• TABLE OF CONTENTS
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 Front Cover
 Title Page
 Copyright
 Table of Contents
 List of Tables
 Figures
 Maps
 Acronyms and abbreviations
 Acknowledgement
 Executive summary
 Background and objectives of the...
 The study area
 Wheat technology generation and...
 Methodology
 Demographic, socioeconomic, and...
 Adoption of improved wheat varieties...
 Factors affecting wheat technology...
 Discriminate analysis of the adoption...
 Multivariate analysis of variance...
 Conclusions and recommendation...
 Reference
 Annex 1
 Annex 2
 Annex 3






Title: Adoption of improved bread wheat varieties and inorganic fertilizer by small-scale farmers in Yelmana Densa, and Farta Districts of northwestern Ethiopia
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Permanent Link: http://ufdc.ufl.edu/UF00077525/00001
 Material Information
Title: Adoption of improved bread wheat varieties and inorganic fertilizer by small-scale farmers in Yelmana Densa, and Farta Districts of northwestern Ethiopia
Physical Description: x, 29 p. : ill., maps ; 28 cm.
Language: English
Creator: Tesfaye Zegeye
Ethiopian Agricultural Research Organization
International Maize and Wheat Improvement Center
Publisher: Ethiopian Agricultural Research Organization :
International Maize and Wheat Improvement Center
Place of Publication: Addis Ababa
Publication Date: 2001
 Subjects
Subject: Wheat -- Varieties -- Ethiopia -- Yelmana Densa District   ( lcsh )
Wheat -- Varieties -- Ethiopia -- Farta District   ( lcsh )
Fertilizers -- Ethiopia -- Yelmana Densa District   ( lcsh )
Fertilizers -- Ethiopia -- Farta District   ( lcsh )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Bibliography: Includes bibliographical references (p. 27).
Statement of Responsibility: Tesfaye Zegeye ... et al..
 Record Information
Bibliographic ID: UF00077525
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: African Studies Collections in the Department of Special Collections and Area Studies, George A. Smathers Libraries, University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 61122883
lccn - 2004414388
isbn - 9706480714

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Table of Contents
    Front Cover
        Front cover
    Title Page
        Page i
    Copyright
        Page ii
    Table of Contents
        Page iii
    List of Tables
        Page iv
    Figures
        Page iv
    Maps
        Page iv
    Acronyms and abbreviations
        Page v
    Acknowledgement
        Page vi
    Executive summary
        Page vii
        Page viii
        Page ix
        Page x
    Background and objectives of the study
        Page 1
        Page 2
        Page 3
    The study area
        Page 4
        Page 5
        Page 6
    Wheat technology generation and dissemination
        Page 7
        Page 8
    Methodology
        Page 9
        Page 10
        Page 11
        Page 12
    Demographic, socioeconomic, and institutional characteristics of wheat farmers in the study area
        Page 13
        Page 14
        Page 15
        Page 16
        Page 17
    Adoption of improved wheat varieties and chemical fertilizer
        Page 18
        Page 19
    Factors affecting wheat technology adoption
        Page 20
        Page 21
        Page 22
    Discriminate analysis of the adoption of improved wheat varieties and fertilizer
        Page 23
    Multivariate analysis of variance (MANOVA)
        Page 24
    Conclusions and recommendations
        Page 25
        Page 26
    Reference
        Page 27
    Annex 1
        Page 28
    Annex 2
        Page 29
    Annex 3
        Page 29
Full Text





Adoption of Improved Bread Wheat

Varieties and Inorganic Fertilizer by

Small-scale Farmers in Yelmana Densa

and Farta Districts of Northwestern

Ethiopia


Tesfaye Zegeye
Girma Taye
Douglas Tanner
Hugo Verkuiji
Aklilu Agidie
Wilfred Mwangi


EARO
Ethiopian Agricultural
Research Organization
(EARO)


Adet Research Center,
Amhara national
Regional State


CIMMYT
INTERNATIONAL MAIZE AND
WHEAT IMPROVEMENT CENTER


Funded by the
European Union







Adoption of Improved Bread Wheat

Varieties and Inorganic Fertilizer by

Small-scale Farmers in Yelmana Densa

and Farta Districts of Northwestern

Ethiopia





Tesfaye Zegeye
Girma Taye
Douglas Tanner
Hugo VerkuijI
Aklilu Agidie
Wilfred Mwangi





































SI M T CIMMYT (www.cimmyt.cgiar.org) is an internationally funded, nonprofit scientific research and
II 1M M T training organization. Headquartered in Mexico, the Center works with agricultural research
INTRNATIONAL MAIZE AND WHEAT IMPROVEMENT CENTER institutions worldwide to improve the productivity, profitability, and sustainability of maize and
wheat systems for poor farmers in developing countries. It is one of 16 similar centers supported by the Consultative Group on International Agricultural
Research (CGIAR, www.cqiar.org). The CGIAR comprises about 60 partner countries, international and regional organizations, and private foundations. It
is co-sponsored by the Food and Agriculture Organization (FAO) of the United Nations, the International Bank for Reconstruction and Development
(World Bank), the United Nations Development Programme (UNDP), and the United Nations Environment Programme (UNEP). Financial support for
CIMMYT's research agenda also comes from many other sources, including foundations, development banks, and public and private agencies.

