The Impact of Agroforestry-Based Soil Fertility Replenishment Practices on the Poor in Western ...

178 Pages·2005·0.76 MB·English

The Impact of Agroforestry-Based Soil Fertility
Replenishment Practices on the Poor in Western Kenya
Frank Place
Michelle Adato
Paul Hebinck
and Mary Omosa
RESEARCH 142
REPORT
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Library of Congress Cataloging-in-Publication Data
The impact of agroforestry-based soil fertility replenishment practices
on the poor in western Kenya / Frank Place ... [et al.].
p. cm. — (Research report ; 142)
Includes bibliographical references.
ISBN 0-89629-144-8 (alk. paper)
1. Soil fertility—Kenya. 2. Agroforestry extension—Kenya. 3. Agroforestry
projects—Kenya. I. Place, Frank, Dr. II. Research report (International Food
Policy Research Institute) ; 142
S591.55.K4I47 2005
631.4′22′096762—dc22 2005013830
Contents
List of Tables iv
List of Figures vi
List of Boxes vii
Foreword viii
Acknowledgments x
Summary xi
1. Introduction 1
2. Research Methods 6
3. The Context of the Research 21
4. Poverty in Context 27
5. Household-Level Livelihood Strategies and Their Context 34
6. Processes and Patterns of Adoption 41
7. Soil Fertility Replenishment and Rural Peoples’Livelihood
in Western Kenya 61
8. Dissemination of Soil Fertility Replenishment Technologies:
Comparing Approaches, Methods, and Experiences 81
9. Human and Social Capital Formation: Dissemination within the Villages 103
10. Conclusions and Recommendations 125
Appendix A: Results and Tests from First-Stage Regressions 132
Appendix B: Six Village-Level Case Studies of Dissemination Processes 136
References 165
iii
Tables
1.1 Phase 1, Wave 1 case studies of impact of agricultural research under the
IFPRI/SPIAproject 2
2.1 Case study villages 14
2.2 Villages and dissemination approaches in the dissemination study 16
2.3 Research design matrix: Assets, vulnerability, and livelihoods 17
2.4 Research design matrix: Dissemination strategies 19
4.1 Distribution of poverty in pilot villages (n= 104) using
alternative classifications 32
4.2 Distribution of poverty in non-pilot villages (n= 360) using
alternative classifications 32
5.1 Livelihood strategies pursued by individuals in pilot villages (130 households) 35
6.1 Use of agroforestry in the pilot villages over time
(as percentage of 1,538 households) 42
6.2 Use of agroforestry in non-pilot villages over time
(as percentage of 360 households) 43
6.3 Patterns of use of improved fallows and biomass transfer in the pilot villages
(as percentage of 1,598 households) 43
6.4 Rates of use of improved fallows in early non-pilot area villages 44
6.5 Size of fallows over time in pilot villages (square meters) 45
6.6 Planting of tithoniabiomass transfer systems on farm over time in pilot
villages (as percentage of households planting) 45
6.7 Household factors related to adoption of improved fallows in pilot villages,
1997–2001 (n= 1,583) 48
6.8 Household factors related to adoption of biomass transfer in pilot villages,
1997–2001 (n= 1,583) 49
6.9 Multinomial logit results for adoption of improved fallows in non-pilot
villages (n= 361) 51
6.10 Multinomial logit results for adoption of biomass transfer in non-pilot
villages (n= 361) 52
6.11 Use rates of soil fertility management options over time in non-pilot
project areas 59
iv
TABLES v
7.1 Soil fertility practices and maize yield impacts 66
7.2 Description of household liquid assets in pilot and non-pilot villages 71
7.3 Econometric results from second-stage regression of agroforestry on changes
in assets in pilot villages (n= 97) 72
7.4 Total non-food expenditures, per capita non-food expenditures, and changes
during the three-month-long rainy season in 2000 and 2002 (in U.S. dollars) 73
7.5 Econometric results from second-stage regressions of agroforestry on
changes in non-food expenditures and per capita non-food expenditures
in pilot villages (n= 102) 74
7.6 Percentage of daily requirements of nutritional measures at the household
level prior to a long-rain harvest 75
7.7 Econometric results from second-stage regression of agroforestry use on
nutritional measurements (n= 102) 76
8.1 Village selection for dissemination study 82
8.2 Summary of reach and effectiveness of different sources of information on
SFR (as percentage of all households located in relevant villages) 93
8.3 Percentage of households receiving information on agroforestry from other
farmers or any source 94
8.4 Percentage of households with direct contact for SFR information, by source
and wealth group 96
9.1 Knowledge gain for tithoniaand improved fallows in the six study villages
from focus group discussions 119
9.2 Knowledge acquisition in agricultural topics from household survey in
non-pilot villages (n= 361) 120
A.1 OLS regression results for improved fallow area 132
A.2 Tobit regression results for improved fallow area 133
A.3 OLS regression results for number of seasons practicing biomass transfer 133
A.4 Tobit regression results for number of seasons practicing biomass transfer 134
Figures
2.1 Sustainable livelihoods framework 8
6.1 Adoption patterns of improved fallows and biomass transfer in the pilot
villages over time, 1997–2001 (as percentage of 1,630 households) 43
6.2 Adoption patterns of improved fallows and biomass transfer in the pilot
villages by 2001 (as percentage of 1,630 households) 44
8.1 Village map of institutions involved with SFR information exchange—
Bukhalalire village, poor men 83
8.2 Village map of institutions involved with SFR information exchange—
Mutsulio village, poor women 84
8.3 Village map of institutions involved with SFR information exchange—
Mutsulio village, poor men 85
8.4 Average preferences for dissemination methods used by external
organizations in six villages 100
9.1 Average assessments of relative importance of internal disseminators in poor
versus non-poor focus groups 105
9.2 Average assessments of relative importance of internal disseminators in men’s
versus women’s focus groups 106
9.3 Sauri poor women’s group: At least four years of primary education 115
9.4 Sauri non-poor women’s group education level: At least four years of
primary education 116
9.5 Mutsulio village—poor women: At least four years of primary education 117
9.6 Mutsulio village—poor men: At least four years of primary education 118
9.7 Average knowledge gain for each technology, by village 119
vi
Boxes
5.1 Relay Type of Strategies 36
5.2 Types of Farmers and Farming Systems 38
7.1 Increased Farm Yields 62
7.2 Mitigating Vulnerability 64
vii
Foreword
F
or decades, there has been significant investment in the development of agricultural
technologies that aim to increase productivity of smallholder farms in Africa. At a
macro-level, however, farm output and productivity have stagnated and poverty rates
have remained stubbornly high, even increasing in some areas.
