• TABLE OF CONTENTS
HIDE
 Front Cover
 Title Page
 Dedication
 Acknowledgement
 Table of Contents
 List of Tables
 List of Figures
 List of acronyms
 Abstract
 Introduction
 General characteristics of Senegalese...
 Methods and materials
 Results of linear programming...
 Adaptability analysis
 Conclusions and recommendation...
 Characteristics of the household...
 Reference
 Biographical sketch






Title: Comparison of on-farm research and extension methods in small scale farm systems in lower Casamance, Senegal
CITATION PAGE IMAGE ZOOMABLE PAGE TEXT
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00055218/00001
 Material Information
Title: Comparison of on-farm research and extension methods in small scale farm systems in lower Casamance, Senegal
Physical Description: xvi, 116 leaves : ill. ; 29 cm.
Language: English
Creator: Lo, Mamadou, 1952-
Publication Date: 1997
 Subjects
Subject: Agriculture -- Research -- Senegal   ( lcsh )
Agricultural extension work -- Research -- Senegal   ( lcsh )
Farms, Small -- Senegal   ( lcsh )
Agricultural Education and Communication thesis, M.S   ( lcsh )
Dissertations, Academic -- Agricultural Education and Communication -- UF   ( lcsh )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Thesis: Thesis (M.S.)--University of Florida, 1997.
Bibliography: Includes bibliographical references (leaves 111-114).
Statement of Responsibility: by Mamadou Lo.
General Note: Typescript.
General Note: Vita.
Funding: Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.
 Record Information
Bibliographic ID: UF00055218
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved, Board of Trustees of the University of Florida
Resource Identifier: aleph - 002267621
oclc - 37230442
notis - ALM0621

Table of Contents
    Front Cover
        Front Cover
    Title Page
        Page i
        Page ii
    Dedication
        Page iii
    Acknowledgement
        Page iv
        Page v
    Table of Contents
        Page vi
        Page vii
        Page viii
    List of Tables
        Page ix
        Page x
    List of Figures
        Page xi
        Page xii
    List of acronyms
        Page xiii
        Page xiv
    Abstract
        Page xv
        Page xvi
    Introduction
        Page 1
        History of agricultural research and extension
            Page 2
            Early model
                Page 2
                Page 3
                Page 4
            Farming systems research and extension
                Page 5
                Page 6
                Page 7
                Page 8
                Page 9
                Page 10
                Page 11
                Page 12
                Page 13
                Page 14
                Page 15
                Page 16
                Page 17
                Page 18
            Farm management and linear programming
                Page 19
            Extension
                Page 20
                Page 21
                Page 22
        History of research systems in Senegal
            Page 23
            Page 24
            Page 25
        Researchable problem
            Page 26
        Hypothesis
            Page 27
        Objectives
            Page 27
            Primary objectives
                Page 27
            Secondary objectives
                Page 28
        Limitation of the study
            Page 28
        Difinition of terms
            Page 29
            Page 30
            Page 31
    General characteristics of Senegalese agriculture
        Page 32
        Overview of the agricultural sector
            Page 32
            Page 33
            Page 34
            Page 35
        Characteristics of the studied area
            Page 36
            Page 37
            Page 38
            Page 39
            Page 40
            Page 41
        Description of the representative household
            Page 42
    Methods and materials
        Page 43
        Population and sample
            Page 43
        Data collection and analysis
            Page 44
        Methods of analysis
            Page 44
            Page 45
            Page 46
    Results of linear programming analysis
        Page 47
        Characteristsics of the village of loudia ouloff
            Page 47
        Characteristics of the studied household
            Page 48
            Schematic modeling of the household
                Page 48
            Gender analysis of the household
                Page 48
                Page 49
                Page 50
        Linear programming analysis of the household
            Page 51
            Resources and minimal survival constraints
                Page 51
            Maximizing the family income
                Page 52
            Influence of gender in the household
                Page 52
            Maximizing the female income
                Page 53
            Maximizing male income
                Page 53
            Proposition of basket making as one alternative solution for the household
                Page 54
        Comparison of results analysis of linear programming with Farmap/Brads results
            Page 55
            Page 56
            Page 57
    Adaptability analysis
        Page 58
        Page 59
        Calculating the enviromental index
            Page 60
            Page 61
        Relating treatment response to enviroment on EI
            Page 62
            Page 63
            Page 64
            Page 65
        Relationship between EI and enviromental characteristics
            Page 66
        Definition of tentative recommendation domains
            Page 67
        Determining risk associated with the new technologies
            Page 68
            Page 69
            Page 70
        Definition of final recommendation domains
            Page 71
        Comparing results using farmers' evaluation criteria
            Page 71
            Page 72
            Page 73
            Page 74
            Page 75
            Page 76
        Definition of tentative recommendation domains using farmers' evaluation criteria
            Page 77
        Determining risk associated with the different varieties using farmers' criteria
            Page 77
            Page 78
            Page 79
            Page 80
        Define final recommendation domains using farmers' evaluation criteria
            Page 81
        Extension recommendations for each of the recommendation criteria
            Page 81
            Page 82
    Conclusions and recommendations
        Page 83
        Conclusions
            Page 83
            Page 84
            Page 85
        Recommendations
            Page 86
            Page 87
    Characteristics of the household in Loudia Ouloff
        Page 88
        Page 89
        Page 90
        Page 91
        Page 92
        Page 93
        Page 94
        Page 95
        Page 96
        Page 97
        Page 98
        Page 99
        Page 100
        Page 101
        Page 102
        Page 103
        Page 104
        Page 105
        Page 106
        Page 107
        Page 108
        Page 109
        Page 110
    Reference
        Page 111
        Page 112
        Page 113
        Page 114
    Biographical sketch
        Page 115
        Page 116
        Page 117
Full Text












COMPARISON OF ON-FARM RESEARCH AND EXTENSION METHODS IN SMALL
SCALE FARM SYSTEMS IN LOWER CASAMANCE, SENEGAL














By

MAMADOU LO


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


1997
















COMPARISON OF ON-FARM RESEARCH AND EXTENSION METHODS IN SMALL
SCALE FARM SYSTEMS IN LOWER CASAMANCE, SENEGAL














By

MAMADOU LO


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


1997



























Copyright 1997

by

Mamadou Lo



























To my parents and my wife for their prayers and

support.














ACKNOWLEDGMENTS


First, I would like to thank my scientific advisor Dr.

Peter E. Hildebrand for his support and advice during all my

studies at the University of Florida, for his help and

advice defining the research proposal preparation and

execution of this research.

I am thankful to my academic advisor Dr. Mathew T.

Baker for his constructive instructions and advice.

I am also thankful to Dr. Tracy Hoover for her critical

instructions and advice.

All three were excellent support as members of my

supervisory committee. I am also grateful to my Institution

(ISRA), to USAID, to Thomas Jefferson Fellowship and to the

University of Florida (UF) for their major support for my

studies. In Senegal, I would like to thank farmers of Lower

Casamance, my colleagues of Djibelor and ISRA for their

support.

My thanks go to my parents and my wife for their

prayers and blessing. It is wonderful to have their magic

wisdom, love and courage helping me in fighting for my

dreams and ideals.








Finally, I would like also to thank all my friends and

colleagues at the University of Florida, for their

friendship, support, encouragement, and advice and for

listening to me and making my life easy in Gainesville,

especially Michael Dougherty, Jonathan Moscatello, Abib

Araujo, Elena Bastidas, and Christopher Johnson. I am also

grateful to all UF's professors and technicians who, near

and far have contributed to the success of my studies.















TABLE OF CONTENTS


ACKNOWLEDGMENTS .......................................... iv

LIST OF TABLES......................................... ix

LIST OF FIGURES........................................ xi

LIST OF ACRONYMS....................................... xiii

ABSTRACT....................................... ......... xv

CHAPTERS

1 INTRODUCTION.... .............................. 1

1.1 History of Agricultural Research and
Extension. ................................. 2
1.1.1 Early Model............................. 2
1.1.2 Farming Systems Research and Extension. 5
1.1.3 Farm Management and Linear Programming. 19
1.1.4 Extension............................... 20
1.2 History of Research Systems in Senegal..... 23
1.3 Researchable Problem....... ................ 26
1.4 Hypothesis ................................. 27
1.5 Objectives.................................. 27
1.5.1 Primary Objectives..................... 27
1.5.2 Secondary Objectives................... 28
1.6 Limitation of the Study................... 28
1.7 Definition of Terms ....................... 29

2 GENERAL CHARACTERISTICS OF SENEGALESE
AGRICULTURE.................................... 32

2.1 Overview of the Agricultural Sector........ 32
2.2 Characteristics of the Studied Area......... 36
2.3 Description of the Representative Household 42

3 METHOD AND MATERIALS.......................... 43









3.1 Population and Sample..................... 43
3.2 Data Collection and Analysis.............. 44
3.3 Methods of Analysis....................... 44

4 RESULTS OF LINEAR PROGRAMMING ANALYSIS.......... 47

4.1 Characteristics of the Village of
Loudia Ouloff........................... 47
4.2 Characteristics of the Studied Household.. 48
4.2.1 Schematic Modeling of the Household.... 48
4.2.2 Gender Analysis of the Household........ 48
4.3 Linear Programming Analysis of the
Household .................................. 51
4.3.1 Resources and Minimum Survival
Constraints............................ 51
4.3.2 Maximizing the Family Income............ 52
4.3.3 Influence of Gender in the Household... 52
4.3.4 Maximizing Female Income............... 53
4.3.5 Maximizing Male Income.................. 53
4.3.6 Proposition of Basket Making as one
Alternative Solution for the Household. 54
4.4 Comparison of Results Analysis of Linear
Programming with Farmap/Brads Results..... 55

5 ADAPTABILITY ANALYSIS.......................... 58

5.1 Calculating the Environmental Index........ 60
5.2 Relating Treatment Response to Environment
on EI..................................... 62
5.3 Relationship Between El and Environmental
Characteristics ........................... 66
5.4 Definition of Tentative Recommendation
Domains .................................... 67
5.5 Determining Risk Associated with the New
Technologies................................ 68
5.6 Definition of Final Recommendation
Domains .................................... 71
5.7 Comparing Results Using Farmers' Evaluation
Criteria .................................. 71
5.8 Definition of Tentative Recommendation
Domains Using Farmers' Evaluation
Criteria................................. 77
5.9 Determining Risk Associated with the
Different Varieties Using Farmers'
Criteria ................................. 77
5.10 Define Final Recommendation Domains
Using Farmers' Criteria................... 81
5.11 Extension Recommendations for Each of the
Recommendation Domains.................... 81


vii










6 CONCLUSIONS AND RECOMMENDATIONS.................. 83

6.1 Conclusions................................ 83
6.2 Recommendations............................ 86

APPENDIX CHARACTERISTICS OF THE HOUSEHOLD IN LOUDIA
OULOFF........................................ 89

LIST OF REFERENCES..................................... 111

BIOGRAPHICAL SKETCH.................................... 115


viii














LIST OF TABLES


TABLE PAGE

2.1 Desciption of the representative household......... 42

4.1 Farm budgets and income in the surveyed area
for a Rainy Year (1984), comparing Farmap/Brads
and Linear Programming............................ 57

5.1 Anova for yields of nappe rice variety trials
(kg/ha)........................................... 60

5.2 Response of four improved nappe rice varieties
and the Farmers' Local Variety (kg/ha) from On
farm research results (1982-1985). Lower
Casamance, Senegal................................. 61

5.3 Nappe rice variety trials regression estimates.... 63

5.4 Environmental characteristics data, sorted by EI,
for nappe rice variety trials (1982-1985). Lower
Casamance, Senegal.................................. 67

5.5 Tentative recommendation domains and the
technologies recommended for nappe rice variety
trials based on environmental characteristics
(zones) and evaluation criterion kg/ha............. 68

5.6 Calculation of lower confidence limits (kg/ha)
for two yielding domains based on zones for five
nappe rice variety trials (1982-1985). Lower
Casamance, Senegal................................ 68

5.7 Response of four improved nappe rice varieties
(Height of Plants) and Farmers' Local variety
for On-farm nappe rice variety trials (1982-1985).
Lower Casamance, Senegal.......................... 73

5.8 Response of five improved nappe rice varieties
(Plant Cycle) for On-farm nappe rice variety
trials (1982-1985). Lower Casamance, Senegal...... 74








5.9 Response of four improved nappe rice varieties
(Tillers/m2) and Farmers' Local Variety
for On-farm nappe rice variety trials (1982-1985).
Lower Casamance, Senegal.......................... 75

5.10 Summary of the recommendation domains and the
technologies recommended for nappe rice variety
trials (1982-1985). Lower Casamance, Senegal...... 81

5.11 Multiple recommendations and the recommended
technologies for nappe rice variety trials (1982-
1985), based on environmental characteristics
and four evaluations criteria. Lower Casamance,
Senegal........................................... 82

A.1 Schematic modeling of the household............... 89

A.2 Activities analysis of the household.............. 90

A.3 Resource analysis of the household................ 91

A.4 Benefits and incentives analysis.................. 92

A.5 Seasonal calendar of the household................. 93

A.6 Basic matrix of the household..................... 94

A.7 Maximizing family income.......................... 96

A.8 Gender analysis of the household.................. 99

A.9 Maximizing female income.......................... 102

A.10 Maximizing male income............................ 105

A.11 Basket making as one alternative solution for the
household............................... .......... 108