F U T U R E" CIMMYT supports Future Harvest, a public awareness campaign that builds understanding about the importance of agricultural
HAR E S T issues and international agricultural research. Future Harvest links respected research institutions, influential public figures, and
leading agricultural scientists to underscore the wider social benefits of improved agriculture-peace, prosperity, environmental renewal, health, and
the alleviation of human suffering (www.futureharvest.org).

International Maize and Wheat Improvement Center (CIMMYT) 2000. All rights reserved. Responsibility for this publication rests solely with CIMMYT.
The designations employed in the presentation of material in this publication do not imply the expressions of any opinion whatsoever on the part of
CIMMYT or contributory organizations concerning the legal status of any country, territory, city, or area, or of its authorities, or concerning the
delimitation of its frontiers or boundaries. CIMMYT encourages fair use of this material. Proper citation is requested.

Printed in Mexico.

Correct citation: Tesfaye Zegeye, Girma Taye, D. Tanner, H. Verkuijl, Aklilu Agidie, and W. Mwangi. 2001. Adoption of Improved Bread Wheat
Varieties and Inorganic Fertilizer by Small-Scale Farmers in Yelmana Densa and Farta Districts of Northwestern Ethiopia. Mexico, D.F.: Ethiopia
Agricultural Research Organization (EARO) and International Maize and Wheat Improvement Center (CIMMYT).

Abstract:

ISSN:
AGROVOC descriptors:
AGRIS category codes:
Dewey decimal classification:











Contents


iv Tables
iv Figures
iv Maps
v Acronyms and Abbreviations
vi Acknowledgments
vii Executive Summary

1 1. Background and Objectives of the Study

4 2. The Study Area
4 2.1 Amhara National Regional State
5 2.2 The Study Districts

7 3. Wheat Technology Generation and Dissemination

9 4. Methodology
9 4.1 Sampling Procedure
10 4.2 Data Collection
10 4.3 Analytical Procedure

13 5. Demographic, Socioeconomic, and Institutional Characteristics of Wheat Farmers
in the Study Area
13 5.1 Demographic Characteristics
14 5.2 Socioeconomic Characteristics
16 5.3 Institutional Characteristics

18 6. Adoption of Improved Wheat Varieties and Chemical Fertilizer
19 6.1 Adoption of Improved Wheat Varieties
19 6.2 Adoption of Chemical Fertilizer

20 7. Factors Affecting Wheat Technology Adoption
20 7.1 Logistic Regression of Improved Bread Wheat Varieties
22 7.2 Logistic Regression of Chemical Fertilizer Use

23 8. Discriminate Analysis of the Adoption of Improved Wheat Varieties and Fertilizer

24 9. Multivariate Analysis of Variance (MANOVA)

25 10. Conclusions and Recommendations

27 References
28 Annex 1: Bread Wheat Varieties Tested and Released in Ethiopia since 1950
29 Annex 2: Durum Wheat Varieties Tested and Released in Ethiopia Since 1967
29 Annex 3: Bread Wheat Varieties Presently in Use in Ethiopia










Tables

2 Table 1. Area, yield, and production of wheat and all cereals, Ethiopia, 1988-98
6 Table 2. Rural and urban populations of Farta and Yelmana Densa Districts and South
Gonder and West Gojam Zones, Ethiopia
6 Table 3. Area and production of various crops in Yelmana Densa and Farta
Districts, Ethiopia
9 Table 4. Amount of improved seed quintalss) distributed in Yelmana Densa and Farta
Districts, Ethiopia, 1995-99
13 Table 5. Demographic characteristics of wheat farmers in Yelmana Densa and Farta
Districts, Ethiopia
14 Table 6. Size of land holding (eka) of wheat farmers in Yelmana Densa and Farta
Districts, Ethiopia
15 Table 7. Socioeconomic characteristics of wheat farmers in Yelmana Densa and Farta
Districts, Ethiopia
16 Table 8. Number of livestock owned by adopters and non-adopters in Yelmana Densa and
Farta Districts, Ethiopia
16 Table 9. Institutional characteristics in Yelmana Densa and Farta Districts, Ethiopia
18 Table 10. Credit availability in Yelmana Densa and Farta Districts, Ethiopia
21 Table 11. Parameter estimates from the logit model for the adoption of improved
wheat varieties
22 Table 12. Parameter estimates from the logit model for the adoption of chemical fertilizer
24 Table 13. Misclassification error rate estimates for adoption of improved wheat varieties
and chemical fertilizer