It is widely acknowledged that policy and infrastructural constraints play a large role in
reducing incentives for farmers to invest in agriculture. Yet the fact that farmers have made
some investments and that some progress has occurred suggests that characteristics of the
technologies themselves, or the way in which they are promoted, also facilitate or inhibit wider
adoption and impact.
This research report, part of a set of studies on the impact of agricultural research on
poverty led by IFPRI, analyzes the adoption and impact of agroforestry techniques for soil fer-
tility enhancement in one of the poorest regions of the world—the western Kenyan highlands.
It further examines the role that government and nongovernmental organizations and their dif-
ferent dissemination methods play in reaching potential users and helping them understand
and use this knowledge-intensive technology. In this study, the researchers have used quanti-
tative and qualitative research methods to make discoveries and develop insights that neither
method alone could accomplish.
The authors find that improved fallows and biomass transfer systems are attractive to the
poor because they are low in cost and provide noticeable increases in crop yields. Lower and
higher income groups in the study villages use these systems in similar ways—this is not the
case for fertilizer use—but the small farm sizes of the region limit the impact of this technol-
ogy. The size of area under these systems remains small after six years of dissemination, in-
dicating that yield improvements do not translate into significant household-level welfare
impacts for the most part.
Maintaining information flows is a challenging task with such high rates of poverty and
the continuous search for livelihoods on and off farm. Persons in close contact with develop-
ment organizations increase their knowledge of soil fertility management, but villagers noted
problems with the quantity and quality of information. Different methods for disseminating
knowledge have strengths and weaknesses with respect to reaching poor farmers and women,
and this has implications for social capital.
It is challenging to reduce short-term poverty rates in highly populated areas where farm
sizes have decreased to less than one hectare. Adverse shocks are ubiquitous and they almost
always deplete household asset bases. The authors do not find any single occupation or in-
vestment that always improves welfare. Thus, the study confirms in a clear way that poverty
reduction will require sets of interventions and greater understanding of their sequencing and
integration. Low-cost methods for raising soil fertility can be coupled with other feasible en-
terprises for poor farmers, such as the use of improved maize varieties that are resistant to
streak virus and the increased planting of higher value crops like kales and climbing beans.
Greater investment in poultry or ruminants and in fruits or woodlots are also within the capacity
viii
FOREWORD ix
of poor households. The spread and speed of such investments would need to be underpinned
by increased access to capital or credit, which is very scarce in the study site.
Joachim von Braun
Director General, IFPRI
Acknowledgments
T
he authors acknowledge the central contributions of members of the research team:
Wesley Ongadi and Pamella Opiyo lived in several of the research villages for six
months, carrying out the case study research; Mary Nyasimi helped supervise the dis-
semination focus groups and designed figures of the village maps and knowledge ladders;
Maggie Lwayo, Edward Ontita, and Christopher M. C. O’Leary assisted with data analysis.
The authors also thank Anthony Bebbington, Jere Behrman, and Robert Chambers for their
many valuable contributions as members of the project’s Independent Advisory Committee.
Finally, they thank Lawrence Haddad, Ruth Meinzen-Dick, and John Pender for helpful com-
ments on an earlier draft of this report; Ginette Mignot for project administration; and Carole
Douglis and Jay Willis for editorial assistance.
Funding for the project was provided by the United Kingdom’s Department for Inter-
national Development (DID); the Standing Panel on Impact Assessment (SPIA) of the Con-
sultative Group on International Agricultural Research (CGIAR); the Australian Centre
for International Agricultural Research (ACIAR); the Danish International Development Agency
(Danida); the Government of the Netherlands; the International Fund for Agricultural Devel-
opment (IFAD); and the Food and Agriculture Organization of the United Nations (FAO).
x