LIST OF FIGURES


FIGURE PAGE

2.1 Administrative map of Senegal.................... 33

2.2 Map of Senegal and farming systems zones in
Lower Casamance................................... 38

5.1 Linear response in kg/ha of variety DJ12519 to
environmental index (E ) ......................... 64

5.2 Quadratic response in kg/ha of variety IRAT112
to environmental index (EI) ...................... 64

5.3 Linear response in kg/ha of variety IRAT133 to
environmental index (EI) ......................... 64

5.4 Linear response in kg/ha of variety IKP to
environmental index (EI) ......................... 65

5.5 Quadratic response in kg/ha of variety Local to
environmental index (EI) ........................ 65

5.6 Estimated responses in kg/ha of five nappe rice
varieties to environment index (EI) for nappe
rice variety trials (1982-1985). Lower Casamance,
Senegal........................................... 65

5.7 Risk levels for five nappe rice varieties for
zones IV and Station, (Evaluation criterion
kg/ha) ......................................... 70

5.8 Risk levels for five nappe rice varieties for
zones III and V, (Evaluation criterion kg/ha).... 71

5.9 Estimated responses (height of plants in cm) of
five nappe rice varieties to environmental index
(EI) ............................................ 76

5.10 Estimated responses (plant cycle in days) of
five nappe rice varieties to environmental index

xi








(EI) ........................................... 76

5.11 Estimated responses (tillers/m2) of five nappe
rice varieties to environmental index (EI)....... 77

5.12 Risk levels for five nappe rice varieties for
zone IV and Station, (Evaluation criterion
height of plants) ................................ 79

5.13 Risk levels for five nappe rice varieties for
zones III and V, (Evaluation criterion height of
plants).......................................... 79

5.14 Risk levels for five nappe rice varieties for
zone IV and Station, (Evaluation criterion plant
cycle)........................................... 79

5.15 Risk levels for five nappe rice varieties for
zones III and V, (Evaluation criterion plant
cycle)....... .... .................................. 80

5.16 Risk levels for five nappe rice varieties for
zone IV and Station, (Evaluation criterion
(tillers/m2) .................. ................... 80

5.17 Risk levels for five nappe rice varieties for
zones III and V, (Evaluation criterion tillers/
m2) ............................................... 80


xii














LIST OF ACRONYMS


AA: Adaptability Analysis

ARP: Agricultural Research Project

CNRA: Centre National de Recherches Agricoles
National Center for Agricultural Research

CFA: Communaut6 Financiere Africaine francs

EI: Environmental Index

FSRE: Farming Systems Research and Extension

FSSP: Farming Systems Support Project

GNP: Gross National Product

GDP: Gross Domestic Product

IRAT: Institut des Recherches Agronomiques Tropicales et
des Cultures Vivrieres.
Institute for Tropical Agronomic Food and Crop
Research.

IEMVT: Institut d'Elevage et de Medecine V6t6rinaires des
pays Tropicaux.
Institute for Tropical Livestock and Veterinary
Medecine.

IRHO: Institut de Recherches sur les Huiles et Oleagineux.
Research Institute for Oil Plants

IRCT: Institut de Recherche sur le Coton.
Research Institute for Cotton and Textile Fibers

ISRA: Institut Sen6galais de Recherches Agricoles.
Senegalese Institute for Agricultural Research

LP: Linear Program


xiii
















LNERV: Laboratoire National d' Elevage et de Recherches
V6t6rinaires.
National Laboratory for Livestock and Veterinary
Research.

MDR: Ministrre du Developpement Rural.
Minister of Rural Development

MSU: Michigan State University.

NGO: Non-Governmental Organization.

ORSTOM: Office de la Recherche Scientifique et Technique d'
Outre-mer.
Overseas Scientific and Technical Research Office.

OMVS: Organisation pour la Mise en Valeur du Fleuve
Senegal.
Organization for Development of River Senegal.

PAPEM: Point d'Appui et d' Experimentation Multilocale.
Off-station Sites for Multilocational
Experimentation

PSR: Production Systems Research.

RD: Research and Development.

SARPP: Senegalese Agricultural Research and Planning
Project.

USAID: United States Agency for International Development.

UE: Unites Experimentales.
Experimental Units.


xiv














Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science


COMPARISON OF ON-FARM RESEARCH AND EXTENSION METHODS IN
SMALL SCALE FARM SYSTEMS IN LOWER CASAMANCE, SENEGAL

By

Mamadou LO

May, 1997

Chairperson: Dr. Mathew T. Baker
Major Department: Agricultural Education and Communication

Rainfall deficits for more than fifteen years have

turned the Lower Casamance, southern region of Senegal, from

a region of food self-sufficiency to one of food deficits.

Farmers were not widely adopting research results, despite

the contacts and opportunities provided through extension

services and research. Agro-socioeconomic surveys of the

representative small-scale farm and on-farm research trials,

conducted in Lower Casamance, in 1984 and from 1982 to 1985,

respectively, by the FSR of Djibelor were analyzed based

upon the primary data that the researcher collected in

Senegal, in Summer 1996. Research objectives were to (1)

compare the performance of new methods of analysis designed

for on-farm research and extension with existing methods in








the identification of agro-socioeconomic constraints and

analysis of improved technologies of livelihood systems; (2)

provide methods of analysis adapted to on-farm research and

extension; (3) predict response of livelihood systems to

improved technologies and give valuable feedback to

researchers, policy makers and technology change agents; and

(4) propose adapted educational programs to facilitate the

transfer and diffusion of improved technologies in different

environments. Comparison of the results from the linear

programming simulation with the Farmap/Brads data showed

that the LP model resembles the data very well; so it will

help to predict how proposed technologies fit farmers'

socioeconomic environments. Comparison of the results from

Adaptability Analysis (AA) with Analysis of Variance (ANOVA)

showed that Anova, because of its complexity and sensitivity

to violations of a number of statistical assumptions, is not

easily adapted to data produced under farmers' conditions,

which are characterized by a high degree of variability and

heterogeneity. The results of this research suggest that LP

and AA are more effective and efficient methods than the

previous ones (FARMAP/BRADS and ANOVA) in the effort to

predict responses of farmers to improved technologies on one

hand and, on the other hand, to identify improved

technologies and produce extension messages relevant to a

range of farmer circumstances.

xvi














CHAPTER 1
INTRODUCTION


The West African country of Senegal is primarily an

agricultural country. In 1980, agriculture accounted for 28%

of the GNP and provided employment for 80% of the

economically active population.

In recent years, cereal production in Senegal has not

been sufficient to meet consumption needs and most of the

production was generated by the small holders which

represent 60 to 70% of the farming population. The general

crisis in Senegalese agriculture during the last two decades

and in Lower Casamance in particular derives from an

insufficient adaptation of traditional farming systems to

new agroclimatic perturbations (decreased rainfall,

shortening of seasons, degradation of the edaphic milieu)

and evolution of the socioeconomic environment (population

growth, government policies, trade conditions, development

of social dynamics). This unfavorable agro-socioeconomic

context negatively influenced the production strategies of

small-scale farms and family livelihood systems.

The overriding concerns of these small-scale farms is

to sustain the home and the family rather than to produce








2

for growth and profit. The important role they play in the

economic development of the country has received ample

attention from the Senegalese Research and Extension

Institution. It has been recognized and accepted by the

government and other development agencies that agricultural

and social changes in the production systems of these farms,

based on the utilization of new or modified technologies

that meet their needs and aspirations, is an important

strategy and a challenge for the agricultural, economic and

social development of the country.

1.1 History of Agricultural Research and Extension

1.1.1 Early Models

The organizational framework for agricultural research

and development which has evolved over the past century,

into the 1970s, has worked reasonably well for the

industrialized nations. People working in agricultural

development in increasing numbers, however, are coming to

believe that this approach is not working in the developing

countries. This conclusion has prompted a search for new

agricultural R&D models specially designed to improve the

productivity and well-being of the rural majority who have

so far been by-passed. The plant breeding breakthroughs of

the "Green Revolution" of the 1960s, which produced new

high-yielding grain varieties, provided enormous advances in

food production in a number of countries. Yet as many








2

for growth and profit. The important role they play in the

economic development of the country has received ample

attention from the Senegalese Research and Extension

Institution. It has been recognized and accepted by the

government and other development agencies that agricultural

and social changes in the production systems of these farms,

based on the utilization of new or modified technologies

that meet their needs and aspirations, is an important

strategy and a challenge for the agricultural, economic and

social development of the country.

1.1 History of Agricultural Research and Extension

1.1.1 Early Models

The organizational framework for agricultural research

and development which has evolved over the past century,

into the 1970s, has worked reasonably well for the

industrialized nations. People working in agricultural

development in increasing numbers, however, are coming to

believe that this approach is not working in the developing

countries. This conclusion has prompted a search for new

agricultural R&D models specially designed to improve the

productivity and well-being of the rural majority who have

so far been by-passed. The plant breeding breakthroughs of

the "Green Revolution" of the 1960s, which produced new

high-yielding grain varieties, provided enormous advances in

food production in a number of countries. Yet as many










critics have noted, the new technologies have tended to

favor those rural producers already in relatively

advantageous positions, doing much less to improve the lot

of the rural majority. In some cases, these new technologies

have negative effects by spurring labor displacement or land

concentration. This uneven impact of new technologies has

not been a consequence simply of different sizes of

landholdings, yet on the average, larger farmers have

benefited more than smaller farmers. Without the advance of

the so-called "Green Revolution", worldwide production of

cereal grains would be far less than it is today, and the

brunt of shortfalls would surely fall on the poorest sectors

of society.

Existing agricultural R&D strategies have not given

much direct support to those farmers who struggle to survive

under poor climates, soil, and water conditions which are

much less favorable than the conditions assumed for adoption

of "Green Revolution" technologies. Moreover, the rural

majority labor and produce within systems of agricultural

production far more complex than the primitive conventional

stereotype (Harwood, 1979; Scrimshaw and Taylor, 1980).

Research and extension must go beyond dealing with one crop

at a time, and consider the pattern of the farming system as

a whole and relating that farming system to the total

economic and social environment of the rural family.










Agricultural research and development models have

mostly been created in industrialized nations and then have

been introduced into developing countries. Since the

implications of these model are profound, it is instructive

to consider two general types of models that have been

transferred. The first type, the European colonial model,

was already introduced before World War II in the African

and Asian colonies. The second type was developed after 1945

through U.S. technical assistance in Latin America and some

Middle Eastern and Asian nations.

The European model was based primarily upon large-scale

plantations devoted to production of export crops. Until

shortly before the end of the colonial period, when

agricultural research began experimentation on locally

consumed subsistence crops, the European model did not lend

itself to effective work with small farmers. Thus, the

Europeans and their African and Asian research counterparts

were in need of a new agricultural research and development

model. The structure of the European model, in its initial

conception and supporting philosophy, was distinctly

vertical. Research was carried out in the laboratories and

sent "down". Little feedback went "upwards" to the

scientists.

The U.S. model of agricultural research and extension,











with the passing of the colonial era, gained in popularity

and influence. Indeed, in the late 1940s, many U.S. experts

assumed that the transfer of their model to developing

nations could result in the increase in productivity and

farmer income that had occurred in the United States. The

Point IV program, designed to bring technological and

financial assistance to agriculture in developing nations,

carried with it the model of American "land grant"

universities linked to an extension service taking the

results of university-based research "out" to farmers. The

model was intended to be horizontal. Program planners

focused primarily on one part of the model, agricultural

extension. Until the failure of this transplant became

evident, programmers did not undertake to build into

developing countries the other components, particularly the

university and experiment-station-based research programs,

which were vital features of the US model.

1.1.2 Farming Systems Research and Extension

Still, by the early 1970s it was becoming obvious that

even technology tailored for tropical climates was not

trickling down to the small, limited resource farmers who

had less than the best physical resources and little or no

access to infrastructure such as markets and irrigation. The

new approach called "Farming Systems Research and Extension"

(FSRE), with heavy emphasis on the limited resource farmers,










was kindled. FSRE was based on a bottom-up approach rather

than top down,(Norman, 1980). Heavy emphasis was on

participation by the small-scale, limited resource farmers

in diagnosis and evaluation of potential new technologies on

their own farms. For a quarter century following World War

II, the conventional technology generation and diffusion

process was patterned on a progressive farmer strategy. This

strategy (Roling, 1988), in turn, was based on several

assumptions. First was the innovation bias (Rogers, 1983),

under which it was assumed that any innovation resulting

from the established research-extension process was good and

therefore, should be adopted. Second, it was assumed that

this kind of technology was broadly adaptable and scale

neutral, meaning, anyone who was willing could adopt. Third,

diffusion research had shown that innovations spread within

a social system from one decision making unit to the next

over time (Roling, 1988), so any introduced innovation

should spread through out the community. Fourth, it was also

assumed that early and late adopters, as well as non-

adopters were all from the same social system simply because

they lived in the same community late adopters or non-

adopters were thought to be "laggards", and not interested

in "improvement" (Rogers, 1995). It was also noticed from

feedback messages (farmers to extension to research, as well











as from farmers directly to research) that it was the

"progressive farmers" who were adopting the technology

first, if not exclusively. However, this was not a concern

because it was assumed that the good technology would

trickle down from these progressive farmers to those who

were less progressive or more conservative or risk averse.

Indeed, extension used contacts with progressive

farmers as a prime strategy. As it became obvious that these

progressive farmers were becoming wealthier and larger

relative to the other farmers in the community, this was of

little concern. The emerging change in the nature of farms

was supported by the concept that "bigger is better." Small

farmers often were considered more of a social problem than

an agricultural problem. It became obvious early in the

1970s that the "bigger is better" approach was disastrous

for developing countries where the large majority of farmers

were not being served by the progressive farmer, trickle

down strategy. In the less advantaged countries, as opposed

to the industrialized countries, little capacity was

available to employ persons forced from agriculture into

urban areas. The gains made by small farms in the Third

World with Green Revolution technology were made only by

those with the best resource base, a limited minority. The

technology did not trickle down from them to other farmers










who did not have the advantages of a better resource base.