Figures


19 Figure 1. Adoption of improved bread wheat varieties in Yelmana Densa and Farta
Districts, Ethiopia
20 Figure 2. Adoption of chemical fertilizer in Yelmana Densa and Farta Districts, Ethiopia





Maps


4 Map 1. Amhara National Regional State, Ethiopia
5 Map 2. Location of the two study districts in the Amhara National Region zonal
administrative divisions, Ethiopia










Tables

2 Table 1. Area, yield, and production of wheat and all cereals, Ethiopia, 1988-98
6 Table 2. Rural and urban populations of Farta and Yelmana Densa Districts and South
Gonder and West Gojam Zones, Ethiopia
6 Table 3. Area and production of various crops in Yelmana Densa and Farta
Districts, Ethiopia
9 Table 4. Amount of improved seed quintalss) distributed in Yelmana Densa and Farta
Districts, Ethiopia, 1995-99
13 Table 5. Demographic characteristics of wheat farmers in Yelmana Densa and Farta
Districts, Ethiopia
14 Table 6. Size of land holding (eka) of wheat farmers in Yelmana Densa and Farta
Districts, Ethiopia
15 Table 7. Socioeconomic characteristics of wheat farmers in Yelmana Densa and Farta
Districts, Ethiopia
16 Table 8. Number of livestock owned by adopters and non-adopters in Yelmana Densa and
Farta Districts, Ethiopia
16 Table 9. Institutional characteristics in Yelmana Densa and Farta Districts, Ethiopia
18 Table 10. Credit availability in Yelmana Densa and Farta Districts, Ethiopia
21 Table 11. Parameter estimates from the logit model for the adoption of improved
wheat varieties
22 Table 12. Parameter estimates from the logit model for the adoption of chemical fertilizer
24 Table 13. Misclassification error rate estimates for adoption of improved wheat varieties
and chemical fertilizer




Figures


19 Figure 1. Adoption of improved bread wheat varieties in Yelmana Densa and Farta
Districts, Ethiopia
20 Figure 2. Adoption of chemical fertilizer in Yelmana Densa and Farta Districts, Ethiopia





Maps


4 Map 1. Amhara National Regional State, Ethiopia
5 Map 2. Location of the two study districts in the Amhara National Region zonal
administrative divisions, Ethiopia










Tables

2 Table 1. Area, yield, and production of wheat and all cereals, Ethiopia, 1988-98
6 Table 2. Rural and urban populations of Farta and Yelmana Densa Districts and South
Gonder and West Gojam Zones, Ethiopia
6 Table 3. Area and production of various crops in Yelmana Densa and Farta
Districts, Ethiopia
9 Table 4. Amount of improved seed quintalss) distributed in Yelmana Densa and Farta
Districts, Ethiopia, 1995-99
13 Table 5. Demographic characteristics of wheat farmers in Yelmana Densa and Farta
Districts, Ethiopia
14 Table 6. Size of land holding (eka) of wheat farmers in Yelmana Densa and Farta
Districts, Ethiopia
15 Table 7. Socioeconomic characteristics of wheat farmers in Yelmana Densa and Farta
Districts, Ethiopia
16 Table 8. Number of livestock owned by adopters and non-adopters in Yelmana Densa and
Farta Districts, Ethiopia
16 Table 9. Institutional characteristics in Yelmana Densa and Farta Districts, Ethiopia
18 Table 10. Credit availability in Yelmana Densa and Farta Districts, Ethiopia
21 Table 11. Parameter estimates from the logit model for the adoption of improved
wheat varieties
22 Table 12. Parameter estimates from the logit model for the adoption of chemical fertilizer
24 Table 13. Misclassification error rate estimates for adoption of improved wheat varieties
and chemical fertilizer




Figures


19 Figure 1. Adoption of improved bread wheat varieties in Yelmana Densa and Farta
Districts, Ethiopia
20 Figure 2. Adoption of chemical fertilizer in Yelmana Densa and Farta Districts, Ethiopia





Maps


4 Map 1. Amhara National Regional State, Ethiopia
5 Map 2. Location of the two study districts in the Amhara National Region zonal
administrative divisions, Ethiopia










Acronyms and Abbreviations



ANRS Amhara National Regional State
BOA Bureau of Agriculture
BOPED Bureau of Planning and Economic Development
CDE Center for Development and Environment (University
of Bern)
CIMMYT International Maize and Wheat Improvement Center
CSA Central Statistical Authority
DA Development Agent
DAP Diammonium phosphate
EARO Ethiopian Agricultural Research Organization
ESE Ethiopian Seed Enterprise
ETB Ethiopian Birr
GDP Gross Domestic Product
IAR Institute of Agricultural Research
masl Meters above sea level
MEDAC Ministry of Economic Development and Cooperation
MOA Ministry of Agriculture
NCRS National Crop Research Strategy
PADETES Participatory Agricultural Demonstration and Training
Extension System
PAs Peasant Associations
SG-2000 Sasakawa-Global 2000
SPSS Statistical Package for the Social Sciences
TLU Tropical Livestock Unit
UNECA United Nations Economic Commission for Africa
WRS Wheat Research Strategy










