty red (hamri) soils covering 81 percent of the hectares operated.
Few pockets of black clay soils are found. This sparsely populated zone has a
very dry climate, often experiencing severe spring droughts. The majority of
the annual rainfall (ranging from 150 to 300mm) comes during November/December
and to a lesser extent during February/March.

Barley, bread wheat and durum wheat are the most frequently grown crops
accounting for nearly three-quarters of the hectares operated. Corn is also
grown by many in the zone. Fifty-six percent of farm households keep 18
percent of the land in fallow. Grain and straw yields are extremely low.
Sheep and cattle are raised by 83 and 74 percent of the farm households,
respectively. Another 42 percent have small goat herds. Ninety-six percent own
an average of 2 donkeys, providing the primary means of transport. Thirty-nine
percent possess a draft animal (mule, horse or camel).

The aridity of the zone requires larger minimum holdings. Twenty-eight percent
have over 18 hectares for an overall average of 16.7 hectares. Nevertheless, a
near majority of the farm households operate between 3.5 and 13 hectares.
About a third of the land is shared or rented, primarily involving urban
dwellers.

Sixty-two percent have from 4 to 9 household members for an average of 7.8
members per household. Fifty-four percent of household heads are over 54 years
of age.


-55-











Distribution of Soil Types in Zone IV


Farm Households by Soil Type
Percent of Hectares
Percent Average Hectares in the Zone

Tirs 19.0 6.50 7.4
Hrash 47.0 8.82 24.8
Biad 54.5 8.20 26.7
Hamri 48.6 10.25 29.8
Rmel 16.6 5.88 5.9
Other 11.9 7.55 5.4



Table 4.12: Distribution of Crops Grown in Zone IV

Farm Households by Crop Grown
Average Percent of Hectares
Percent Hectares Change* in the Zone

Bread Wheat 79.8 5.28 ++/- 25.2
Durum Wheat 51.4 4.45 -13.7
Barley 98.3 5.76 ++ 33.9
Corn 38.8 2.09 -4.8
Faba Beans 13.8 .92 -.8
Chick Peas .0 na na na
Peas 13.4 1.13 -.9
Lentils 5.7 .79 -.3
Horse Beans .0 na na na
Fallow 56.4 5.21 -17.6
Forage 7.7 2.51 +/- 1.1
Vegetables .0 na -na
Other 15.1 1.87 -1.7



Table 4.13: Composition of Livestock in Zone IV

Farm Households by Livestock
Average Head per Hectare
Percent Number Change* in the Zone

Sheep 82.7 29.3 ++/- 1.99
Cattle 73.9 3.2 -- .23
Goats 42.1 6.4 -- .59
Donkeys 95.6 2.0 +/- .25
Horses 18.3 1.2 -- .06
Mules 32.1 1.2 -- .56
Camels 3.2 1.8 -- .06


* Change in
-- general
++ general


-56-


production since early 1980s: +/-- selective decrease;
decrease; slight decrease; +/- stability; + slight increase;
increase; ++/- selective increase (concentration).


Table 4.11:












Despite the distance from urban centers, over half of these farm households
engage in off-farm employment. For many, this employment is irregular, nearly
a quarter work off-farm for less 'than 100 days a year. A third of household
heads are employed off the farm. In addition 30 percent of farm households
receive financial assistance from non-resident family members. Only 12 percent
possess non-agricultural investments.

Motor-powered mechanization has advanced slowly in this zone. Only 4 percent
own a tractor. Ninety percent do not prepare their seedbeds, prefering to
hand-broadcast seed and work it in with a seed covering passage. Forty-three
percent use custom-tractor services for seed covering on all of their largest
parcels. Occasionally, this involves a chisel plow, as opposed to the more
common cover-crop. Eighty-three percent do not use a combine for harvesting.
The majority of harvesting involves the uprooting of the entire plant, thereby
maximizing bio-mass collection, other parcels are simply grazed.

The use of purchased inputs is rare. Ninety-nine percent do not apply
commercial fertilizer to any of their small grain parcels. Sixty-seven
percent, however, use certified seed on at least one parcel. Certified barley
seed is rarely encountered. Herbicides are not applied. Purchased animal feed
is more common, with 65 percent making such purchases.

Just under half of all farm households allocate over 150 days of household
labor to crop production for an average of 166 days per year. However, 70
percent have less than 30 days of hired labor, averaging 40 days. Over half
allocate between 11 and 30 person-days of labor per hectare, with a median of
less than 20 days. The majority of this labor is involved in hand harvesting.
Household labor allocated to livestock production is more important with 37
percent allocating 2 person-years or more. Only 13 percent allocate less than
300 days. Hired shepherds are also rare, only 9 percent have at least one
full-time hired shepherd.

Harvested grain and produce are directed primarily to animals and human
consumption. Sixty-four percent of farm households feed over 30 percent of
production to their livestock. Nearly half direct over 30 percent to human
consumption. Over half, and over a third, have no production remaining to sell
or stock, respectively.