Research on technology diffusion was able to show ex post

what characteristics were related to slow or non-adopters

(smaller farms, poorer, less education), but was unable to

provide ex ante suggestions for effective intervention

strategies (Roling, 1988). The progressive farmer strategy

coupled with the trickle down concept fails when the farm

population is not homogenous, but heterogeneous. "Laggards"

and "innovators," originally considered to be members of the

same social system simply because they lived in the same

community, region or country, are very different farmers,

with different production environments.

The strategy employed in agricultural extension also

involved the term "transfer of technology" which is

misleading because it implies that small farmers have such

inadequate knowledge about agriculture that they must depend

upon academic professionals to provide them with the

information and ideas to improve production. In many

developing countries there were many common deficiencies in

knowledge and ability on the part of the extension agents

(Chambers, 1974). The scope of the agent's responsibilities

and the number of available agents also limited more

intensive relations between change agents and farmers.

Developing countries lack the money and the










professional talent to provide a more intensive technical

assistance relationship. They have a lack of integration

among the various government agencies who have official

responsibilities for serving small farmers. Researchers look

down upon the extension agents, considering them incompetent

and poorly trained. On the other hand, extension agents are

often inclined to think that researchers are out of touch

with the practical realities of farming and are simply

pursuing esoteric projects designed to accumulate

professional prestige.

Another major factor affecting equitable agricultural

extension is the lack of agricultural credit and marketing.

As various studies have shown (Blair, 1978), credit tends to

go predominantly to the more affluent farmers. The cost of

credit for small farms is also likely to be a major problem.

In order to protect poor people from the exorbitant rates of

moneylenders, many governments have established special

credit programs for small farmers. But the cost, in terms of

time to obtain the necessary certificates and the rates of

interest, make the situation difficult for small farmers to

obtain credit. To deal with marketing problems, some

governments have established buying organizations that

guarantee purchase of farmers produce at prices designed to

provide a reasonable income. However, such government

organizations are often so inefficient that they fail to










provide real help to small farmers.

Finally there was the gender bias that has been built

into agricultural R&D organizations. In the past, all over

the world, agricultural extension has been a job almost

exclusively for men. As a result, extension services

provided for women have traditionally centered only around

the homemaking functions of women, such as cooking and

sewing (Poats et al., 1988).

While all of these factors add up to a general

explanation of the ineffectiveness of the conventional

agricultural extension system, one can gain a more

systematic picture of the problem if it is placed in the

context of the socioeconomic structure of developing

countries. In developing countries there is a gap in social

status, education, income and communication style between

peasant farmers and extension agents. Farmers are hesitant

to express their opinions and make demands upon the agent,

and this tends to lead the agent to underestimate the

intelligence and competence of peasant farmers. Without a

means to modify the extension model to suit the resources

and environments of "laggards", the conventional

agricultural research and extension models will not provide

improved technology for farmers whose resources and

environments do not reflect the highly modified conditions

found on research stations.








11

The 1970s produced a convergence of thinking, across a

range of disciplines, toward integrated systems of rural

development. Those who approach development problems from

plant, animal and soil science perspectives have come to

recognize the importance of farming systems research.

Furthermore, natural scientists have increasingly come to

recognize the importance of active participation by small

farmers if a program of agricultural research and

development is to be effective. During the same period,

social scientists have abandoned the myth of the "passive

peasant." Researchers have begun to recognize that small,

resource poor farmers reject innovations offered by

extension because they yield poor results in their

particular situation. Typically, they simply do not have the

resources that would allow them to follow the extension

recommendations. This realization has led to an increasing

interest in studies of what may be called the "social

organization of agriculture"; a field in which social

scientists must have a grasp of some of the key elements of

plant, animal and soil sciences. This understanding needs to

go far beyond that possessed by proponents of community

development. Researchers and socioeconomic planners have

come to recognize not only the need for small farmer

participation in agricultural R&D programs, but also the

importance of small farm organizations to give individual











farmers an effective voice in these programs. Both natural

and social scientists have recognized the importance of an

interdisciplinary approach to agricultural research.

In the early 1970s, the beginnings of FSRE research had

begun in the developing countries of Asia, Africa and Latin

America funded by several international institutions (CGIAR,

Ford Foundation, Rockefeller Foundation, USAID, World Bank,

etc.). These early FSRE efforts were prompted by the desire

to improve farmer participation in the agricultural R&D

process. The results of these early FSRE studies showed that

any new R&D model should include the following

characteristics (William, 1981):

1. Research must be carried out on the fields of small

farmers as well as in the agricultural experiment stations.

2. Small farmers must actively participate in the research

and extension activities carried out in their area helping

to identify problems and set criteria as well as judge

results.

3. The research program must include a major emphasis upon

cropping and farming systems, field studies and experiments.

4. The research program should involve a strong emphasis

upon interdisciplinary collaboration.

5. People with special responsibilities in extension and

local economic development should not be isolated from the


research process.








13

6. Government program designs should enhance the quality of

human resources among small farmers and build material

resources into the organizational base of the community and

be maintained under the control of community.

There are four distinct stages in adaptive technology

development (Hildebrand, 1988):

1-The Descriptive or Diagnostic Stage: The diagnostic phase

in FSRE provides researchers with the information required

for identifying farmer problems and constraints and

determining appropriate solutions. There are a number of

methods for collecting data (sondeos, Rapid Rural Appraisal,

Participative Diagnostic, etc.). Secondary data are also

very important to consider before the diagnosis. Data can be

collected and summarized from unpublished as well as

published sources. The main objective of secondary data is

to have an overview of the entire community being studied

(Hildebrand, 1979). Commonly used methods for collecting

data in FSRE are informal surveys, formal surveys,

observation, on-farm experiments, and case studies. However,

the most widely used method is the formal and informal

survey.

2-The Design or Planning Stage: In this stage, a number of

alternative intervention strategies (solutions to problems)

are identified which may be appropriate in dealing with the










constraints delineated in the descriptive or diagnostic

stage. Then, the multidisciplinary team draws the community

into the planning process first by discussing the diagnostic

report and its initial recommendations. In the discussion

with the community, suggestions are made regarding needed

modifications to the team findings and the discussion

provides the basis for planning the first field activities.

3-The Testing Stage: The purpose of FSRE is to develop new

agricultural technologies that address priority problems of

farm households. The ultimate measure of the success of a

new technology is the acceptance, adoption, and sustained

use by farmers (Hildebrand, 1988). FSRE teams use on-farm

experimentation involving active participation of farm

household members to test and adapt alternative

technologies. On-farm research is characterized by farmers'

participation on their own land. This participation varies

according to the nature of the experiments. During the

testing stage, potential recommendations derived from the

design stage are examined under actual farm conditions. This

is done to evaluate the suitability and acceptability of the

new practices in the existing farming systems.

4-The Recommendation and Dissemination Stage: Extension is

very important in this stage because successfully tested

technologies or practices developed by researchers and a










group of farmers are made available to other farmers with

similar circumstances. Farm households are divided into

different recommendation domains based on factors such as

the farmers' knowledge base, their level of resources, etc.,

which combine to determine the particular farming system of

the household. In this stage, standard extension techniques

are used to spread the news about new technologies to other

farmers in similar recommendation domains (Wotowiec et al.,

1988).

The grassroots origins of FSRE began to coalesce into a

school of thought during the late 1970s and early 1980s.

Innovative international agricultural scientists, working

independently in dispersed locations around the globe,

sensed the need for whole systems analysis for dealing with

the small scale farm populations. Peter Hildebrand, working

in Guatemala, Collinson in Tanzania, and Norman in Nigeria,

were a few of these lead innovators. Their initial successes

and alternative paradigm for dealing with this special

population was recognized by international donors like

USAID, the Ford Foundation, the Rockefeller Foundation, and

others. As a result of the initial success by innovators,

USAID sponsored a meeting in Fort Collins, Colorado, to

create a forum for discussion and to begin the

commercialization of FSRE concepts. From these meetings one








16

can see the first steps of the commercialization of FSRE by

the production of conference proceedings. These papers were

intended by their authors to serve as guidelines for

application of the newly named Farming Systems concepts and

methods (Shaner et al., 1982). In concert with the

publication of these methods, USAID began to fund projects

purporting to use the FSRE research approach. USAID, in this

case the centralized authority, initiated diffusion by

offering the incentive of funding for projects which applied

the FSRE approach. However, it was soon realized that these

projects did not integrate natural science research with

social science research, and that the farmer was not being

included in the study. Systematic findings were central to

these early studies, but these examples did not capture the

idea behind FSRE, which is the establishment of technologies

for the needs of, as perceived by, and with the cooperation

of the farmer.

As a result of the shortcomings of some early farming

systems projects, donors realized that further

commercialization was needed for the effective diffusion of

the FSRE concept and methods. Donors, in cooperation with

innovators, realized the need for people trained to conduct

research using FSRE methods. To meet this need, USAID funded

the Farming Systems Support Project (FSSP) at the University

of Florida. This massive initiative involved 21 institutions










and over 600 professionals worldwide, with the goal of

creating instructional materials and training practitioners

in the methodologies and concepts used to conduct farming

system research exercises (Hildebrand .P.E, 1996, personal

communication). The FSSP constitutes an active

commercialization and diffusion program by international

donors to transform it into new methodologies which could

then be diffused to researchers working on USAID (and other)

funded projects. The experience gained in these projects

showed the need for other methods and concepts. As a result,

livestock system analysis, gender analysis, and later

community development components were included

(Hildebrand.P.E, 1996, personal communication). The FSSP

helped to formulate a cluster of research methodologies to

apply when conducting farming systems research, development,

and extension.

By the late 1980s two very important things occurred

simultaneously. A new buzzword had entered the discourse:

sustainability. The concept of sustainable farm production

in the face of global warming, rising population, and

limited energy reserves, seemed logical and necessary. As a

result, the concept of sustainability supplanted FSRE and

was quickly adopted by funding agencies. USAID and others

switched from funding FSRE projects to those explicitly

working towards sustainable production. A sudden








18

discontinuance of explicit FSRE projects was mandated by the

donor organizations who held the purse strings. However,

the actual level of discontinuance of FSRE methodologies was

small. Since the methodologies proved useful for dealing

with the problems underlying sustainable production, FSRE

methods were highly compatible with the new buzz word.

It seems that the adoption of FSRE had reached a

critical mass near the time that sustainability became the

credo of the donor organizations. FSRE methodologies

continue to diffuse without donor incentive. FSRE

methodologies are now widely used and are understood as

important tools in many research projects which do not

explicitly claim to be FSRE projects (Okali, Sumberg &

Farrington, 1994). When lower than expected adoption rates

began to be recorded, FSRE methodologies were developed to

respond to the lack of farmer participation of the past.

FSRE efforts began to move more in the direction of

consultative participation in an effort to identify why more

widespread adoption was not taking place. Farming systems

research has begun the process of normalizing collaborative

work with farmers, from the problem identification stage

through technology evaluation with on-farm trials.

Individual farmers must repeatedly make decisions about

what commodities to produce, by what method, in which

seasonal time periods, and in what quantities. Decisions are











made subject to the prevailing farm physical and financial

constraints, and often in the face of considerable

uncertainty about the planning period. Uncertainty may arise

in forecasted yields, costs and prices for individual farm

enterprises, in enterprise requirements for fixed resources,

and in the total supplies of the fixed resources available.

1.1.3 Farm Management and Linear Programming

Traditionally, farmers have relied on experience,

intuition and comparisons with their neighbors to make

decisions. Formal techniques of budgeting and comparative

analysis were developed by farm management specialists, and

these can be useful aids for making decisions in less

complex situations or for analyzing selected decisions when

all the other farm decisions are taken as given. It is only

with the more recent advances in computers and in

mathematical programming software that satisfactory

procedures have been developed for whole-farm planning in

more complex situations. Whole-farm planning can assist

farmers in efficiently adapting to a changing economic and

technological environment. Optimization models such as

Linear Programming (LP) which adequately articulates the

goals and constraints of representative farmers can also

predict quite accurately what these farmers do. This is

particular true in more stationary situations where farmers

have time to adapt to the economic and technological










environment. It is this predictive possibility of

representative farm models that make them useful for

inclusion in agricultural sector models intended for

agregated policy analysis. Delicate technologies developed

solely under the conditions common on experiment stations

are, however, rarely transferable directly to limited-

resource farmers because they require a certain specific

growth factors in order to perform well. Robust

technologies, those capable of good, or at least adequate

results under the broader, more variable, and often

unpredictable range of conditions faced by farmers, tend to

be systematically disfavored under usual experiment station

conditions (Hildebrand & Russell, 1996).