Acknowledgements



The authors are very grateful to the Yelmana Densa and Farta farmers who spared their precious
time to respond to the lengthy questionnaire; without their cooperation, this document could not
have been developed. We would also like to thank the enumerators who tolerated the hardship of
moving from one farmer's house to another.

We are also very much indebted to the Ethiopian Agricultural Research Organization (EARO) and
the Adet Research Center for their assistance and unreserved support. The Regional Bureaus of
Agriculture of Amhara, the West Gojam and South Gonder Zonal Agriculture Departments, and
Yelmana Densa and Farta District Agriculture Departments deserve special thanks for providing
background information and staff as needed.

We are very grateful to Demeke Negussie of EARO for extracting the two maps included in this
study from the CD-ROM entitled Ethio-GIS, Volume 2.

The International Maize and Wheat Improvement Center (CIMMYT) deserves special
acknowledgement for providing technical support and funds for this study through the CIMMYT/
CIDA Eastern Africa Cereals Program (EACP) and the CIMMYT/EU Project on Economics
Networking.











Executive Summary



Wheat is one of the most important cereal crops in Amhara National Regional State (ANRS) of Ethiopia,
representing a source of both food and cash. The mean area (1996-99) under wheat cultivation in the region
was 225,540 ha, constituting 10% of the total cereal area in the ANRS. The Ethiopian government aims to
increase both the extent and intensity of wheat production by expanding the area planted to the crop and
improving crop productivity. Studies to develop improved wheat varieties and cultural practices were
initiated during the 1950s and 1960s by the Institute of Agriculture (IAR) (now the Ethiopian Agricultural
Research Organization, EARO) and Alemaya University of Agriculture, with the assistance of international
research centers and foreign donors. Since the 1950s, 48 bread and 14 durum wheat varieties have been
released with their respective agronomic recommendations. Since the national extension package program
was launched in 1994, significant efforts have been made to raise the adoption of production technology
packages for wheat and other crops.

For new wheat technologies to be adopted, they must be appropriate to the biophysical and socioeconomic
conditions of the producers. It is well known that the generation and transfer of technology is not an end in
itself. The goal of increasing wheat productivity and production will be realized if and only if the ultimate
users, namely the farmers, adopt the technologies developed through research. Because the reasons for low
or no adoption of new agricultural technologies may be technical, socioeconomic, and/or institutional, it is
relevant to determine the current rate and pattern of adoption and to identify specific factors that affect or
determine adoption.

A study was initiated in two major wheat-growing districts of ANRS, Yelmana Densa and Farta, in West
Gojam and South Gonder Zones. The objectives of the study were to investigate and document adoption of
improved wheat varieties and inorganic fertilizer, to determine the factors affecting their adoption, and to
determine ways in which research, extension, and policy could improve their adoption.

Farmers were selected in a two-stage sampling procedure. First, a random sample of peasant associations
(PAs) was selected; second, a random sample of 200 farmers was drawn from the PAs using a sampling
frame developed in conjunction with development centers and/or PA offices.

Data were collected from primary and secondary sources. Secondary sources included published and
unpublished information about agricultural production in particular and the study area in general. Primary
data on farming practices during 1998 were collected from sample farmers through a structured
questionnaire during April and October of 1999 in Farta and Yelmana Densa Districts, respectively. The data
were analyzed using descriptive statistical procedures and the logistic econometric model.

The mean age of household heads for adopters of improved varieties and nonadopters was 41 and 42 years,
respectively. The mean farming experience of adopters was 20.7 years and that of nonadopters about 25.7.
The mean household size of adopters was 5.83 persons, consisting of 30% children under 8 years, 20%
children between 8 and 13 years, 26% adult males, 22% adult females, and 2% aged dependents.

The majority of the adopters of improved wheat varieties were literate: 16% attained an elementary
education, 48% attended a literacy campaign, and 3% and 8% reached junior and senior high school,
respectively. No systematic association was found, however, between the farmer's level of education and
adoption of improved wheat.










The mean size of land holding per household was 5.13 eka (one eka is equivalent to 0.25 ha), of which 75%
was cultivated, 6.6% was for grazing, 1.8% was fallow, and 16% was homestead. Nonadopters possessed
significantly more land than adopters in terms of total land holding and cultivated land. The area of land
allocated by adopters and nonadopters for wheat production is 1.33 and 0.83 eka, respectively. In terms of
area covered, tef, wheat, barley, and maize are the most important crops grown. In terms of the number of
growers (i.e., the frequency of production), barley ranked first, followed by wheat, tef, and maize.