Agricultural Conditions in the Survey Region

Climatic conditions for the 1989-90 survey year were stable and relatively
average for Zones I, II, and III. Although total rainfall in Zone IV was just
below average, a mid-season drought severely affected small grain production.
Such conditions are not unusual in this zone. Consequently, farm household
behaviors recorded during the survey were close to what one could expect on
average. The following comparison of the four zones will serve to highlight
the prevailing characteristics of agricultural production in the region.

Zone I deserves somewhat separate treatment since it is distinctive in terms of
both productive activities and the high degree of differentiation between farm
households. Intensive vegetable production, irrigated corn, forage crops and
cattle production stand out in the zone's production portfolio. Proximity to
the commodity and labor markets of Casablanca affect both production practices
as well as the farm household structures.


-57-












Land uses and crop yields across the region are related to soil quality as well
as precipitation patterns. Zone II with its better soils and higher rainfall
levels has the greatest diversity in production mix. A variety of food legume
crops are grown primarily in this zone. As one moves toward the interior,
small grain crops become increasingly dominant in Zones III and IV. Barley,
with the exception of Zone I, is the most common crop increasing from 20
percent of all hectares operated in Zone II to 34 percent in Zone IV. Durum
wheat is pervasive in Zones II and III occupying 23 and 27 percent of the land,
respectively. Bread wheat, again with the exception of Zone I where it is the
dominant small grain crop, increases from 8 percent of all hectares operated in
Zone II to 25 percent in Zone IV.

Across all zones, the hand-broadcasting of seed and custom-hire of motorized
equipment are pervasive. Nearly all tractor-powered land preparation and seed
covering is conducted with a cover-crop. Tractor-powered mechanization and the
use of purchased inputs declines with increasing aridity and shallower soils.
As yield levels decline, farm households tend to concentrate more on maximizing
bio-mass collection for animal feed and less on the quality and quantity of
grain produced. Crop production becomes more land extensive as farm sizes
increase from Zone II to Zone IV, although labor intensity per hectare does not
necessarily decline.

Livestock production is pervasive in all zones. Cattle production is more
intensive in Zone I, but more operations have cattle than sheep across the
region. Sheep production is more concentrated in Zone III, but it too, is
pervasive. In contrast to purchased inputs for crop production, the vast
majority across all zones purchase animal feeds, primarily bran and sugar beet
pulp.

Zone I has relatively more smaller and older farm households and households in
Zone III tend to be somewhat larger. For the most part, however, each zone has
a wide and broadly distributed representation of household sizes and ages of
their heads. Non-resident heads of household are more common in Zones I and
III. In all zones, about a half of the farm households have members engaged in
off-farm employment, a third involving the head of household. The hiring of
labor for crop and livestock production is also common to all zones, but tends
to be concentrated on a few operations in each.


-58-












APPENDIX 4.1:

AGGREGATE FARM HOUSEHOLD CHARACTERISTICS
FOR EACH AGRO-ECOLOGICAL ZONE


Table 4.14: Average Cereal Grain Yields for the Largest Parcel of Each Cereal
Farm Households Grow by Agro-Ecological Zone (quintals/hectare)

Largest Parcels Zone I Zone II Zone III Zone IV

Bread wheat 15.6 16.5 5.7 2.1

Durum wheat 16.9 16.8 6.7 1.8

Barley .9 14.9 9.0 2.8

Corn 14.4 8.9 -- 2.9


Table 4.15: Average Cereal Straw Yields for the Largest Parcel of Each Cereal
Farm Households Grow by Agro-Ecological Zone (bales/hectare)

Largest Parcels Zone I Zone II Zone III Zone IV

Bread wheat 63.0 53.6 17.8 8.6

Durum wheat 71.3 54.8 23.3 8.9

Barley 3.3 46.4 23.5 9.2

Corn 64.5 31.4 -- 6.1


-59-











Table 4.16: Percentage of Farm Households in Each Agro-Ecological Zone by
Hectares Operated

Hectares Operated Zone I Zone II Zone III Zone IV

Less than 3.5 43.3 11.4 15.1 10.7

3.5 to 7.9 31.5 28.3 27.9 16.8

8.0 to 12.9 11.4 22.4 20.7 31.4

13.0 to 17.9 1.7 16.7 17.4 13.3

13.0 to 24.9 1.7 10.2 7.6 11.3

25.0 to 49.9 8.6 8.1 8.3 11.4

50 or more 1.7 2.9 3.1 5.2


Table 4.17: Percentage of Farm Households in Each Agro-Ecological Zone by
Size of Household

Number of Members Zone I Zone II Zone III Zone IV

1 to 3 22.2 9.4 10.9 13.4

4 to 6 26.3 35.9 24.8 28.5

7 to 9 31.5 28.9 37.0 33.6

10 to 12 8.6 12.0 18.0 12.5

13 or more 11.4 13.8 9.3 12.0


Table 4.18: Percentage of Farm Households in Each Agro-Ecological Zone by
Age of Household Head