1.1.4 Extension

On-farm research-extension, as carried out in the FSRE

approach can convey knowledge of important problems to be

addressed by on-station research. It can also attempt to

adapt the results of this necessary discipline and

commodity-based experiment station research through joint

research-extension farmer evaluation of promising

technologies under a broad range of environmental

conditions. To incorporate farmers' perspectives and

evaluations into the development of agricultural technology,

two traditional methods have been used. The first is through










farmer feedback about the technology after viewing

"demonstrations," or after some initial adoption during the

extension/diffusion process. The second is through field-day

visits by farmers to experiment station trials. Although

these two methods are useful, it must be recognized that

farmers are not likely to give pertinent opinions and

evaluations of technologies they see "demonstrated" to them

under foreign conditions, conditions almost always

substantially more favorable than their own (Hildebrand &

Russell, 1996). By conducting a considerable portion of the

development of new technologies in close collaboration with

farmers, through on-farm research-extension activities,

incorporation of farmers' own evaluation criteria of those

technologies will help to ensure relevance of the final

recommendations eventually made. To be most effective,

farmers' perspectives and judgments should be elicited

continually during their participation in all phases of the

technology development process. Such participation is

essential in the identification of priority problems and

potential solutions to those problems through both formal

and informal diagnostic efforts, in design and

implementation of on-farm trials, and in analysis and

interpretation of trial results. Collaborating farmers also

learn, through participation in trials, how best to

implement new technological options and how to modify and








22

adapt them to specific local conditions in order to get the

most out of them. On-farm researchers also learn from these

adaptation efforts how better to design future technologies

aimed at similar farmers, and also how better to interpret

and extend the results of on-farm research.

In recent years, there has arisen an increased

awareness that the ecological and economic costs of creating

and maintaining the high controlled, and homogeneous

agricultural environments will not be sustainable in the

long term. Greater attention is currently devoted to the

development of "alternative" or "sustainable" agricultural

systems. As agricultural policy makers begin to change the

incentives which have encouraged the use of technologies

broadly adapted to these superior environments, new

technologies will have to be developed to conform with the

the environments where they will used, not dominate them

(Hildebrand, 1990). This change in emphasis and the

concurrent imperative to target specific environmental

conditions will make on-farm research-extension activities

central to the development of technologies conducive to more

sustainable agricultural systems. Socioeconomic analysis of

limited-resource farms using Linear Programming (LP) and

Adaptability Analysis (AA) using environmental

characterization and evaluation criteria appropriate to

specific farmers' production circumstances, can give










valuable feedback to policy makers, researchers, extension

agents and farmers on one hand, and on the other hand, can

help in ensuring the identification of technologies

specifically adapted to the more variable environmental

conditions of these systems.

1.2 History of Research Systems in Senegal

Senegal has a rich tradition of agricultural research,

spanning over sixty years. During this period, the research

strategy has evolved in response to both political changes--

dating back to the colonial period--and the introduction of

new methodologies from abroad. From the turn of the century

until the mid-1970s, agricultural research in Senegal and

through out Francophone West Africa was implemented under

the auspices of the French overseas research institutes--

most notably IRAT (crop research), IEMVT (livestock), IRHO

(oil plants), IRCT (cotton) and ORSTOM (overseas scientific

and technical research).

In 1975, the Government of Senegal established the

Senegalese Agricultural Research Institute (ISRA) as part of

its policy to nationalize agricultural research. Under ISRA,

crop research was concentrated at the Centre National de

Recherches Agricoles (CNRA) at Bambey and carried out

through a network of regional stations. This research

focused on variety improvement and plant protection. Animal










production and veterinary research were carried out the

Laboratoire National d'Elevage et de Recherches Veterinaires

(LNERV) in Dakar and at two substations (Dahra and Kolda).

Because Senegal served as the headquarters for agricultural

research in Francophone West Africa until the 1960s, today

the country has one of the most extensive research

infrastructures in Sahelian Africa (Bingen & Faye, 1987).

Since 1975, ISRA has conducted extensive research on

groundnuts, cereals, cotton, cowpeas and soybean at

CNRA/Bambey and has generated many research results for

improving agriculture in the in the Sudano-Sahelian zone. In

spite of these successes, in the 1960s many observers

pointed out several weaknesses in the research system. For

example, crop research had few links with extension and was

concentrated on stations, with little research conducted at

the farm level. Similarly, livestock research, which focused

on veterinary research and breed improvement, neglected the

production constraints faced by pastoralists (World Bank,

1981).

In response to these criticisms, in the early 1960s,

ISRA established outreach (off-station) sites called Point

d'Appui et d'Experimentation Multilocale (PAPEM) where

researchers implemented multi-locational experimentation on

soil fertility, crop rotations and variety evaluation










(Bingen & Faye, 1987). A major objective of PAPEM was to

develop simple extension themes--mainly on fertilization and

improved varieties (millet, sorghum, groundnuts)--that

collaborating farmers could verify in their own fields; and

which would serve both demonstrations and training purposes.

In the late 1970s, ISRA realized that (1) farmers were not

widely adopting research results, despite the contacts and

opportunities provided through PAPEM, (2) it was possible to

develop coherent extension packages and improved cropping

systems models from many available research results, and (3)

it was necessary to conduct on-farm adaptative research on a

wide enough scale to take into account differences in

farmers' socio-economic constraints. In responses to these

realizations, ISRA initiated the Unites Experimentales (UE)

project in 1979 to assess the relevancy to farmers of

technologies developed on research stations. At the time,

this project represented a unique attempt in West Africa to

apply a systems approach, with a farm-level focus, to

agricultural research (Gilbert et al., 1980; Norman et al.,

1981). The UE project generated considerable knowledge about

the technical and economic feasibility of intensifying

agriculture and the process of transferring technology from

research stations to farmers (Faye, 1978; Benoit-Cattin,

1982). Researchers and extension services gained a better










understanding of the structure and functioning of the farm

family.

In 1981, the World Bank-funded Agricultural Research

Project (ARP) was initiated to decentralize agricultural

research at ISRA, develop interdisciplinary programs to

address commodity improvement constraints, and implement

production systems research (PSR) in the country's major

agro-ecological zones. Under the umbrella of the

Agricultural Research Project, USAID/Dakar subcontracted

Michigan State University (MSU) to assist ISRA in

implementing the Senegal Agricultural Research and Planning

Project (SARPP)--designed to organize and carry out

production systems research, as well as macroeconomic

research (Kamuanga et al., 1992). As part ot the

reorganization, ISRA established the Production Systems

Department in 1982, responsible for establishing five PSR

teams and managing several research support programs

bioclimatologyy, weed control, post-harvest technology and

soil fertility). By 1986 the department had three operating

production systems teams, stationed at Djibelor (Lower

Casamance), Kaolack (Peanut Basin) and St Louis (Fleuve

region).

1.3 Researchable Problem

The low adoption of improved technology by small-scale

farm systems in Lower Casamance contributes to low average










productivity, deficient agricultural policy and marketing

systems that do not meet farmers'needs and aspirations.

Traditional methods used for the analysis of these small

scale farm systems have not performed well for technology

producing research. Methods and tools more adapted for on-

farm research and extension to meet farmers' needs and

situations are needed by the national research and extension

organizations.

1.4 Hypothesis

There are methods of analysis of on-farm research and

extension developed for small scale farm systems, that if

used by researchers, extension agents and NGOs, could be of

assistance in formulating recommendations that meet farmers'

needs and situations, and provide valuable feedback to

policy makers and policy change agents.

1.5 Obiectives

1.5.1 Primary Objectives:

1- Compare the performance of new methods of analysis

designed for on-farm research and extension with the

traditional methods in the identification of agro-

socioeconomic constraints and analysis of improved

technologies of small-scale farm systems.

2- Propose adapted educational programs to facilitate

the creation, transfer, and diffusion of improved

technologies in the different agro-socioeconomic situations.










productivity, deficient agricultural policy and marketing

systems that do not meet farmers'needs and aspirations.

Traditional methods used for the analysis of these small

scale farm systems have not performed well for technology

producing research. Methods and tools more adapted for on-

farm research and extension to meet farmers' needs and

situations are needed by the national research and extension

organizations.

1.4 Hypothesis

There are methods of analysis of on-farm research and

extension developed for small scale farm systems, that if

used by researchers, extension agents and NGOs, could be of

assistance in formulating recommendations that meet farmers'

needs and situations, and provide valuable feedback to

policy makers and policy change agents.

1.5 Obiectives

1.5.1 Primary Objectives:

1- Compare the performance of new methods of analysis

designed for on-farm research and extension with the

traditional methods in the identification of agro-

socioeconomic constraints and analysis of improved

technologies of small-scale farm systems.

2- Propose adapted educational programs to facilitate

the creation, transfer, and diffusion of improved

technologies in the different agro-socioeconomic situations.










productivity, deficient agricultural policy and marketing

systems that do not meet farmers'needs and aspirations.

Traditional methods used for the analysis of these small

scale farm systems have not performed well for technology

producing research. Methods and tools more adapted for on-

farm research and extension to meet farmers' needs and

situations are needed by the national research and extension

organizations.

1.4 Hypothesis

There are methods of analysis of on-farm research and

extension developed for small scale farm systems, that if

used by researchers, extension agents and NGOs, could be of

assistance in formulating recommendations that meet farmers'

needs and situations, and provide valuable feedback to

policy makers and policy change agents.

1.5 Obiectives

1.5.1 Primary Objectives:

1- Compare the performance of new methods of analysis

designed for on-farm research and extension with the

traditional methods in the identification of agro-

socioeconomic constraints and analysis of improved

technologies of small-scale farm systems.

2- Propose adapted educational programs to facilitate

the creation, transfer, and diffusion of improved

technologies in the different agro-socioeconomic situations.










1.5.2 Secondary Obiectives:

1- Provide methods of analysis and tools adapted to on-

farm research and extension to better understand,

conceptualize, and model the nature and the complexity of

family livelihood systems.

2- Provide appropriate methods of analysis and tools to

predict responses of small-scale farm systems to improved

technologies and give valuable feedback to research, policy

makers and technology change agents.

1.6 Limitation of the Study

The study focuses mainly on the small-scale limited

resource family farms in Lower Casamance, using two

approaches: 1) Linear Progamming analysis (LP) of small farm

livelihood systems and 2) Adaptability Analysis (AA) of

on-farm Research-Extension data. The study intends to

compare previous results of socio-economic analysis realized

in 1984, and on-farm trials analyses realized in 1982-1985

by the Farming Systems Team of Djibelor (Lower Casamance).

It also intends to bring to the attention of a larger

audience the most up-to-date thinking in the use of

comprehensive methods for design, analysis and

interpretation of on-farm research and agronomic survey

results of limited resource family farms, in different

diffusion domains and different environments by

incorporating farmers' perspectives and evaluation criteria










1.5.2 Secondary Obiectives:

1- Provide methods of analysis and tools adapted to on-

farm research and extension to better understand,

conceptualize, and model the nature and the complexity of

family livelihood systems.

2- Provide appropriate methods of analysis and tools to

predict responses of small-scale farm systems to improved

technologies and give valuable feedback to research, policy

makers and technology change agents.

1.6 Limitation of the Study

The study focuses mainly on the small-scale limited

resource family farms in Lower Casamance, using two

approaches: 1) Linear Progamming analysis (LP) of small farm

livelihood systems and 2) Adaptability Analysis (AA) of

on-farm Research-Extension data. The study intends to

compare previous results of socio-economic analysis realized

in 1984, and on-farm trials analyses realized in 1982-1985

by the Farming Systems Team of Djibelor (Lower Casamance).

It also intends to bring to the attention of a larger

audience the most up-to-date thinking in the use of

comprehensive methods for design, analysis and

interpretation of on-farm research and agronomic survey

results of limited resource family farms, in different

diffusion domains and different environments by

incorporating farmers' perspectives and evaluation criteria










to make recommendation domains socially acceptable,

technically and economically feasible.

1.7 Definition of Terms

Linear Programming Analysis (LP) is a model for

determinating a profit maximizing combination of farm

enterprises (activities) that is feasible with respect to a

set of fixed farm constraints.

Adaptability Analysis (AA) is a simple and accurate

method of identifying and comparing the performance of

agricultural technologies under a wide range of specific

biophysical conditions and socioeconomic circumstances

(research domain).

Family farms are those which are first a home rather

than a business. Decisions are made from the point of view

of the home and the farm includes a wide diversity of

enterprises or activities.

Farm constraints include land, labor, capital,

management of many different types of activities,

information services, and fixed resources.

Diffusion domains are informal and naturally occurring

interpersonal communication networks for diffusion of

information on agricultural technology. Often specific to

the commodity or product involved.

Environment is the natural biophysical and










socioeconomically modified or created conditions existing

for plant or animal growth in the trial location. It

includes influences of any differences in management

practices not part of treatments.

Environmental Index (EI) is a convenient measure of the

environment at the location of the trial. For a specific

environment, it is the average response of all the

treatments for that environment, usually based on physical

yield per hectare.

Evaluation criterion is the measure or (measures) used

to compare the treatments in a trial. It can reflect a

researcher's concern (t/ha) for example, or a farmer's

concerns and/or needs (kg/kg seed, kg/CFA cash cost,

tillers/m2, among many others).

Recommendation domains are the situations for which

specific treatments or technologies will be recommended.

Extension messages can be designed differently for use in

specifically defined diffusion domains within a single

recommendation domain.

Risk is the probability (or percentage of time) that

the selected evaluation criterion, such as t/ha, will fall

below a certain level.

Trial usually refers to a set of treatments being

evaluated over a range of environments. It can also refer to








31

the set of treatments at each environment.

Diffusion is the process by which an innovation is

communicated through certain channels over time among the

members of a social system.

Technology transfer is the exchange of technical

information between R&D workers who create a technological

innovation and the users of the new idea.















CHAPTER 2
GENERAL CHARACTERISTICS OF SENEGALESE AGRICULTURE


2.1 Overview of the Agricultural Sector

The West African Country of Senegal covers 196,722

square kilometers with maximum dimensions of 700 km

east/west and 500 km north/south (Figure 1). Senegal has a

total of 2,640 km of land boundaries with the Gambia,

Guinea, Guinea-Bissau, Mali and Mauritania. The national

territory is divided into ten administrative regions, each

with three departments- which are in turn divided into

districts (arrondissements). The population is further

organized into urban communes and rural communities; there

are 314 rural communities and 33 urban communes. The climate

is tropical with two main seasons: a dry season from May to

November and a rainy season from June to October. The

country has six primary surface water systems, each with its

own potentials and problems. Potentially irrigated lands are

estimated at 240,000 hectares in the River Senegal Valley

and at 20,000-30,000 ha in Eastern Senegal and in the Upper

Casamance.