About 77.4% of adopters and 66.7% of nonadopters reported that they faced labor constraints. To overcome
this problem, 47.3% and 26.4% of adopters and 25% and 12.5% of nonadopters use community and hired
labor, respectively, for wheat production.

Mean animal numbers per household were 3.73 cattle, 0.94 equines (i.e., mules, horses, and donkeys), 1.97
small ruminants, and 2.01 chickens. Each household also had a mean number of 0.53 beehives. Only 15% of
households owned beehives, 64% owned chickens, 18% mules, 26% horses, and 36% donkeys. On the other
hand, 22.5% of households did not own oxen, 43.5% owned one ox, 32% owned two oxen, and only 2%
owned 3 oxen; no household owned more than 3 oxen.

Access to information or extension messages was one of the institutional characteristics hypothesized to
influence a farmer's decision to adopt a new technology. One can gain access to information about new
technologies through various means, such as attending field days, visiting demonstration fields,
participating in formal training, listening to agricultural programs on the radio, and communicating in
various ways with neighbors, relatives, and community leaders. Of all these sources of information, the
main source for wheat production technologies is the extension service of the BOA at the regional, zonal,
and districts levels.

About 42.7% and 32.6% of adopters reported attending demonstrations and field days, respectively,
whereas 20.1% of adopters reported attendance at a formal training course on improved wheat production.
The chi-square analysis showed a systematic association for both participation in demonstrations and
attendance at a formal training course with the adoption of improved wheat varieties. The types of contacts
made by extension agents with farmers were identified as individual or group: 21.6% and 68.9% of adopters
were visited individually and as a group, respectively, during the survey year.

About 20.6% of adopters and 6.3% of nonadopters owned a radio. No systematic association was found,
however, between the adoption of improved wheat varieties and listening to agricultural programs on
the radio.

Access to credit was hypothesized to be a major institutional factor influencing the farmers' adoption
decisions. In the study area, 73.6% of the adopters of improved wheat varieties and 46.7% of nonadopters
reported obtaining credit from the state (i.e., the Bureau of Agriculture at all levels). The chi-square analysis
showed a systematic association between adoption of improved wheat varieties and access to credit,
indicating that farmers with access to credit are more likely to adopt improved wheat varieties than farmers
without access. The main purpose for which both categories of farmers take credit is to purchase chemical
fertilizer.

About 86% of nonadopters and 63% of adopters obtained credit from other, non-State sources, primarily
relatives and local moneylenders. The main purpose for obtaining credit through the informal sector is for
home consumption <>. It is important to










note, however, that almost 67% of nonadopters partially fulfill their fertilizer requirement by borrowing
from the informal sector. The most important credit problems cited in the study area were the
unavailability of loans from either formal or informal sources, high interest rates, and unfavorable loan
repayment terms.

The rate of adoption of improved wheat varieties increased from less than 1% in 1981 to 72% in 1998.
Adoption increased < over the six years since the national extension package
program had commenced. The preferred improved wheat varieties were ET-13, Dashen, Enkoy, HAR-
1685, and HAR-1709 (in descending order of importance). About 98% of the farmers included in the study
knew about improved wheat varieties. In addition, 98% practiced crop rotation and only 17% fallowed
their wheat fields, mainly due to a shortage of cultivated land. The major actors in the dissemination of
information on improved wheat varieties were extension agents and neighbors. Other sources of
information included relatives, researchers, traders, and producer and service cooperatives (in decreasing
order of importance).

The most important initial source of seed of improved wheat varieties in the study area was the District
Department of Agriculture. The reasons cited for adopting improved wheat varieties were many, but the
most frequently cited reason was that improved wheat varieties yielded better with fertilizer.

Ninety percent of respondents reported using chemical fertilizer at least once during their farming
experience. Chemical fertilizer had been used in the study area since 1973. The analysis indicated that the
rate of adoption for chemical fertilizer increased from less than 1% in 1976 to 77% in 1998. The adoption of
fertilizer had also increased markedly over the six years since the national extension package program
came into effect. Prior to that program, fertilizer use was at about the 5% level.

Over 93% of adopters of improved wheat varieties also used chemical fertilizer on their farms. The major
crops to which chemical fertilizer was applied for the first time were tef, wheat, and barley. During the
survey year (i.e., the 1998 cropping season), nearly 70% of adopters and 27% of nonadopters of improved
wheat varieties applied chemical fertilizer on wheat. The analysis of the relationship between the
adoption of an improved wheat variety and use of chemical fertilizer showed that the two factors are
systematically related.