Years of Age Zone I Zone II Zone III Zone IV

Less than 45 19.4 22.8 28.2 24.4

45 to 54 14.9 25.3 25.7 21.4

55 to 64 27.0 29.7 20.7 26.4

65 or more 38.8 22.2 25.4 27.8


-60-












Table 4.19: Percentage
Level of

Days of Household Labor

No off-farm employment

Less than 100 days

100 to 199 days

200 or more days


of Farm Households in Each Agro-Ecological Zone by
Off-Farm Employment

Zone I Zone II Zone III Zone I'

37.7 52.6 48.9 48.8

19.4 17.0 21.5 24.7

14.9 6.4 4.7 8.5

28.0 24.0 24.8 18.0


Table 4.20: Percentage of Farm Households in Each Agro-Ecological Zone by
Off-Farm Employment Status of Household Head

Status of Household Head Zone I Zone II Zone III Zone IV

No off-farm employment 64.0 69.7 60.0 66.5

Employed off-farm 26.3 26.9 28.3 31.2

Non-resident 9.7 3.4 11.7 2.3


Table 4.21: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Financial Remittances from Non-Resident Family Members

Financial Remittances Zone I Zone II Zone III Zone IV

None 68.5 62.4 52.8 70.2

Some 29.8 31.7 31.2 28.1

Considerable 1.7 5.9 16.0 1.8


Table 4.22: Percentage of Farm Households in Each Agro-Ecological Zone
Owning Non-Agricultural Investments

Zone I Zone II Zone III Zone IV

Non-agricultural wealth 39.4 18.5 20.6 12.1


-61-


V


V












Table 4.23: Percentage of Farm Households in Each Agro-Ecological Zone
Owning a Tractor

Zone I Zone II Zone III Zone IV

Own a tractor 13.8 9.2 6.2 3.7





Table 4.24: Percentage of Farm Households in Each Agro-Ecological Zone by
Predominant Mode of Seedbed Preparation for Small Grains

Seedbed Preparation Zone I Zone II Zone III Zone IV

None 13.1 15.9 22.9 89.6

Animal traction only 5.2 7.0 28.6 4.9

Tractor-powered 81.7 77.1 48.5 5.5





Table 4.25: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Tractor-Powered Seed Covering for Small Grains

Largest Parcels Zone I Zone II Zone III Zone IV

None 18.3 16.7 32.6 34.1

Some 1.7 26.5 9.3 22.7

All 79.9 56.8 58.1 43.3





Table 4.26: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Combine Harvesting of Small Grains

Largest Parcels Zone I Zone II Zone III Zone IV

None 9.4 6.7 37.7 83.0

Some 18.3 32.1 23.9 12.6

All 72.4 61.1 38.4 4.4


-62-












Table 4.27: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Commercial Fertilizer Use for Small Grain Production

Largest Parcels Zone I Zone II Zone III Zone IV

None 19.4 17.7 78.9 99.3

Some 16.6 48.9 16.8 .7

All 64.0 33.4 4.3 --





Table 4.28: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Certified Seed Use for Small Grain Production

Largest Parcels Zone I Zone II Zone III Zone IV

None 13.1 9.6 12.7 33.1

Some 46.8 72.6 79.9 66.9

All 40.1 17.9 7.4 --





Table 4.29: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Chemical Herbicide Use for Small Grain Production

Largest Parcels Zone I Zone II Zone III Zone IV

None 60.6 48.0 82.2 100.0

Some 10.4 27.6 16.0 --

All 29.1 24.4 1.8





Table 4.30: Percentage of Farm Households in Each Agro-Ecological Zone
Purchasing Animal Feed

Zone I Zone II Zone III Zone IV

Purchasing animal feed 84.1 85.8 88.1 65.1


-63-











Table 4.31: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Household Labor Allocated to Crop Production

Days of Household Labor Zone I Zone II Zone III Zone IV

Less than 30 days 25.0 12.8 18.9 9.1

30 to 90 days 33.1 22.2 32.4 20.3

91 to 150 days 24.3 24.6 18.0 23.5

151 to 250 days 10.5 19.9 17.2 28.6

251 or more days 7.1 20.5 13.5 18.5


Table 4.32: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Hired Labor Allocated to Crop Production

Days of Hired Labor Zone I Zone II Zone III Zone IV

Less than 30 days 59.2 42.1 44.2 69.6

30 to 90 days 5.3 23.8 29.9 14.5

91 to 150 days 16.2 15.3 13.5 7.6

151 to 250 days 12.3 9.2 8.5 6.0

251 or more days 7.0 9.6 3.9 2.3


Table 4.33: Percentage of
Person-Days

Person-Days per Hectare

Less than 5 days

5 to 10 days

11 to 20 days

21 to 30 days

31 to 45 days

45 days of more


Farm Households in Each Agro-Ecological Zone by
Labor per Hectare of Crop Production

Zone I Zone II Zone III Zone Il

3.6 3.0 13.9 12.7

20.1 12.5 17.3 13.6

17.2 32.2 25.6 37.0

13.6 27.8 17.2 16.8

17.2 15.3 14.3 10.6

28.3 9.3 11.7 9.4


-64-


1


V












Table 4.34: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Household Labor Allocated to Livestock Production