The 1994 population census counted eight million















CHAPTER 2
GENERAL CHARACTERISTICS OF SENEGALESE AGRICULTURE


2.1 Overview of the Agricultural Sector

The West African Country of Senegal covers 196,722

square kilometers with maximum dimensions of 700 km

east/west and 500 km north/south (Figure 1). Senegal has a

total of 2,640 km of land boundaries with the Gambia,

Guinea, Guinea-Bissau, Mali and Mauritania. The national

territory is divided into ten administrative regions, each

with three departments- which are in turn divided into

districts (arrondissements). The population is further

organized into urban communes and rural communities; there

are 314 rural communities and 33 urban communes. The climate

is tropical with two main seasons: a dry season from May to

November and a rainy season from June to October. The

country has six primary surface water systems, each with its

own potentials and problems. Potentially irrigated lands are

estimated at 240,000 hectares in the River Senegal Valley

and at 20,000-30,000 ha in Eastern Senegal and in the Upper

Casamance.

The 1994 population census counted eight million


































REGION DU
CAP- VERTj


LEGEND Ecelle
Limile d' Etat 0, so o
- Lmite de Region Km
.-- Limite de Deparlement


FIGURE 1: ADMINISTRATIVE MAP OF SENEGAL










people, 39% of whom live in urban areas. The population

density, estimated at 41 persons/km2, is unequally

distributed throughout the country. The population is

composed of diverse groups, the most important of which are

Wolofs, Sereres, Toucouleurs, Peuls, Diolas and Mandingues

(MDR, 1986).

Senegal, as a Sahelian country, has few resources.

Agriculture constitutes the main source of revenue of two

thirds of the Senegalese population. Because of the Sahelian

drought, cultivated areas from 1961 to 1990 did not change

and the average during this period was 2,352,000 hectares

with 52% used for cereal production. Senegalese agricultural

production is dominated by rainfed crops which represent 90%

of the the total agricultural production and 90% of cereals

produced locally (World Bank, 1991). Rice represents 3

percent; maize 4 percent; cotton 2 percent; cowpea 2

percent; groundnut 50%, and millet/sorghum 40%. Vegetable

production, fruit production and cassava occupy very limited

areas. Groundnuts and cotton are the main cash crops and

their average yields are respectively 760 kg/ha and 1201

kg/ha (World Bank, 1991). Livestock is constituted mainly of

cattle, sheep and goats. Livestock production fluctuates

between 65,000 and 75,000 tons of carcass weight (Faye &

Bingen, 1989).








35

Agricultural development today is the central point of

Senegalese policy. The 60% of Senegalese living in rural

areas have seen a decline of four to six percent per year in

real per capital cash income over the last fifteen years

(World Bank, 1991). Senegal has lost control of two of its

most basic and fragile resources: land and forest. Pasture

lands and protected forests represent 20% of the total

surface area of the country. Available arable lands are

limited and according to the "Septieme Plan" of Senegalese

development policy, they are estimated at 3.7 million

hectares that represents 19% of Senegalese surface area. As

the result of stagnant total production and a shift from

cash crops to food crops, the real market value of

agricultural output has declined by 2.5% per year since

1960. Cash income to purchase food, to fill the production

consumption gap, to purchase health and education services,

farm inputs and implements and to invest in improved

technology has declined. This means that a resource based-

development strategy is essential to obtain improvement in

the quality of life for Senegalese people. The primary

sector (crops, livestock, forestry and fishing) represents

about 20% of GDP in Senegal (World Bank, 1991), which is

extremely low for an economy with a 60% rural population. In

the Senegalese agricultural sector, it is clear that the

resources, even though marginal, can be managed more










effectively and that productivity can be increased. This

management for productivity requires that investments be

redirected to support it, that policies be modified to

encourage it, that technologies be proven to accomplish it,

and that all these be brought to farmers, local

organizations and NGOs (World Bank, 1991).

2.2 Characteristics of the Studied Area

The Lower Casamance covers an area of 7,300 km2 in

Southern Senegal from the Soungrougrou Valley to the

Atlantic Coast (Figure 2). It became the Ziguinchor

administrative region in July, 1984 and comprises the

Ziguinchor, Oussouye and Bignona Departements (MDR, 1984).

The region's subguinean climate receives a strong

maritime wind and is characterized by two seasons: a dry

season from November to May and a rainy season from June to

October, with August receiving the heaviest rainfall. The

Atlantic Ocean has a dominant influence on the hydrology of

the region because of its very low relief and the current

rainfall deficit. Salt water frequently flows as far as 220

km upstream from the mouth of the Casamance River. The

region has also an extensive network of lowland swamps which

facilitate even further penetration of the sea water.

The nature of the soils depends on their position in

the topographical sequence. On the upland, the soil is a

sandy loam with a sandy surface. Two types dominate: (1)








37

red, low base status iron soils (ferralitique) with a higher

clay content in the B-horizon and (2) ferruginous, leached

tropical beige soils found in the central well-drained

uplands (Beye, 1972; Bertrand, 1973). Along the talwegs

(inland valleys) and the river itself can be found a sandy

zone (sol gris de nappe) that is temporarily flooded during

the rainy season and is favorable to palm groves. In the

low-lying areas of the talwegs, rice is grown during the

rainy season and commercial production of vegetables is

carried out in the off-season. Saline soils are found in the

river bottomland, the lowest position on the topographical

sequence--alluvial flats (para-sulphate and acids or

sulphate soils) and the mangrove swamps (potentially acid

sulphate soils). "Mangrove" rice can be cultivated in these

soils if there is sufficient rainfall to leach salt.

The rural population in the Lower Casamance is

estimated at 262,000 (ISRA, 1985) living in 330 villages.

The population is evenly distributed and ranges from a

density of around 10 per km2 in the Northeast (Bignona

District) to 35 per km2 in the Southeast. The population is

young and fairly mobile. There are two main ethnic groups:

(1) the Diola, who constitute the majority (85%) of the

totalpopulation and who form a group which is composed of

several distinct sub-groups (Kassa, Blouf, Fogny-Kalounayes,















Lgcnd
Zone
I: Sodal organize. of labor
S MAURITANE traction; transplanted ri

SII: Social organize of labor
-t traction; upland crops it

Si: Social organize. of labor
dominant with Diolas i
\ traction; direct seeding
TAMBACOUNDA MA IV: Social oraniz. of labor
expanding use of anima
cropping

V: Social organize. of labor
S -= = animal traction: transpl



:.......... ... ......... .* ..*** ....













STENDIi MAJ.


ZONE II. ANOOR
4
TENDON' '?' 7
*--S~a. \ / ^'-OUOK


- Diola: no animal
cc dominant

- Diola no animal
npt. direct seeding of rice

- Mandinka
, others: limited animal
of rice

- Mandinka:
ls, expanding upland


- Dida: widespread
antcd rice remains impt


\.Vx"A 4--yR. .*.. K A .
< E xxMM )ax -x
K Ki K K KKN,


SENEGAL


...........


...........
-- --- ::. : ::::

i:::::

-::::


FIGURE 2: MAP OF SENEGAL AND FARMING SYSTEMS ZONES IN LOWER
CASAMANCE










Fogny-Combo) and (2) the Mandingues, a minority group (5%),

but whose cultural influence is great in the North, the

Northeast and around Ziguinchor (ISRA, 1984).

The Lower Casamance is essentially an agricultural

region and because of its relatively abundant rainfall it

plays an important role in Senegal' s agricultural

development policy. Groundnuts, rice, millet/sorghum and

maize are the main crops. Different types of livestock are

found in the Lower Casamance, such as N'dama cattle and

Guinean species of sheep, goats and pigs; however, very few

donkeys and horses exist. The cattle are not equally

distributed throughout the region: 84% of the herd is in

the Bignona District, 9% in Ziguinchor and 7% in Oussouye.

Herd organization and management varies from North to South.

Like other regions of Senegal, the Lower Casamance has

experienced and continues to experience a considerable

decline in rainfall. During the last ten years the mean has

fallen to 1,000mm-1,100mm in Ziguinchor (compared to the

normal 1,500mm). This average, however does not reflect the

significant inter-annual variations. Each of the three

districts has suffered several years of drought over the

past ten-year period--so severe as to put the agricultural

system in serious situations, decreasing the total area

cultivated and the production of all cereals.

In spite of its relatively small area and the








40

predominance of one ethnic group, the social organization of

agricultural production in Lower Casamance is quite

heterogeneous. In order to control for this heterogeneity,

the Djibelor Farming Systems divided the region into five

zones which cover relatively homogenous agricultural

situations (geographical area in which farmers confront

similar constraints, have comparable potential to produce,

and thereby constitute a group for which a common development

strategy can be devised). The three criteria used by the

Djibelor Farming Systems Team (ISRA, 1983) and derived from

the peasant farmers' method of exploiting land, to divide

the region are:

1) Division of Labor: Among the Diola in the South and the

Northwest (Figure 2), men clear and plow the fields while

the women transplant and harvest rice. The plowing and

transplanting of rice fields, the most labor intensive

activities, do not begin until mid-August, allowing farmers

sufficient time to plant their rice nurseries, household

gardens and upland fields in July. Among the Mandingues and

the Diola "mandinguise" (influenced culturally by the

Mandingues), the division of labor is more simply defined by

the topographical location of the crops: men cultivate

upland rainfed crops while women cultivate rice.










2) The Relative Proportion of Upland versus Lowland Crops:

As the rainfall levels decline from the Southeast to the

Northeast, the type of cropland gradually changes from

aquatic rice land (transplanted rice) to rainfed, upland

crops. Upland crops represent less than 50% of the total

land under production in the Southeast, but exceeds 80% of

cropland in the North.

3) The Use of Animal Traction: This criterion differentiates

one production system from another in the Lower Casamance.

More than 90% of the upland areas in the Sindian-Kalounayes

(Northeast) and 50% in the villages of Fogny-Combo (North)

are plowed with animal-drawn equipment. Animal traction is

almost unknown in the Southeast. The five existing

agricultural zones are: (1) Zone I: characterized by a

Diola-type organization without animal traction.

Transplanted rice is dominant; (2) Zone II: is a Diola-type

organization with a dominance of rainfed crops and direct

seeding of rice; (3) Zone III: is a cosmopolitan zone with

some animal traction and direct seeded rice; (4) Zone IV: is

characterized by a Mandingue-type organization with an

importance of upland crops and animal traction. Zone V has a

Diola-type organization of labor. Animal traction is

important with a significance of transplanted rice.












2.3 Description of the Representative Household

The main characteristics of the household in Loudia Ouloff

(Zone I), are summarized in Table 2.1.

Table 2.1: Description of the representative household


Farm resources
- Family farm size (ha)
- Community land (ha)
- Family labor (person-day/ha)
- Cash available (francs CFA)
- Seeds groundnut (kg)
- Seeds maize (kg)
- Seeds aquatic rice (kg)
- Seeds cowpea (kg)
- Seeds sweet potatoes (cuttings)


2
3
700
6000
40
20
100
12
100


Labor for the different activities (pers-
days/ha)
- Groundnuts 96
- Maize 94
- Aquatic rice 144
- Cowpea 30
- Sweet potatoes 8
- Palm oil 158
- Fruit production 15

Family consumption needs (kg/year)
- Groundnuts 83
- Maize 500
- Aquatic rice 602
- Cowpea 30
- Sweet potatoes 46
- Palm oil (liters) 50
- Fruit production 84

Seeds for different crops (kg/ha)
- Groundnuts 60
- Maize 15
- Aquatic rice 120
- Cowpea 10
- Sweet potatoes (cuttings) 10000


Yields of crops (kg/ha)
- Groundnuts 608
- Maize 554
- Aquatic rice 120
- Cowpea 122
- Sweet potatoes 4600
- Palm oil (liters) 120
- Fruit production 200
(Source: FSR/E, ISRA. Djibelor, 1984)














CHAPTER 3
METHOD AND MATERIALS


3.1 Population and Sample

In 1982, a map delimiting farming zones in Lower

Casamance (Senegal) was constructed by the Farming Systems

Team of Djibelor on the basis of three criteria: the sexual

division of labor, the extent to which animal traction had

been adopted, and the importance of transplanted rice versus

other cereals in the cropping systems. These criteria

resulted in five agricultural zones (Figure 2). The Team

then chose two villages in each zone in order to take in

account the variability within each zone. A random selection

of between 10 and 15 farms per village resulted in a sample

of 125 farms across ten villages. These farms were studied

from 1982 through 1985. For the purpose of this present

work, a representative farm based on the data in Table 2.1

was choose in zone I (Loudia Ouloff). These data were used

for the linear programmining analysis. For the Adaptability

Analysis (AA) of on-farm trials, the sample includes nappe

(phreatic) rice variety trials conducted from 1982 to 1985.

Data from these on-farm trials were used for the

adaptability analysis.














CHAPTER 3
METHOD AND MATERIALS


3.1 Population and Sample

In 1982, a map delimiting farming zones in Lower

Casamance (Senegal) was constructed by the Farming Systems

Team of Djibelor on the basis of three criteria: the sexual

division of labor, the extent to which animal traction had

been adopted, and the importance of transplanted rice versus

other cereals in the cropping systems. These criteria

resulted in five agricultural zones (Figure 2). The Team

then chose two villages in each zone in order to take in

account the variability within each zone. A random selection

of between 10 and 15 farms per village resulted in a sample

of 125 farms across ten villages. These farms were studied

from 1982 through 1985. For the purpose of this present

work, a representative farm based on the data in Table 2.1

was choose in zone I (Loudia Ouloff). These data were used

for the linear programmining analysis. For the Adaptability

Analysis (AA) of on-farm trials, the sample includes nappe

(phreatic) rice variety trials conducted from 1982 to 1985.