The major source of chemical fertilizer reported by 40% of adopters and nonadopters was the Bureau of
Agriculture at all levels. Few respondents mentioned the Amalgamated and Ambassel companies as
sources of chemical fertilizer. About 87% and 63% of adopters and nonadopters reported obtaining
chemical fertilizer on time. The study revealed that receiving fertilizer on time is significantly associated
with the adoption of improved wheat varieties.

Each of the explanatory variables hypothesized to potentially influence adoption of improved wheat
varieties was fitted into a logistic model. Farm size influenced the adoption of improved wheat varieties
positively and significantly. Participation of farmers in on-farm demonstrations also positively and
significantly affected the adoption pattern of respondents. Attendance at training courses, access to credit,
and the farmer's educational level contributed to adoption positively, but the relationship was weak (i.e.,
not significant at the 10% level). Contacts made with extension agents, service cooperative (SC)
representatives, or PA chairmen contributed significantly and positively to adoption. Other variables such
as radio ownership contributed very little, suggesting that information about improved wheat production
technologies is more effectively diffused among farmers through other methods such as extension contact










and demonstration of an improved wheat variety. Number of livestock units, distance to a development
center, and years of farming experience did not contribute to the adoption of improved wheat varieties.

Attendance at an agricultural training course, radio ownership, membership in a producer cooperative,
farm size, total livestock units owned, and access to credit exerted a significant influence on the adoption
of chemical fertilizer. The optimal logistic model developed to explain fertilizer adoption contained only
participation in demonstrations and access to credit.

To increase the flow of information to farmers (and the adoption of new technologies), the extension
package program (PADETES) needs further strengthening. More demonstration sites for improved
technologies, including wheat varieties and fertilizer application, should be organized to increase
awareness of the new technologies among farmers in the study area. The contact between extension agents
and farmers must be strengthened by reducing the ratio of farmers to development agents. The extension
program should enhance transport facilities for development agents to increase their capacity to travel
within their mandated area. In addition, frequent training must be organized for development agents and
supervisors about existing and newly developed improved agricultural technologies and practices. This
training would bolster the agents' confidence and ability to transmit appropriate and useful information
to farmers.

Research on bread wheat has established that the improved varieties released to date are responsive to
fertilizer and that farmers obtain an economic benefit by applying fertilizer. The mean fertilizer application
rate is lower than the recommended rate, however, despite the dramatic increase in fertilizer use since the
PADETES extension program was implemented. As observed by the authors of this study, fertilizer
application is constrained by a perceived high price of fertilizer and by farmers' lack of knowledge about
how to use it. An efficient marketing system for inputs and outputs would benefit farmers by facilitating
higher prices for marketed wheat and reducing the cost of fertilizer. Since the input and output markets for
crops, including bread wheat, have been liberalized, there is a need to obtain updated information on the
economics of using improved seed and fertilizer. The government should provide the necessary support to
develop rural roads and other infrastructure such as storage facilities, which should enable inputs to be
transported to farmers more efficiently and at a lower unit transport cost.

The agricultural research system should put more emphasis on solving the problems of wheat producers
and increase the frequency with which it releases new varieties that resist diseases and pests, yield well,
and tolerate drought. To make the research effort more successful, seed of newly developed varieties must
be produced in sufficient quantities and quality for producers in the study area, the region, and the nation
at large. Steps taken by the government to establish the National Seed Industry Agency and allow the
private sector to participate in seed production, processing, and distribution are expected to increase the
volume of seed produced. However, to achieve this goal, the government must provide incentives and
support to public and private seed companies, including infrastructure and credit.

The most important credit constraints cited in the study area was the unavailability of loans from formal
and informal sources, high interest rates, and unfavorable loan repayment terms. It has been noted that
with rising input prices, improved access to credit for peasant farmers becomes indispensable. The formal
credit system must address the credit constraints of small-scale farmers and increase awareness about the
types of credit available for agricultural production. In addition, the government should encourage
farmers to form service cooperatives or farmers' groups to reduce transaction costs and improve loan
recovery rates.








Adoption of Improved Bread

Wheat Varieties and Inorganic

Fertilizer by Small-scale Farmers in

Yelmana Densa and Farta Districts of

Northwestern Ethiopia
Tesfaye Zegeye, Girma Taye, Douglas Tanner, Hugo Verkuijl,

Aklilu Agidie, and Wilfred Mwangi







1. Background and Objectives of the Study


Ethiopia has a total land area of about 111.5 million hectares (ha), of which 73.6 million (66%) are
estimated to be potentially suitable for agricultural production. Of the total land area suitable for
agriculture, 16.5 million hectares (22%) are estimated to be under cultivation, with about 14.6
million under annual crop production and the remainder under perennial crops. Of the total area
under major food crops in 1998/99, 88.7%, was under cereals, 8.7% under pulses, and 2.6% under
oilseeds.