Days of Household Labor Zone I Zone II Zone III Zone IV

Less than 300 days 28.0 15.6 29.4 12.9

300 to 599 days 70.2 60.7 44.3 50.6

600 or more days 1.7 23.8 26.3 36.5


Table 4.35:


Percentage
Level of


of Farm Households in Each Agro-Ecological Zone by
Hired Labor Allocated to Livestock Production


Days of Hired Labor Zone I Zone II Zone III Zone IV

less than 300 days 78.2 86.1 83.1 90.6

300 to 599 days 20.1 9.7 13.8 8.6

600 or more days 1.7 4.2 3.1 .8






Table 4.36: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Harvested Grain and Produce Consumed by Livestock

Percentage for Feed Zone I Zone II Zone III Zone IV


None

Less than 15 percent

15 to 30 percent

31 to 50 percent

Over 50 percent


35.9 6.2 2.6 1.0

15.1 19.8 9.3 9.8

22.2 31.4 28.2 25.2

26.8 32.5 47.0 41.1

-- 10.2 12.8 22.9


-65-













Table 4.37: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Harvested Grain and Produce Sold

Percentage Sold Zone I Zone II Zone III Zone IV

None 44.0 16.1 44.6 53.1

Less than 15 percent 3.5 13.2 14.9 17.0

15 to 30 percent 14.5 25.9 23.1 20.7

31 to 50 percent 21.1 33.9 15.1 9.1

Over 50 percent 16.9 10.8 2.2 --


Table 4.38: Percentage
Level of

Percentage Consumed

None

Less than 15 percent

15 to 30 percent

31 to 50 percent

Over 50 percent


of Farm Households in Each Agro-Ecological Zone by
Harvested Grain and Produce Consumed by the Household

Zone I Zone II Zone III Zone IV

11.6 1.7 5.6 4.5

16.9 26.7 20.8 15.6

20.4 46.0 31.9 33.1

28.5 20.1 28.8 26.4

22.6 5.5 12.9 20.3


Table 4.39: Percentage of Farm Households in Each Agro-Ecological Zone by
Level of Harvested Grain and Produce Stocked for Later Use

Percentage Stocked Zone I Zone II Zone III Zone IV

None 20.4 11.8 17.7 34.9

Less than 15 percent 18.7 36.3 26.3 19.2

15 to 30 percent 13.4 26.9 26.8 29.3

31 to 50 percent 37.7 22.7 25.7 13.2

Over 50 percent 9.9 2.3 3.5 3.4


-66-












APPENDIX 4.2:


GLOSSARY OF TERMS USED IN TEXT




TYPES OF SOIL ACCORDING TO LOCAL USAGE


Tirs: Normally deep soils, high in clay content, having a black color. These
soils swell and stick when wet, and shrink and crack when dry. They
have a high production potential when water is not limited. Tirs soils
are most commonly found in the More Favorable Zone (Zone II) tending to
decrease in depth from west to east. (Chromoxerert)

Hrash: Shallow stony soils, often eroded down to the bedrock. They have little
water retention capacity. Production potential is low when water is
not limited, but can produce more than tirs when rainfall is low but
timely. Hrash soils are most commonly found in the Coastal Zone (Zone
I) or in the dry interior areas of the Less Favorable Zone (Zone III)
and the Zone of Ahmar (Zone IV). (Rendolls)

Biad: Normally shallow soils most notable for their white color. These soils
are soft, coming either in a chalky or calcareous form or as a marl
having up to a 50 percent clay content. They have some moisture
retention capacity, depending on the level of clay content. Production
potential is medium when water is not limited. Biad soils are
scattered in pockets throughout Zones II, III, and IV, most commonly
found on eroded lands of interior plateaus.

Hamri: Red soils due to their high content of iron oxide. These soils normally
have a balanced texture, often silty and rich in potassium. Their
moisture retention capacity is good if not too shallow. Production
potential is good when water is not limited. Hamri soils are found
throughout the region, but most concentrated in the Less Favorable Zone
(Zone III) and the Zone of Ahmar (Zone IV) where they may be shallow
and sandier in texture.

Rmel: The sandy texture of these soils is their most significant
characteristic. These soils are very low in moisture retention
capacity. Production potential is limited unless water is readily
available through frequent rains or irrigation. Rmel soils are most
commonly found in the Coastal Zone (Zone I) or in small pockets of the
interior areas of the Less Favorable Zone (Zone III) or the Zone of
Ahmar (Zone IV).


-67-













OTHER TERMINOLOGY USED IN THE TEXT


Age Group Categories: To facilitate discussion a standard terminology has been
adopted in the presentation of findings concerning age groups.
Children refer to those less than 15 years of age. Young men or women
refers to those between 15 and 30 years of age. Middle-aged men and
women refers to those between 30 and 50 years of age. Elderly men and
women refers to those over 50 years of age.

Animal Feed Unit: This provides a standard measuring unit to compare different
types of animal feed used in the study area. The weights used to
calculate this measure are: 1.0 for cereal and food legume grain; 0.5
for cereal and food legume straw (15 kilograms per bale); and 0.85 for
bran and sugar beet pulp.