Data from these on-farm trials were used for the

adaptability analysis.










3.2 Data Collection and Analysis

Data to augment those from earlier stidies were

collected in Lower Casamance (Senegal) during the summer of

1996. Variables included existing data from an agronomic

survey and on-farm trials conducted by the Farming Systems

Team of Djibelor from 1982 to 1985. For the socio-economic

analysis of small limited-resource farms using the linear

programming model, data were collected from the 1984

agronomic survey realized by the Farming Systems Team of

Djibelor in Lower Casamance, Senegal. Data included

available resources of the representative farm choose in

Loudia Ouloff, zone I (Table 2.1), different characteristics

of the farm systems, systems components and interaction,

gender analysis including activities, seasonal calendars,

responsibilities and benefits, resource use and control,

farmers' criteria, etc. For the Adaptability Analysis, data

were collected from the agronomic on-farm trials conducted

by the Farming Systems Team of Djibelor from 1982 to 1985.

The data set included nappe rice variety on-farm trials. The

data set also included characteristics of the different

environments (biophysical and socio-economic factors) where

the trials took place, and farmers' own evaluation criteria.

3.3 Methods of Analysis

The methodology of analysis used includes the two

methods of analysis (Linear Programming and Adaptability










3.2 Data Collection and Analysis

Data to augment those from earlier stidies were

collected in Lower Casamance (Senegal) during the summer of

1996. Variables included existing data from an agronomic

survey and on-farm trials conducted by the Farming Systems

Team of Djibelor from 1982 to 1985. For the socio-economic

analysis of small limited-resource farms using the linear

programming model, data were collected from the 1984

agronomic survey realized by the Farming Systems Team of

Djibelor in Lower Casamance, Senegal. Data included

available resources of the representative farm choose in

Loudia Ouloff, zone I (Table 2.1), different characteristics

of the farm systems, systems components and interaction,

gender analysis including activities, seasonal calendars,

responsibilities and benefits, resource use and control,

farmers' criteria, etc. For the Adaptability Analysis, data

were collected from the agronomic on-farm trials conducted

by the Farming Systems Team of Djibelor from 1982 to 1985.

The data set included nappe rice variety on-farm trials. The

data set also included characteristics of the different

environments (biophysical and socio-economic factors) where

the trials took place, and farmers' own evaluation criteria.

3.3 Methods of Analysis

The methodology of analysis used includes the two

methods of analysis (Linear Programming and Adaptability










Analysis) developed for on-farm Research and Extension,

compared to the traditional methods (FARMAP/BRADS and

ANOVA).

1) Method of Economic Analysis of technology and policy

changes in small livelihood systems using Linear Progarmming

(LP): This method is used to conceptualize, and to model the

nature and complexity of small farm systems to predict the

response of family livelihood systems to improved

technologies and give valuable feedback to research, policy

makers, local organizations (public and private) and

technology change agents. The method includes the following:

(1) definition of basic linear programming matrix to

adequately reflect the resources, activities and

constraints, (2) definition of the family consumption

constraints and intermediate products and interactions, (3)

gender analysis and resource flow, (4) simulaton of the

representative farm systems and (5) assessing alternative

technology and policy analysis. Linear programming uses sets

of linear equations in an optimization procedure that

allocates scarce resources among competing alternatives to

maximize specified objectives. The standard form of a linear

programming model is composed of three sections: (1) the

objective function, (2) resource constraints, (3) activities

or competing alternatives. The model used in this thesis is








46

a maximization procedure whose objective function maximizes

cash income.

2) Method of Adaptability Analysis (AA) of on-farm

research and extension: Adaptability analysis is a method

used to analyze and interpret on-farm research trials, to

provide technological recommendations for specific

biophysical and socioeconomic environments and for

individual farmers' evaluation criteria. It includes the

following: (1) calculation of the environmental index (EI),

(2) identification of treatment response to environment, (3)

assessment of treatment interaction with environment, (4)

characterization of the environment, (5) interpretation of

results and definition of recommendation domains, (6)

comparison of results using alternative evaluation criteria,

and (7) creation of extension recommendations for each

recommendation domain and formulation of messages

appropriate to each of the diffusion domains. Data were

analyzed with the Quatro-pro 6.0 statistical software

program.














CHAPTER 4
LINEAR PROGRAMMING ANALYSIS



4.1 Characteristics of the Village of Loudia Ouloff

Loudia Ouloff, a village located in Zone I, south of

the River Casamance, represents the traditional Diola

system. The traditional Diola live in households (butong)

composed of conjugal units with autonomy in economic

matters. Villages are organized in groups of individual

residential units (eluf). Households follow an intensive

aquatic rice production system with a marked division of

labor by task. The heavy work of dike building and ridging

is done by men, while women transplant and harvest rice.

Land is nominally owned by patrifilial groups (Linares,

1981; Diouf, 1984), but usufruct rights to land lie with the

conjugal unit under the direct responsibility of the head of

the compound. Women, as a rule do not own land. The village

of Loudia Ouloff has a population of 306 people with 43

households (Farming Systems Team Djibelor, 1983). The

average size of the work force by household/farm is five.

The village also has 172 hectares of community land. The

average farm size is 2 to 2.5 ha.














CHAPTER 4
LINEAR PROGRAMMING ANALYSIS



4.1 Characteristics of the Village of Loudia Ouloff

Loudia Ouloff, a village located in Zone I, south of

the River Casamance, represents the traditional Diola

system. The traditional Diola live in households (butong)

composed of conjugal units with autonomy in economic

matters. Villages are organized in groups of individual

residential units (eluf). Households follow an intensive

aquatic rice production system with a marked division of

labor by task. The heavy work of dike building and ridging

is done by men, while women transplant and harvest rice.

Land is nominally owned by patrifilial groups (Linares,

1981; Diouf, 1984), but usufruct rights to land lie with the

conjugal unit under the direct responsibility of the head of

the compound. Women, as a rule do not own land. The village

of Loudia Ouloff has a population of 306 people with 43

households (Farming Systems Team Djibelor, 1983). The

average size of the work force by household/farm is five.

The village also has 172 hectares of community land. The

average farm size is 2 to 2.5 ha.










4.2 Characteristics of the Studied Household

4.2.1 Schematic Modeling of the Household

The representative household is composed of nine people

(3 men, 4 women and 2 children). It has a total of two

hectares of family land for crop production and three

hectares from the community land used for different purposes

(fruit production, firewood, palm oil collection, etc.). The

active labor force is five (Table A.1). Palm oil and fruit

production are the main sources of family revenues. The farm

also has six sheep, six goats, a family herd of 15 cattle

and some chickens. The family constitutes the main source of

labor for all activities. During peak periods (plowing,

transplanting and harvesting), the farm can hire a limited

amount of labor if cash is available. Rice production which

is never sold according to Diola tradition, remains the

staple food. Livestock is used for ceremonies and rituals

and constitutes the main source of manure for crop

production. Fuel, medicine and other family needs are

purchased from the market.

4.2.2 Gender Analysis of the Household

The household activities (Table A.2) include crop

production (rice, groundnuts, maize, sweet potatoes and

cowpea), livestock management, non-agricultural activities

(fruit picking, palm oil) and domestic activities. Both men

and women are involved in these different enterprises.










4.2 Characteristics of the Studied Household

4.2.1 Schematic Modeling of the Household

The representative household is composed of nine people

(3 men, 4 women and 2 children). It has a total of two

hectares of family land for crop production and three

hectares from the community land used for different purposes

(fruit production, firewood, palm oil collection, etc.). The

active labor force is five (Table A.1). Palm oil and fruit

production are the main sources of family revenues. The farm

also has six sheep, six goats, a family herd of 15 cattle

and some chickens. The family constitutes the main source of

labor for all activities. During peak periods (plowing,

transplanting and harvesting), the farm can hire a limited

amount of labor if cash is available. Rice production which

is never sold according to Diola tradition, remains the

staple food. Livestock is used for ceremonies and rituals

and constitutes the main source of manure for crop

production. Fuel, medicine and other family needs are

purchased from the market.

4.2.2 Gender Analysis of the Household

The household activities (Table A.2) include crop

production (rice, groundnuts, maize, sweet potatoes and

cowpea), livestock management, non-agricultural activities

(fruit picking, palm oil) and domestic activities. Both men

and women are involved in these different enterprises.










4.2 Characteristics of the Studied Household

4.2.1 Schematic Modeling of the Household

The representative household is composed of nine people

(3 men, 4 women and 2 children). It has a total of two

hectares of family land for crop production and three

hectares from the community land used for different purposes

(fruit production, firewood, palm oil collection, etc.). The

active labor force is five (Table A.1). Palm oil and fruit

production are the main sources of family revenues. The farm

also has six sheep, six goats, a family herd of 15 cattle

and some chickens. The family constitutes the main source of

labor for all activities. During peak periods (plowing,

transplanting and harvesting), the farm can hire a limited

amount of labor if cash is available. Rice production which

is never sold according to Diola tradition, remains the

staple food. Livestock is used for ceremonies and rituals

and constitutes the main source of manure for crop

production. Fuel, medicine and other family needs are

purchased from the market.

4.2.2 Gender Analysis of the Household

The household activities (Table A.2) include crop

production (rice, groundnuts, maize, sweet potatoes and

cowpea), livestock management, non-agricultural activities

(fruit picking, palm oil) and domestic activities. Both men

and women are involved in these different enterprises.










Both men and women have access to and control of the

resources (Table A.3) but land management is the

responsibility of men. Farm revenues come from selling palm

oil and fruit. The farm does not have access to chemical

fertilizers and other inputs, nor credit. Most of the

varieties used for crop production are local varieties

characterized by their long cycles (late maturing).

Both men and women have access to the farm benefits and

incentives (Table A.4). All the upland crop production is

controled mainly by men while women control the production

of lowland crops (rice). For livestock, women control only

the small ruminants (goats, sheep). Women also control the

domestic household activities such as cooking, fetching

water, firewood collection, etc. Construction material and

building material are in the hands of men. Palm oil

collection is realized by men while women do the extraction

and the benefits of this activity are controlled by women.

Benefits and incentives gained from all these activities are

shared commonly, part of them are reinvested in agricultural

production or used for family needs.

For crop production, the seasonal calendar (Table A.5)

begins in May and ends in October-November, depending on the

rainfall pattern. For groundnuts, land preparation ranges

from May until the first good rain (June). It is done by

men. Land preparation takes 34 person-days and seeding 12








50

person-days per ha. Weeding of groundnuts is done generally

one time at the end of August or the beginning of September

and takes around 30 days of family labor. Harvesting, done

both by men and women takes 13 person-days per ha, while

threshing and conditioning total 7 person-days. For maize,

land preparation takes 34 person-days per ha of the family

labor; seeding, two person-days per ha; weeding and

harvesting take 33 and 25 person-days respectively. For

aquatic rice (transplanted rice), the activities begin after

the seeding of upland crops groundnutss and maize) when the

lowlands are wet and easy to plow. Clearing/nurseries and

plowing take about 50 person-days per ha of planted rice.

Transporting and transplanting of seedlings total an average

of 46 person-days of female labor. Weeding when it is done

takes around 2 person-days per ha. Harvesting rice is a slow

and difficult task due to the traditional method used

(sheaves). This activity, done only by women, takes 44

person-days while conditioning takes around two person-days

per ha. Cowpea and sweet potatoes are secondary crops in

this area and take 30 person-days and 8 person-days

respectively of the total family labor. Palm oil production

and fruit harvesting are done throughout the year but more

intensively during the dry season. They take respectively

158 person-days and 15 person-days of the family labor.

Domestic activities such as firewood collection, cooking,










fetching water, and child care are daily activities. The

farm total wet season labor is estimated at 700 person-days

(66% for the wet season and 34% for the dry season). The

male wet season labor and the female wet season labor

represent respectively 48% and 52% of the total family wet

season labor. Male dry season labor represents 46% of the

total family dry season labor and female dry season labor is

54%.

4.3 Linear Programming Analysis of the Household

4.3.1 Resources and Minimum Survival Constraints

The farm possesses two hectares of family land for crop

production to satisfy family consumption needs (Table A.6)

and can also use three hectares of the community land for

palm oil and fruit picking production, firewood, building

material, etc. Family labor remains the main source of farm

labor. The farm does not use chemical fertilizers nor

improved varieties due to the lack of credit. Aquatic rice

(transplanted rice) constitutes the main crop for the

household. Its average production is 854 kg/ha. For

groundnuts and maize the yields are 608 kg/ha and 554 kg/ha

respectively. Cowpea produces 122 kg/ha. Palm oil production

and fruit production count 120 liters and 200 kg/ha,

respectively. The farm's annual consumption needs are 602 kg

of rice, 500 kg of maize, 83 kg of groundnuts, 50 liters of

palm oil and 84 kg of fruit.










fetching water, and child care are daily activities. The

farm total wet season labor is estimated at 700 person-days

(66% for the wet season and 34% for the dry season). The

male wet season labor and the female wet season labor

represent respectively 48% and 52% of the total family wet

season labor. Male dry season labor represents 46% of the

total family dry season labor and female dry season labor is

54%.