The agricultural sector-the principal engine of growth of the Ethiopian economy-employs 85% of
the labor force, contributes about 90% of exports and 50% of gross domestic product (GDP), and
provides about 70% of the country's raw material requirement for large-and medium-scale
industries (MEDAC 1999). To exploit the potential that exists in the agricultural sector, the nation
has developed a strategy of "Agriculture Led Industrial Development" (ALID), which emphasizes
enhanced productivity in smallholder agriculture and industrialization based on the utilization of
domestic raw materials by adopting labor-intensive technologies. The agricultural component of
ALID is designed to provide commodities for export, to overcome problems of domestic food
sufficiency, to produce and supply adequate amounts of industrial raw materials, and to expand
domestic markets for goods and services produced by local industries.

The agricultural sector is the basis of domestic food production and the major sector involved in
food security. Small-scale producers operating under rainfed conditions in low-input, low-output
mixed farming systems and using traditional technologies dominate the sector. Small-scale farmers
account for 95% of the total area under crop cultivation and more than 96% of total agricultural










output. The total number of farmers involved in small-scale agricultural production is estimated at
about seven million (MEDAC 1999). The major crops grown by the small-farm sector include cereals
(tef, maize, sorghum, wheat, barley, millet, and oats), pulses (faba beans, field peas, lentils,
chickpeas, and haricot beans), and oil crops (flax and noug) (CSA 1999).

Ethiopia is the largest wheat producer in sub-Saharan Africa (Hailu 1991). The total area under both
durum and bread wheat was about 0.987 million hectares (14.6% of total cereal area) in 1998/99
(Table 1). Statistical data and wheat literature reveal that Ethiopia produced surplus wheat and also
exported wheat during the 1960s and early 1970s (EARO 2000). In terms of area and total production
on a national basis, wheat ranks fifth following tef, maize, barley and sorghum (CSA 1999).

Wheat is also one of the most important cereal crops in the Amhara National Regional State (ANRS),
where it is grown as a source of food and cash. The mean area of wheat under cultivation in the
region during 1996-99 was 225,540 ha, constituting 10% of the total cereal area.

The Ethiopian government aims to increase wheat production extensively by expanding cultivated
area and intensively by improving the productivity of the crop. Studies to develop improved wheat
varieties and cultural practices were initiated by the Institute of Agricultural Research (IAR),
presently the Ethiopian Agricultural Research Organiztion (EARO), and the Alemaya University of
Agriculture, with the assistance of international research centers and foreign donors. From the
1950s, 48 bread wheat and 14 durum wheat varieties were developed and released (Annexes 1 to 3)
with their respective agronomic recommendations. Since the national extension package program
was launched in 1994, significant efforts have been made to raise the level of adoption of technology
packages for wheat and other crops.

For new wheat technologies to be adopted, they must be appropriate to the biophysical and
socioeconomic conditions of the producers. From the relevant literature, we note that many
agricultural technologies have been developed and transferred to the farming community in various


Table 1. Area, yield, and production of wheat and all cereals, Ethiopia, 1988-98
Wheat All cereals
Percentage of
Area Percent of total Yield Production total cereal Area Yield Production
Year (000 ha) cereal area (qt/ha) (000 qt) production (000 ha) (qt/ha) (000 qt)
1988/89 647.0 13.9 13.4 799.9 14.0 4,848.5 11.6 56,859.0
1989/90 605.1 12.3 13.2 798.8 13.1 4,915.5 12.1 60,888.0
1990/91 506.6 11.8 14.0 7,112.4 12.8 4,295.2 13.1 55,779.0
1991/92 559.9 13.1 13.5 7,556.7 15.3 4,263.3 11.5 49,290.5
1992/93 555.5 14.0 15.4 8,577.3 16.7 3,954.1 13.1 51,487.7
1993/94 722.8 13.7 10.8 7,833.2 15.3 5,287.4 9.7 51,052.6
1994/95 801.1 12.4 9.1 7,270.6 12.4 6,448.6 9.1 58,484.9
1995/96 932.4 12.2 11.9 11,119.8 12.0 7,670.5 12.1 92,654.0
1996/97 819.0 11.0 12.7 10,424.8 11.1 7,436.8 12.6 93,591.5
1997/98 831.8 13.2 13.7 11,427.0 15.9 6,312.7 11.4 71,974.4
1998/99 987.1 14.6 11.3 11,137.8 14.5 6,744.7 11.4 76,829.9
Source: CSA (1990,1992,1995,1997,1998,1999).
Note: One quintal (qt) = 100 kg.










regions of the world. Only a small proportion of farmers tend to adopt all components of these
technology packages, however. Epoug (1996) indicated that only 10% of farmers in Africa had
adopted new technologies. It is well known that the generation and transfer of technologies is not an
end in itself. The goal of increasing productivity and production of wheat will be realized if and
only if the ultimate users, namely farmers, adopt the technologies that are developed by research.