Association, Land: This refers to a variety of forms of share cropping.
Traditionally, sharecropping involved the land owner providing all
inputs except for labor and the sharecropper received one fifth of the
production. Today, the output of sharecrop production is divided more
equally with the operator receiving from a third to two-thirds. Inputs
are more frequently provided by the operator as well. The contracts
are usually informal and the land owner may be an urban resident, a
non-resident family member, or, more rarely, another farmer.

Association, Livestock: This refers to a variety of forms of sharing the costs
and production of livestock, both cattle and sheep. The operator
usually receives from a third to a half of all off-spring. Specific
arrangements vary and usually involve an urban resident.

Bales: A standard measure for bales of hay or straw has been established as
equivalent to 15 kilograms.

Cattle, Cross Breed: Cattle which are the result of crossing pure bred cattle
(often holstein) with the local species.

Cattle, Local: Cattle which have been traditionally raised and adapted to the
study region.

Cattle, Pure: Normally imported holstein cattle.

Collective/Communal Lands: This refers to a variety of forms of collective or
communal land ownership. Generally, lands are assigned to douar
residents by the collective leadership. Security of land tenure is
variable, asthese lands may be reassigned annually.

Cover-crop: This is the local name used for a tandom set of off-set disks, the
most commonly used tractor-drawn implement in the region. The design
involves two rows of disks, off-set, on a metal frame. The vast
majority of these implements have been locally-constructed and are
easily repaired.


-68-












Douar: Technically, this term refers to an administrative unit at the village
level. The dwellings of douar residents may be either concentrated or
dispersed. In general, these dwellings and the lands farmed are
contiguous, however, the dwelling and land of a particular resident may
be intermixed with those of other douars.

Fallow: Land described as left in fallow in this report refers to both operated
hectares which are worked during the year and those which are left to
grow weeds for animal grazing.

Harvested grain and produce: This term is a constructed indicator referring to
the average percent of all crops harvested for grain and/or produce by
each farm household and subsequently allocated to some end use: sale,
stocks, human or animal consumption. It does not include straw
production which is also harvested for animal feed.

Large Draft Animals: This term is inclusive of horses, mules and camels capable
of pulling heavy loads: wagons, carts and animal traction equipment.
For the most part, only horses and mules are actually involved. Some
farmers use donkeys for small carts and occasionally will draw an
animal traction plow or rake with two donkeys. Cattle have also been
used for animal traction equipment.

Livestock Production: This term as used in the text refers to ruminants:
cattle, sheep and goats. Poultry production conducted almost
exclusively by women and pervasive on a small scale across the entire
region has not been included.

Medic/Alfalfa: Farmers were asked specifically about whether they grew and fed
medic production to their livestock. Some confusion was apparent but
not pursued during the interviews and medic could refer to a wide
range of forage species, including alfalfa and clover which is found
in the Coastal Zone.

Milk Production Sold: When milk was sold, the primary buyer was a cooperative.
Milk production is routinely transformed into butter in the home, or
consumed as skim milk. Surplus production during the lactation period
is either fed to calves or shared with neighbors.

Ruminant Unit: This term refers to a standard measure of all ruminants (cattle,
sheep and goats). It is calculated with the following equivalancies: 1
head of cattle equals 5 head of sheep equals 5 head of goats. It is
similar to UGB (unite de gros b6tail), but does not include large draft
animals or donkeys.

Small Grain Crops: This term includes barley, durum wheat and bread wheat,
often referred to as automn or winter cereals. These cereals are
distinguished from corn, a large grain cereal.

Veterinary Visits: Farmers were asked about whether or not they had a
veterinary visit for either their cattle or sheep. The reason for the
visit was not asked.


-69-

















CHAPTER 5

TYPOLOGY OF
AR IDOCULTURE FARM ING SYSTEMS




This chapter provides representative portraits of the 19 farming systems
identified in Chapter 3. These portraits are designed to achieve three
objectives: (1) to demonstrate the interdependency between the level and form
of farm household resources and their mobilization within the sub-systems to
achieve farm household goals; (2) to highlight distinctive patterns and key
components associated with each farming system; and (3) to provide a farming
system-level focus on specific conditions which a continuing dialog between
farmers and researchers may improve. These descriptions are idealized images,
emphasizing modal patterns, and are not intended to account for all of the
existing diversity in farm household activities and circumstances.

The types are presented by zone and in the order established during the cluster
analyses. A wide range of variables have been used to elaborate the
circumstances and practices associated with each farming system. Sample
statistics are indicative of and proportional to the population parameters for
each type (for sampling procedures and climatic conditions, see Chapters 2 and
4). The role of these statistics is to indicate levels of magnitude and
population tendencies.

A standard format for each description has been followed facilitating
comparisons between types within and across zones. First, farm household
resources are described, followed by the cropping, livestock, and non-farm sub-
systems. A final section highlights key features and their interdependencies.
Key terms utilized in this text have been defined in the Glossary at the end of
the preceding chapter. Four tables have been included for each type. The
first of these presents modal patterns of cereal production practices for the
largest parcel of each cereal cultivated. The second and third indicate the
relative importance of harvested crops and purchased animal feed to livestock
production. The last table summarizes the seasonal sheep and cattle feeding
schedules for a range of common feed sources. In addition, each description is
accompanied by a graphic representation of key features to facilitate
understanding of system interdependencies.