4.3 Linear Programming Analysis of the Household

4.3.1 Resources and Minimum Survival Constraints

The farm possesses two hectares of family land for crop

production to satisfy family consumption needs (Table A.6)

and can also use three hectares of the community land for

palm oil and fruit picking production, firewood, building

material, etc. Family labor remains the main source of farm

labor. The farm does not use chemical fertilizers nor

improved varieties due to the lack of credit. Aquatic rice

(transplanted rice) constitutes the main crop for the

household. Its average production is 854 kg/ha. For

groundnuts and maize the yields are 608 kg/ha and 554 kg/ha

respectively. Cowpea produces 122 kg/ha. Palm oil production

and fruit production count 120 liters and 200 kg/ha,

respectively. The farm's annual consumption needs are 602 kg

of rice, 500 kg of maize, 83 kg of groundnuts, 50 liters of

palm oil and 84 kg of fruit.










4.3.2 Maximizing the Family Income

By maximizing the family cash income after satisfying

consumption requirements (cash for discretionary spending),

selling palm oil and fruit bring to the family a net income

of 92,945 francs CFA ($265). The farm produces 114 liters of

palm oil, 242 kg of fruit and satisfies its consumption

needs using the two hectares of the family land and the

three hectares of community land (Table A.7). The farm uses

207 person-days of the family wet season labor and 240

person-days (the limit) of the family dry season labor. The

solution is not sensitive to the palm oil price of 700

francs CFA and will not change over a price range from 214

to more than 1,200 francs. For the resources, the family

land and the community land are not limiting although all is

essentially used. Family wet season labor is not binding but

family dry season labor is binding and an additional day of

dry season labor would add 435 francs CFA ($1.24) to family

income.

4.3.3 Influence of Gender in the Household

In the previous solution, labor is not disaggrated by

gender. The particularity of Zone I, however, is the sexual

division of labor. Men and women work together but the labor

time remains different for each sex. The division of labor

depends on the task that each sex plays in the household and

this is incorporated in the next model, Table A.8. Wet










4.3.2 Maximizing the Family Income

By maximizing the family cash income after satisfying

consumption requirements (cash for discretionary spending),

selling palm oil and fruit bring to the family a net income

of 92,945 francs CFA ($265). The farm produces 114 liters of

palm oil, 242 kg of fruit and satisfies its consumption

needs using the two hectares of the family land and the

three hectares of community land (Table A.7). The farm uses

207 person-days of the family wet season labor and 240

person-days (the limit) of the family dry season labor. The

solution is not sensitive to the palm oil price of 700

francs CFA and will not change over a price range from 214

to more than 1,200 francs. For the resources, the family

land and the community land are not limiting although all is

essentially used. Family wet season labor is not binding but

family dry season labor is binding and an additional day of

dry season labor would add 435 francs CFA ($1.24) to family

income.

4.3.3 Influence of Gender in the Household

In the previous solution, labor is not disaggrated by

gender. The particularity of Zone I, however, is the sexual

division of labor. Men and women work together but the labor

time remains different for each sex. The division of labor

depends on the task that each sex plays in the household and

this is incorporated in the next model, Table A.8. Wet








53

season labor still is not binding. By disaggrated dry season

labor, less total labor is used (233 days versus 240)

because the woman's labor is a constraint. Men use 93% of

their labor during the dry season, while women use 100%. An

additional day of female dry season labor would add 729

francs CFA ($2.08) to family income.

Cultures vary as to who has the most decision making

authority between the man and the woman in the household. A

way to analyze this effect is to maximize income to each

separately and compare these solutions with the solution

that maximizes income to the family.

4.3.4 Maximizing Female Income

By maximizing female income, after the farm satisfies

all its consumption needs, using 2 hectares of the family

land and 1.99 hectare of the community land (Table A.9), the

farm also produces 111.77 liters of palm oil for selling

which brings to the female an income of 78,238 francs CFA

($224). No fruit is sold, so family income is reduced.

Female dry season labor is binding, and an additional

day of female dry season labor would add 884.21 francs CFA

($2.53) to her income.

4.3.5 Maximizing Male Income

By maximizing male income, after the family also

satisfies all its consumption needs it produces 426.94 kg of

fruit for sale (Table A.10). Labor is not a restriction but








53

season labor still is not binding. By disaggrated dry season

labor, less total labor is used (233 days versus 240)

because the woman's labor is a constraint. Men use 93% of

their labor during the dry season, while women use 100%. An

additional day of female dry season labor would add 729

francs CFA ($2.08) to family income.

Cultures vary as to who has the most decision making

authority between the man and the woman in the household. A

way to analyze this effect is to maximize income to each

separately and compare these solutions with the solution

that maximizes income to the family.

4.3.4 Maximizing Female Income

By maximizing female income, after the farm satisfies

all its consumption needs, using 2 hectares of the family

land and 1.99 hectare of the community land (Table A.9), the

farm also produces 111.77 liters of palm oil for selling

which brings to the female an income of 78,238 francs CFA

($224). No fruit is sold, so family income is reduced.

Female dry season labor is binding, and an additional

day of female dry season labor would add 884.21 francs CFA

($2.53) to her income.

4.3.5 Maximizing Male Income

By maximizing male income, after the family also

satisfies all its consumption needs it produces 426.94 kg of

fruit for sale (Table A.10). Labor is not a restriction but










all available community land is used. Selling fruit brings

to the male an income of 34,154 francs CFA ($98).

4.3.6 Proposition of Basket Making as one Alternative
Solution for the Household

Based upon linear programming, we have seen that the

farm has limited resources but earns substantial revenues

from non-agricultural activities such as fruit production

and palm oil collection. Increased production is limited due

to the lack of land; labor also constitutes a constraint to

increased production of palm oil during the dry season. As

an alternative solution to increase family income, basket

making can be an alternative for the farm because of the

availability of the raw material and the prices offered in

the market (500 francs CFA). Basket making is a female

activity and is done during the dry season. The LP model

that best simulates the farm situation, (maximizing family

cash income) shows that female dry season labor is binding.

To achieve this activity, the farm needs to hire labor to

collect raw material and make baskets. The acceptability of

this innovation depends mainly on the availability of cash

which can be obtained easily from the revenues of palm oil

and fruit production. The results of Table A.11 show that by

hiring labor (50 person-days) at the rate of 250 francs CFA

($0.71) per person, the farm can produce 133 baskets, 252.96

kg of fruit, 107.83 liters of palm oil and satisfy easily










easily the family consumption needs. When sold, basket

making brings to the family an income of 66,500 francs CFA

($190) which increases the total family income to 143,915

francs CFA ($412).

With this scenario, community land is binding. Male wet

season labor, male dry season labor and female wet season

labor are not binding but female dry season labor still is

binding. An additional day of female dry season labor will

add 729 francs CFA ($2.08) to female income. But if more

than 3 extra days were available, female dry season labor

would no longer be limiting. A decrease by 71 days would

also bring change to the results. Availability of hired

labor is also a constraint and an additional day would add

583 francs CFA ($1.66) to family income.

4.4 Comparison of Results Analysis of Linear Programming
(LP) with Farmap/Brads Method

The FARMAP data were used to construct the Linear

Programming (LP). This comparison is a form to show how

closely we were able to simulate the representative farm

with the linear programming model.

Comparison of the linear programming model of the

representative farm in Lower Casamance with FARMAP/BRADS

data shows that the linear programming model closely

resembled the representative farm (Table 4.1). But one

disadvantage of the FARMAP/BRADS method is the fact that it










does not allow to predict how the improved technologies,

taking in account existing farmers' resources and

constraints, fit farmers' situations. Linear Programming

provides this opportunity and shows how well the introduced

technologies fit farmers' situations and how the resources

should be allocated to the different farm entreprises to

better meet farmers' needs and aspirations, allowing

researchers, extensionists, NGOs and other partners of the

development process to have valuable feedback prior to the

introduction of the technologies in the farm level. The

example used with Linear programming analysis in zone I is a

perfect illustration of the usefulness and the performance

of this method.

Introduction of basket making as an alternative

innovation in the household in Loudia Ouloff (Zone I), fits

well in the farms' environments and increases farm income to

143,915 francs CFA ($411). Linear programming facilitates (1)

a better understanding of the objectives and constraints

facing the farm family, as well as the dynamics of the

farming system,(2) a study of the factors likely to affect

wide-scale adoption of proposed improved technologies and

(3) valuable feedback to researchers, extention agents,

NGOs, local communities, decision-makers and other

partnerships of the technological development process.












Table 4.1: Farm budgets and income (francs CFA)a in the
surveyed area for a rainy year (1984), Lower Casamance,
Senegal, comparing FARMAP/BRADSc and Linear Programming (LP)
analyses.


Indicators Representative Farm LINEAR PROGRAMMING RESULTS
(FARMAP/BRADS) (Gender Analysis)

Zone I(Loudia Ouloff) Zone I(Loudia Ouloff)

Family farm area 1.89 2
(ha)

Community land 3 3
(ha)

Total labor used 141 89.6
(person-day/ha)

Value of
production 98,422 89,748
(francs CFA)

Production cost 5,907 5,966
(francs CFA)

Net farm income 92,515 83,782
(francs CFA)

Income per ha 18,919 16,756
(francs CFA)

Income per 134 187
person-day
a = US $1.00 equals 350 francs CFA in 1984
b = Average budget for representative farm
c = FARMAP/BRADS is the program used for setting and analyzing data
collected from households. It is based on the system of codes developed
by FAO and adapted to computer IBM5120. This program was used by the
Farming Systems Team (Djibelor/Lower Casamance) to analize agro-
socioeconomic survey of small households in Lower Casamance.














CHAPTER 5
ADAPTABILITY ANALYSIS


In the late 1970s, ISRA realized that (1) farmers were

not adopting research results despite the contacts and

opportunities provided through PAPEM and the "Unit6s

Experimentales" (UE), (2) it was possible to develop

coherent extension packages and improved cropping system

models from many available research results, and (3) it was

necessary to conduct on-farm adaptative research on a wide

enough scale to take in account differences in farmers'

socioeconomic constraints. During the 1970s, most farmers

acquired farm equipment, fertilizer and improved varieties

through the governmental credit program. When it was

discontinued in 1979, many farmers, particular in Lower

Casamance stopped using fertilizers due to the lack of

credit for purchasing inputs.

In response to this situation, the Farming Systems Team

of Djibelor, created in 1982, began introducing some new

technologies for main crops in Lower Casamance, to improve

farmers' agricultural productivity. The example of on-farm

trials used for this following analysis is taken from nappe

rice variety on-farm trials conducted by the Farming Systems










Team of Djibelor from 1982 to 1985.

The object of the trials was to study and evaluate

several improved nappe rice varieties. The varieties used

had already been tested on the station and in

multilocational trials. The field-size comparisons consisted

of five plots. Four were planted with the improved varieties

and one plot with the farmers' local varieties. The plots

were managed under local agronomic practices. There was only

one plot for each variety on each farm each year with a

total of 13 environments over four years. Four improved

varieties were studied: DJ12619, IRAT112, IRAT133, and IKP

(Table 5.1). All of these are short cycle varieties (90-105

days) and have good tillering capacity (average of 235

tillers/m2 on-station). Data regarding the environmental

characteristics for each site (farmer) and year were

recorded. They include plowing type (flat vs ridge), seeding

dates, rainfall pattern and soil samplings (pH and percent

of organic matter). Trials were implemented on 13 sites (12

on farm and one on station).

The results analysis of ANOVA (Table 5.1) over the four

years (1982-1985) in 13 sites (environments) shows that

variety IKP gave the best average yield (2666 kg/ha),

followed by variety DJ12519 with 2564 kg/ha and farmers'

local varieties with 2119 kg/ha. The analysis also shows

significant differences that can be attributable to the









60

separate effects of year, site and variety. Based on this

analysis of ANOVA, two improved varieties would be

recommended: IKP and DJ12519 and the indigenous local

variety. But the recommendations to be more efficient should

take in account the environmental characteristics of each

site.

Table 5.1: ANOVA for yields of nappe rice variety trials
(kg/ha). Lower casamance. Senegal (Source: FSR/E.
ISRA/Djibelor, Annual report 1985/1986).


Villages DJ1251 IRAT112IRAT133 IKP LOCAL MEAN Fcal. CV%

/year

Maoua/82 1383 0 0 1633 1766 956 NS 22

Maoua/83 1750 616 1067 1408 1350 1238 NS 26

Maoua/84 767 317 908 1017 433 688 14.3 (b)

Boul./82 3466 1700 2233 2933 2200 2506 9.3 (a)

Boul./83 3366 433 3000 2633 2600 2406 24.5 (b)

Boul./84 4433 3766 3366 4516 2516 3719 21.7 (b)

Medeg/82 2366 0 2171 2900 2200 1927 NS 26

Medeg/83 3883 2055 2883 3533 3083 3087 24.4 (b)

Medeg/84 4017 2183 3100 4833 2916 3409 6.9 (a)

Bandj/84 1500 1126 1750 2034 1750 1632 NS 30

Bandj/85 1250 1750 1250 2250 2416 1783 5.1 (a)

Suel/84 1111 740 842 1379 1425 1099 NS 28

Stat./84 4039 2357 2363 3595 2892 3049 12.6 (a)

Mean 2564 1311 1918 2666 2119 2116
aSignificant at 5%; bSignificant at 1%

5.1 Calculatina the Environmental Index (EI)

The range of environments (Els) calculated for each


site/year is presented in Table 5.2.











Table 5.2: Response of four improved nappe rice varieties
(kg/ha) and the farmers' local varieties from on-farm
research results (1982-1985). Lower Casamance, Senegal.