ZONE I: THE COASTAL ZONE

Three types of farming system have been identified in this zone. The sample on
which they are based provides from 6 to 17 cases per type. It should be
recalled that the sample for this zone is small and difficulties were
experienced in accessing the population.


-71-













Type I-1


The Type I-1 farming system represents 38 percent of all farms within the zone.
They are characterized by intensive cropping operations, a dependence on hired
labor, and a high level of non-farm investments.


Resources

These farm operations are medium to large, averaging 16 hectares distributed in
6 parcels of primarily hrash and hamri soils. Owned land averages 9 hectares.
Eighty-two percent own over 2 hectares, and half (48%) attain full operational
size through sharing additional land. A few farm land shared with non-resident
family members. Of the 73 percent owning sheep, 69 percent have flocks of less
than 26 head. All own cattle, 54 percent with herds of over 5 head. These
operations own an average 19 ruminant units. None hold sheep in association,
however, a quarter have over three-quarters of their cattle in association.
Eighty-seven percent own an average of 1.3 large draft animals.

Thirty-six percent own a tractor, 70 percent a car and 25 percent a truck.
Eighty-seven percent have investments in other businesses, shops, and housing.
These households are wealthy. Seventy-five percent have had frequent access to
agricultural credit.

Households tend to be moderate in size, 69 percent with fewer than 9 members.
Eighty-seven percent of the household heads are over 55 years of age. They
have been in charge of their operation for an average of 21 years. With 85
percent of their spouses over 45, these households are predominantly in the
later stages of the family cycle. Only 30 percent of household members are
less than 15 years of age. Boys tend to out number girls by a ratio of 3 to 2.


Cropping Sub-System

Crop production for the Type I-1 farming system is highly mechanized, input and
labor intensive. These households allocate more labor, both hired and
household, than others in the zone to crop production (327 days/farm). Nearly
two-thirds (63%) of this labor is hired. Labor intensities per hectare are
high as well, 52 percent with more than 20 person-days per hectare. Elderly
men and young men provide the vast majority of household labor. Elderly women
and middle-aged men also make significant contributions.

Sixty percent cultivate 3 or 4 different crops led by corn (87%), vegetables
(73%), bread wheat (70%) and durum wheat (52%). Additional crops include
forage crops (45%), barley (27%), faba beans (9%) and other specialty crops
(18%). Forty-one percent keep 6 percent of the land in fallow. Twenty-two
percent of the land is committed to bread wheat, 21 percent to corn, 18 percent
to vegetables, 13 percent to durum wheat and 10 percent to forage crops. Only
10 percent of the land is allocated to other crops. Crop sales account for a
third (33%) of all production. These households are 87 percent self-sufficient
in all small grains consumed in the home.


-72-








Table 5.1: Sumary of Type I-1 Cropping Practices for CereaLs Based on the Largest Parcel of Each Cereal CuLtivated (1989-90)

Population Parameters Durum Wheat Barley Bread Wheat

% of farmers cultivating 52 27 70

average hectares in parcel 2.9 1.9 4.8

Cropping Practices

seed bed preparation 91% with two passes 83% with two passes 82% with two passes 95% with

% of farmers fertilizing at seeding 100 83 (+33% w/manure) 78

% of farmers using certified seeds 38 0 72

seeding rate 91% 191-300 kg/ha 50% 230-540 kg/ha 88% 175-350 kg/ha 57

% of farmers seeding in rows na na na

seeding date 62% 1-30 November 67% from 1-10 November 58% from 1 20 Dec 62%

seed covering 100% with cover crop 100% with cover crop 100% with cover crop 53% b'

% of farmers grazing in winter na 83 na


% of farmers applying top-dressing

% of farmers harrowing

% of farmers weeding by hand

% of farmers applying herbicides

% of farmers combine harvesting


Yields

average yield (grain)

average yield (straw)


16.8 qx/ha

77 bales/ha


83


0

0
(grazed)



0.0 qx/ha

0 bales/ha


100

na

52

42


17.5 qx/ha

53 bates/ha


Corn

87

1.0


two or more passes

75

15

% 13-24 kg/ha

100

during February

y animal traction

na

28

80

100

0


16.1 qx/ha

59 bales/ha











The cereal cropping practices of these farmers involve two seedbed preparation
passes with a tractor-drawn implement and seed covering with a cover-crop.
When wheat is harvested, a combine harvester is always used. Fertilizing at
seeding is common (over 80% use 14-28-14) and top-dressing (over half use urea)
is practiced by all on wheat crops. Over a third purchase certified durum
wheat seed, and nearly three-quarters, certified bread wheat seed. Seeding
rates are high. Herbicides are also often applied, although bread wheat is
more commonly weeded by hand. Corn production receives similar care, but no
herbicides are applied. Average yields are around the norm for the zone.
Barley is left in the field as pasture. Ninety-one percent irrigate at least
one or two parcels (usually for vegetables and corn).


Livestock Sub-System

Livestock production is a major component of the Type I-1 farming system.
Salaried shepherds constitute 59 percent of the average 392 person-days
allocated to livestock tending. The share of household labor allocated to
livestock is only just over a quarter, half of that allocated by any other
farming system. This minimal labor commitment is distributed across all family
members depending on who is available at the time. Young and elderly men, and
middle-aged and elderly women are most often involved.

Although sheep flocks are normally small is size, some own very large flocks
contributing to the high average size of 42 head. More important, for all of
theses farm operations are cattle. Cattle herds average 13 head of which 79
percent are cross breeds. Only 5 percent are pure bred cattle. These
operations have an average of 1.52 ruminant units per hectare. An average of
13 sheep and 3 cattle were sold during the year. Wool production averaged 41
kilograms and 6 percent of this was sold. Milk production averaged 7760 liters
during the year and 38 percent of this was sold. Disease prevention and
treatment is provided by a veterinarian on 47 percent of these operations for
sheep and on 70 percent for cattle.

Thirty-nine percent of those harvesting corn feed over half (60%) of it
directly to their livestock. For another 73 percent, nearly all of their
harvested straw is fed directly, as well. Half of those growing faba beans
feed 60 percent to their livestock. Barley and other forage crops are grazed.
Purchases of animal feed are essential to this farming system. Bran and sugar
beet pulp are purchased in large quantities by four-fifths of all operations.
Half of these operations must also purchase additional cereal grain and straw.
A fifth also purchase food legume straw for feed. No more than half of these
operations can be considered self-sufficient in cereal feed grains and straw.

Cereal grains and straw are the principal sheep feeds during the fall and
winter months. Nearly half supplement this with bran and sugar beet pulp.
Three-quarters rely on bran and sugar beet pulp for their cattle during this
period, in addition to cereal straw. Over a third feed food legume grains to
both sheep and cattle, as well. Nearly a third graze stubble during the fall,
shifting over to barley grazing for up to a half during the winter, when some
also use fallow and rangeland. Medic/alfalfa is an important feed source
during the winter and into the spring for about half of these operations, when
the feeding of cereal grains, straw, bran and sugar beet pulp drops off.
Stubble provides the primary feed during the summer, although a quarter still
continue with bran and sugar beet pulp for their cattle.


-74-













Table 5.2:


Summary of Harvested Crops Fed to Livestock for the 100 Percent
of Type I-1 Farm Households Who Possess Ruminants


Harvested crop


Percent of those
with ruminants who
harvested and fed
crop to livestock


Average percent
of harvested crop
fed to livestock


Average quantity
per ruminant unit


Barley
Corn
Faba beans
Straw
Other


1.01 qx.
.45 qx.
29.77 bales
n.a.


Table 5.3:


Animal Feed


Summary of Purchased Animal Feed for the 100
Farm Households Who Purchased Feed


Percent of farms
purchasing feed


Average quantity
Purchased


Percent of Type I-1


Average quantity
per ruminant unit


Bran
Sugar beet pulp
Cereal grain
Legume grain
Legume straw
Cereal straw


30 qx.
15 qx.
14 qx.
8 qx.

32 bales


1.84 qx.
.84 qx.
.94 qx.
.77 qx.

4.73 bales


Table 5.4: Type I-1 Seasonal Sheep and Cattle Feeding Schedules

Percent of farm households using feed


Fall
Sheep Cattle


Winter
Sheep Cattle


Spring
Sheep Cattle


Summer
Sheep Cattle


Cereal grains
Legume grains
Bran
Sugar beet pulp
Hay
Cereal straw
Legume straw
Weeds
Medic
Fallow
Rangeland
Stubble
Barley grazing


0 0


53 43


25 14


-75-


0 0












Figure 6: Type 1-1 Farming System


Labor Allocations by Sub-System and Category


9 members


Crops
Livestock
Non-Farm


SI I I I I


0 100 200 300 400 500 600 700
Days of Labor

Men Women ChildrenK Hired


I I


Repartition of Land Uses

Bread Wheat
.-.. --r .Durum Wheat


Forage Crop,


Corn


Fallow

Other


Vegetables


16 hectares/6 parcels


End Use of Harvested
Grains and Produce


Animal Feed


Consumption


Stocks
Sale


87% self-sufficient in small grains
for human consumption


Animal Feed Composition by Source


42 Sheep
13 Cattle


Feed Grains 1 l
Bran/Pulp I
Straw -


1 I


0 1 2 3 4 5
Average Feed Units (Qx.) per Ruminant


* Produced- Purchased


-76-


I I


6
Unit


L -~t:ej~aa;cxxxxx~
I i.- 1 ~F~j;"~~;c"~,~'i~X'~~'$=;t
I rl


I I


I I












Non-Farm Sub-System

Non-farm activities are a key component of the Type I-1 farming system. Fifty-
seven percent of these farm households have at least one member working off the
farm for an average of over 270 days per year. This accounts for nearly a
third of all household labor allocations. Thirty-four percent of the household
heads work off farm in commerce (61%), in government (13%), in artisanal trades
(13%) and in occasional employment (13%). More important, however, than this
off-farm employment is ownership by 87 percent of these households of non-farm
businesses and housing which contribute significantly to their overall standard
of living.