Farmer/year Yields (kg/ha) of varieties sorted by El

DJ12519 IRAT112 IRAT133 IKP LOCAL El

6/84 4433 3766 3366 4516 2516 3719

9/84 4017 2183 3100 4833 2916 3410

8/83 3883 2055 2883 3533 3083 3087

13/84 4039 2357 2363 3595 2892 3049

4/82 3466 1700 2233 2933 2200 2506

5/83 3366 433 3000 2633 2600 2406

7/82 2366 0 2171 2900 2200 1927

11/85 1250 1750 1250 2250 2416 1783

10/84 1500 1126 1750 2034 1750 1632

2/83 1750 616 1067 1408 1350 1238

12/84 1111 740 842 1379 1425 1099

1/82 1383 0 0 1633 1766 956

3/84 767 317 908 1017 433 688


Col. AVG 2563.92 1311.00 1917.92 2666.46 2119.0 2116



The overall mean is 2116 and the range of Els is 3031

so the ratio of range to mean is 1.4. This is an acceptable

ratio that indicates a wide range of environments has been

sampled. The other two conditions to indicate high quality

data (Hildebrand & Russell, 1996) generally are also met. It

appears that the yield of the local varieties are generally

quite high, but the minimum is 433 kg/ha. The distribution

of Els is acceptable. The best varieties, based on yield










(kg/ha), are IKP with an average of 2666kg/ha, followed by

DJ12519 with 2564 kg/ha and farmers' local varieties with

2119 kg/ha.

5.2 Relating Treatment Response to Environments on El

Treatment (variety) response to environment, by regression

on environmental index (EI), is presented in Table 5.3. From

this table, it was concluded that data for varieties

DJ12519, IRAT133 and IKP can be represented by linear

regressions because R2 of the linear regression of each of

these varieties is similar to their respective R2 quadratic

regressions. For varieties IRAT112 and local varieties,

their respective quadratic regressions better fit the data

because their respective R2 (0.75 and 0.85) are greater than

R2 of their linear regressions. The yields and regressions,

plotted on the environmental index (EI), are presented

respectively in Figures 5.1 to 5.5. Figure 5.6 presents the

estimated responses of all varieties on environmental index

(EI), using kg/ha as the selection criterion. In good

environments (Els > 2000 kg/ha), the best varieties are IKP,

and DJ1519. In poorer environments, the farmers' local

varieties join the other two as superior.

Based on the criterion kg/ha, we can tentatively

recommend varieties IKP and DJ12519 for good environments

(Els > 2000 kg) and varieties IKP, DJ12519 and farmers'

varieties for poor environments (Els < 2000 kg).













Table 5.3: Nappe rice variety trials regression estimates.


DJ12519 lin


Regression Output DJ12519 q


Constant -153.132
Std Err of Y Est 393.7941
R Squared 0.91981
No. of Observations 13
Degrees of Freedom 11
X Coefficient(s) 1.284258
Std Err of Coef. 0.114331


Regression Output


Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s) 1.31802
Std Err of Coef. 0.662824


-183.0222
412.9594
0.9198319
13
10
-7.709E-05
0.0001488


IRAT112 lin


Regression Output


Constant -647.2731
Std Err of Y Est 650.5132
R Squared 0.685882
No. of Observations 13
Degrees of Freedom 11
X Coefficient(s) 0.9256079
Std Err of Coef .0.188655


IRAT133 lin


Regression Output


Constant -115.755
Std Err of Y Est 431.7577
R Squared 0.842413
No. of Observations 13
Degrees of Freedom 11
X Coefficient(s) 0.961249
Std Err of Coef 0.125354

IKP lin Regression Output

Constant 180.7105
Std Err of Y Est 313.50131
R Squared 0.938073
No. of Observations 13
Degrees of Freedom 11
X Coefficient(s) 1.174929
Std Err of Coef 0.09102


Local lin


Regression Output


Constant 735.4496
Std Err of Y Est 399.4582
R Squared 0.742962
No. of Observations 13
Degrees of Freedom 11
X Coefficient(s) 0.6539564
Std Err of Coef 0.1159759


IRAT112 q Regression Output

Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s) -0.66275
Std Err of Coef. 0.968691

IRAT133 q Regression Output

Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s) 1.561298
Std Err of Coef. 0.700746

IKP q Regression Output


Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s) 0.748715
Std Err of Coef. 0.509648

Local q Regression Output

Constant
Std Err of Y Est
R Squared
No. of Observations
Degrees of Freedom
X Coefficient(s) 2.034722
Std Err of Coef. 0.50506


758.9227
603.534
0.754203
13
10
0.000363
0.000218



-646.985
436.5858
0.853518
13
10
-0.00014
0.000157


558.04262
317.52598
0.9422482
13
10
0.9732E-05
0.0001144



-486.958
314.6677
0.855001
13
10
-0.00032
0.000113






































Figure 5.1: Linear response in kg/ha of variety DJ12519 to
environmental index (EI).

RESEARCHER S CRITERION
NAPPE RICE VARIETY LTRLS, 982- 985


4000 *






I I _1 H IRAT112q

1000 =----^ ^-- n


500 1000 1500 2000 2500 3000 3500 4000
ENVIRONMENTAL INDEX (El)


Figure 5.2: Quadratic response in
to environmentalal index (EI).


kg/ha of variety IRAT112


Figure 5.3: Linear response in kg/ha of variety IRAT133 to
environmental index (EI).


RESEARCHER' S CRITERION
NAPPE RICE VARIETY TRILS.1982-1995



4000C


I I obs

5 DJ12519 in





500 1000 1500 2000 2500 3000 3500 4000
ENVIRONMENTAL INDEX (El)


RESEARCHER' S CRITERION
NPPE RICE VARIETY TRLS, 1980-1985



4WD


obs

S2 000o- I I J I L IRAT1331in





00 1000 1500 2000 2500 3000 3500 4000
ENVIRONMENTAL INDEX (El)








































Figure 5.4: Linear response in kg/ha of variety IKP to

environmental index (EI).


RESEARCHER' S CRITERION
NAPPE RICE VARIETY 0TF5S. 1092-1995




0 -


obs

2 -- LOCALq

5000 -


0 .
65 1000 00 I 0 0 50 30m 3500 4000
ENVIRONMENTAL INDEX (El)





Figure 5.5: Quadratic response in kg/ha of variety Local to
environment index (EI).


RESEARCHER' S CRITERION
NAPPERICEVARIETY T S. 182-1985

5000 -

DJ-12-519

SIRAT112

IRAT133

0 0M IKP


oo. LOCAL

El
500 1000 1500 2000 2500 3000 3500 4000
ENVIRONMENTAL INDEX (El)




Figure 5.6: Estimated responses in kg/ha of five rice

varieties to environment (EI) for nappe rice variety trials

(1982-1985). Lower Casamance, Senegal.


RESEARCHER' S CRITERION
WNPPE RICE VARIETY TR .S. 199821985




*a


m obs

S 1- -IKP lin


1000 -



50 00 0 1500 2000 2500 3000 3500 4000
ENVIRONMENTAL INDEX (El)










5.3 Relationship between El and Environmental
Characteristics

Characterization of the different environments usually

can help better understand the existing differences in

yields of the different varieties and to see how the

specific environmental characteristics are related to Els.

The environmental characteristics available include the

different existing plowing type (flat vs ridge), soil

samplings for pH and organic matter, number of days between

seeding and weeding, the rainfall pattern and the

agricultural zones. Data characterizing the environments are

presented in Table 5.4. The data are sorted by EI.

On this case, it is evident that none of the measured

data nor plowing practices are related to EI. However, it is

clear that the best environments are in zone IV and on the

experimental station, and the poorest environments are in

zones III and V. Zones III and V generally correspond to an

El below 1800. Zone IV is located in the manding area

characterized by a large use of animal traction and flat

plowing. Zones III and V are located in the cosmopolitan and

in the Fogny-Combo zones respectively. Ridge plowing is

dominant in zone V, while in zone III, the two types of

plowing (flat and ridge) are used.












Table 5.4: Enviromental characteristic data, sorted by El,


for nappe rice
Senegal.


variety trials (1982-1985). Lower Casamance.


El Zones Plowing Rain Interval pH %

type (mm) seeding/weeding organic

(days) matter

3719 IV Flat 1015 47 4.30 6.10

3410 IV Flat 766 23 3.30 4.40

3087 IV Flat 638 19 3.90 4.10

3049 STI Flat 1259 19 4.40 3.00

2506 IV Flat 843 28 4.10 5.60

2406 IV Flat 580 15 4.50 5.10

1927 IV Flat 786 18 3.30 3.38

1783 V Ridge 1159 32 4.00 3.31

1632 V Ridge 687 30 4.10 3.58

1238 III Flat 806 17 3.30 4.00

1099 V Ridge 698 28 5.10 0.13

956 III Flat 898 17 3.70 4.30

688 III Flat 1199 29 3.60 4.10
1 = station


5.4 Definition of Tentative Recommendation Domains

For the researcher' s criterion (kg/ha), the following

tentative recommendation domains presented in Table 5.7 can


delineated.











Table 5.5: Tentative recommendation domains and the
technologies recommended based on environmental
characteristics (zones) and the evaluation criterion kg/ha.

Environmental characteristics Recommendations

(Zones) Evaluation criterion (kg/ha)
IV and Station IKP and DJ12519
III and V IKP Local and DJ12519


5.5 Determining Risk Associated with the New Technology

The formula y- [ (t(df=n-1,p)) (Sd) /Vn] provides

information on the probability that values lie below the

confidence interval and is a measure of the level of risk

associated with the technology in these tentative

recommendation domains. Table 5.6 presents summaries of the

risk calculations with environmental characteristic based on

Zones. Figures 5.7 and 5.8 present graphs for each variety

comprised the different selected environment.

Table 5.6: Calculation of lower confidence limits (kg/ha)
for two yielding domains based on Zones for five nappe rice
variety trials (1982-1985). Lower Casamance, Senegal.

El Zones DJ12519 IRAT112 IRAT133 IKP LOCAL
3719 IV 4433 3766 3366 4516 2516 -

3410 IV 4017 2183 3100 4833 2916 -
3087 IV 3883 2055 2883 3533 3083 -
3049 STATION 4039 2357 2363 3595 2892
2506 IV 3466 1700 2233 2933 2200
2406 IV 3366 433 3000 2633 2600

1927 IV 2366 0 2171 2900 2200













Table 5.6 (continued)


El Zones DJ12519 IRAT112 IRAT133 IKP Local

1783 V 1250 1750 1250 2250 2416

1632 V 1500 1126 1750 2034 1750

1228 III 1750 616 1067 1408 1350

1099 V 1111 740 842 1379 1425

956 III 1383 0 0 1633 1766

688 III 767 317 908 1017 433


Avg of IV-Station
STDS of IV-Station
Square Root of n=7


Avg of III-V
STDS of III-V
Square Root of


3653
673
2.65


1294
338
n=6 2.45


1785
1259
2.65

758
618
2.45


2731
471
2.65

970
576
2.45


3563
839
2.65

1620
455
2.45


2630
351
2.65

1523
654
2.45


Risk analysis for Zone IV and Station (n = 7)

ALPHA PROB. t,df=6 DJ12519 IRAT112 IRAT133 IKP LOCAL

0.25 25.00 0.71 3472.29 1447.11 2604.42 3338.11 2535.37

0.20 20.00 0.91 3421.43 1351.97 2568.81 3274.68 25.8.84

0.15 15.00 1.13 3365.48 1247.32 2529.63 3204.91 2479.65

0.10 10.00 1.44 3286.64 1099.85 2474.42 3106.60 2438.52

0.05 5.00 1.94 3159.48 861.99 2385.39 2948.02 2372.18

0.03 2.50 2.45 3029.77 619.39 2294.57 2786.28 2304.51

0.01 1.00 3.14 2854.29 291.16 2171.70 2567.45 2212.96

0.01 0.50 3.71 2709.33 20.00 2070.19 2386.68 2137.34

0.00 0.05 5.96 2137.11 -1050.3 1669.52 1673.10 1838.81

Risk analysis for zones III and V (n = 6)

ALPHA PROB. t,df=5 DJ12519 IRAT112 IRAT133 IKP LOCAL

0.25 25.00 0.73 1192.75 574.01 797.92 1484.57 1328.56

0.20 20.00 0.92 1166.53 526.09 753.26 1449.28 1277.87

0.15 15.00 1.16 1133.40 465.55 696.85 1404.70 1213.83

0.10 10.00 1.48 1089.24 384.83 621.64 1345.26 1128.45

0.05 5.00 2.01 1016.09 251.13 497.07 1246.81 987.04

0.03 2.50 2.57 938.80 109.86 365.44 1142.79 837.63













Table 5.6 (continued)


ALPHA PROB t,df=5 DJ12519 IRAT112 IRAT133 IKP LOCAL

0.01 1.00 3.36 829.77 -89.42 179.76 996.04 626.85

0.01 0.50 4.03 737.30 -258.43 22.28 871.59 448.08

0.00 0.05 6.86 346.72 -972.32 -642.88 345.92 -306.99



Results of risk analysis for the two domains: high-


yielding environments (Zone IV and Station) and low-yielding


environments (Zones III and V) are presented in Figures 5.7


and 5.8 respectively. In Zone IV and on Station, varieties


DJ12519 and IKP would be recommended. In Zones III and V,

IKP would be preferred but also Local and DJ12519 would be

recommended.


Figure 5.7: Risk levels for five nappe rice varieties for
Zone IV and Station (Evaluation criterion kg/ha).


RISK ESTIMATION

4000 -

-- DJ12519

IRAT112
IRAT133
LJ
IKP

S- LOCAL

0 5 10 15 20 25
RISK (% time below Y-axis value)




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