All of these household heads had been involved in some type of employment
before taking over the farming operation: 43 percent in skilled trades, 36
percent in agriculture, 17 percent in commerce, and 4 percent in government.
Half had migrated for a period of over 18 years. Another half had worked as an
adult on their father's farm.


System Interactions

The Type I-1 farming system is mechanized and input intensive. Capable of
attaining near self-sufficiency in crop production for home consumption, these
farm households specialize in cash crops (vegetables, corn, and bread wheat)
and cattle to generate farm income.

The off-farm wealth of these households provides a ready basis for investments
in crop or livestock production. Off-farm employment provides an additional
flow of cash to the household. However, the level of farm activities tends to
diminish when major time commitments are required of the household head in his
non-farm activities.

Dairy production is the focus of livestock activities and is conducted
primarily by hired labor. Crop production also requires a significant amount
of hired labor, particularly for labor intensive activities associated with
vegetable production. The management of hired labor poses the major constraint
for this farming system.

Although capable of purchasing the required feed for their cattle, increases in
the quantity and quality of forage production would improve the profitability
of these operations. In order to gain information on improved production
techniques, these farm operators rely in part on extension agents, and, to a
lesser extent, on other douar residents and television programs.


-77-












Type I-2


The Type I-2 farming system represents 24 percent of all farms within the zone.
These farm operations are small, predominantly managed by women heads of
household or elderly farm couples. Livestock production is the major activity.


Resources

These small farm operations average 3 hectares of predominantly tirs or rmel
soils distributed in 3 parcels. Owned land averages 2.5 hectares and the vast
majority do not expand their operations further. Only 41 percent own sheep
with flocks of 1 to 10 head. However, all own cattle, 67 percent with between
'3 and 10 head. These farm households own an average of 4 ruminant units. A
third hold 29 percent of their sheep in association. Only 7 percent hold all
of their cattle in association. A third possess an average of 1.0 large draft
animals.

None of these households owns a tractor, car or truck. Many, however, own
animal traction equipment. Twenty-six percent have some non-farm investment.
Overall these farm households have the lowest standard of living in the zone.
Fifty-two percent have had some access to agricultural credit.

The Type I-2 farm households are small, 78 percent have less than 4 members.
Fifty-two percent of household heads are women, almost all of whom are less
than 45 years of age. Thirty-three percent of household heads are over 65
years of age with spouses over 55. These household heads have been in charge
of their operations for an average of 18 years. Forty-four percent of
household members are less than 15 years old. Women out number men by a ratio
of 2 to 1.


Cropping Sub-System

Although crop production receives a low priority in the Type I-2 farming
system, many operations are mechanized and purchased inputs used. Less than 9
percent (30 person-days/year) of all household labor is allocated to crop
production. Just over a quarter (28%) of these days are supplied by hired
labor. Labor intensities are low with 59 percent allocating less than 10 days
per hectare. Middle-aged women provide the majority of household labor aided
to a lesser extent by elderly women. Middle-aged men occasionally provide
additional help.

Ninety-three percent cultivate 1 to 3 crops led by bread wheat (67%), forage
crops (67%), and corn (59%). Additional crops include vegetables (14%), durum
wheat (7%), and barley (7%). Two-thirds keep 15 percent of the land in fallow.
Thirty-five percent of the land is committed to forage crops, 29 percent to
bread wheat, and 14 percent to corn. Only 8 percent of the land is committed
to other crops. Twenty-one percent of production is sold. These households
are only 66 percent self-sufficient in small grains consumed in the home. They
are often the beneficiaries of produce gifts from other local farmers.


-78-









Table 5.5: Sumary of Type I-2 Cropping Practices for Cereals Based on the Largest Parcel of Each Cereal Cultivated (1989-90)

Population Parameters Durum Wheat Barley Bread Wheat

% of farmers cultivating 7 7 67

average hectares in parcel 1.0 .5 1.3

Cropping Practices

seed bed preparation 100% with two passes 100% with no passes 897/ with two passes 78%

% of farmers fertilizing at seeding 100 0 100

% of farmers using certified seeds 100 0 39

seeding rate 100% 20-80 kg/ha 100% 230-540 kg/ha 100% 175-350 kg/ha 7

% of farmers seeding in rows na na na

seeding date 100% during December 100% 1-10 November 89% after 20 November 100%

seed covering 100% with cover crop 100% with cover crop 100% with cover crop 100%


% of farmers grazing in winter

% of farmers applying top-dressing

% of farmers harrowing

% of farmers weeding by hand .

% of farmers applying herbicides

% of farmers combine harvesting

Yields

average yield (grain)

average yield (straw)


na

100

na

0

100

100




26.0 qx/ha

72 bales/ha


100

0


0

0




8.0 qx/ha

30 bales/ha


50

100




21.2 qx/ha

86 bales/ha


ith two passes

22

0

8% 25-32 kg/ha

100

from 1-10 March

by animal traction

na

0

100

100

0

na




13.6 qx/ha

51 bales/ha




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