The reasons for low or no adoption of new agricultural technologies can be technical,
socioeconomic, and/or institutional. It is therefore relevant to determine the current rate and pattern
of adoption of improved bread wheat varieties and fertilizer and to identify specific factors that
affect their adoption. This information should suggest interventions that may help improve the
efficiency of agricultural research and extension.

The International Maize and Wheat Improvement Center (CIMMYT) pointed out that adoption
studies can provide research and extension staff, rural development institutions, and policymakers
with valuable information to improve the efficiency of communication among them (CIMMYT
1993). Such studies can also play an important role in demonstrating the impact of research and
extension and in justifying continued support from funding sources. Additionally, adoption studies
can contribute to improving the efficiency of agricultural research, technology transfer, input
provision, and agricultural policy formulation.

With this background and rationale, the Socio-economics Division of Adet Research Center, in
collaboration with the Department of Socio-economics Research of EARO and the CIMMYT office in
Ethiopia, initiated a wheat adoption study in ANRS. The study was undertaken in West Gojam and
South Gonder Zones, specifically in Yelmana Densa and Farta Districts where the rate and pattern of
wheat technology adoption had not previously been investigated.

This report is organized in ten sections. In this first section, we provide the background, outline the
rationale and objectives of the study, and also describe very briefly the status of wheat production
and its importance in the national economy. Section two provides background information about the
study area. Section three provides details on wheat technology generation and transfer. Section four
elaborates the methodology used to execute the field study and analyze the data. Sections five to
nine present the findings of the study. Section ten discusses the conclusions and recommendations
arising from the study.

The overall objectives of the study in Yelmana Densa and Farta Districts were to investigate and
document adoption of improved wheat varieties and inorganic fertilizer, determine which factors
affected adoption, and develop recommendations for research, extension, and policy to improve
adoption in the future. Specific objectives of the study were to:

* Investigate the rate and pattern of adoption of improved wheat varieties and fertilizer (both use
and application rates);
* Examine the characteristics of technology adopting and nonadopting farmers;
* Identify the socioeconomic and institutional factors that affect the adoption of improved wheat
technologies <>; and
* Draw implications of the findings for research, extension, and policy.










2. The Study Area



2.1 Amhara National Regional State
Amhara National Regional State (ANRS) is one of the constituent states of the Federal Democratic
Republic of Ethiopia. The ANRS is located in the northwestern part of the country (Map 1) between
8045' and 13045' North latitude and 35046' and 40025' East longitude. The boundaries of the ANRS
adjoin Tigray in the north, Oromia in the south, Afar in the east, Benishangul Gumuz in the southwest,
and Sudan in the northwest. The state is divided into 11 administrative zones, including the capital city
of the region, Bahir Dar. The other 10 administrative zones are East Gojam, West Gojam, Awi, North
Gonder, South Gonder, Wag Himra, North Wollo, South Wollo, Semien Shewa, and Oromia (BOPED
1999). The region consists of 101 districts and 5,300 rural and urban associations (UNECA 1996).

The total area of the region is 170,752 km2. Topography is divided mainly into plains, mountains,
valleys, and undulating lands. The high- and mid-altitude areas (about 65% of total area) are
characterized by a chain of mountains and a central plateau. The lowland part, constituting 33% of the
total area, covers the western and eastern parts of the region; these are mainly plains that are large river
drainage basins. Of the total area of the region, 27.3% is under cultivation, 30% is under grazing and
browsing, 14.7% is covered by forest, bush and herbs, and 18.9% is currently not used for productive
purposes. The remaining 9.1% represents settlement sites, swampy areas, and lakes (UNECA 1996).

The population of the region was estimated to be 15 million in 1998/99. Of these, 90.3% live in rural
areas. Mean population density is 91 persons/km2 and ranges between 39 persons/km2 in Wag Himra
to 151 persons/km2 in West Gojam (BOPED 1999). Persons below 25 years of age form more than 65% of
the population. A large proportion of the population in ANRS depends upon crop and livestock
farming. Cropping systems are predominantly rainfed. Because of population pressure and poor land
husbandry, the level of land degradation and environmental
depletion is worsening over time. Eri

The region has fertile farmland and water resources Tigr
suitable for crop production and livestock
husbandry. High-potential areas include the Sudan Benish.
-J vSudan Bensh. \ j _un
western lowlands and the densely populated, Gumuz Amhara Region A
Afar
surplus-producing areas of Gojam and O ,my.
Gonder (UNECA, 1996). Farmers produce a Somalia
combination of cereals, pulses, and oilseeds.
Cereals account for the largest percentage of
cultivated area (84.3%) and total production
(85%). As noted, this study was undertaken in /
Yelmana Densa District of West Gojam Zone and
Farta District of South Gonder Zone.
Kenya

Map 1. Amhara National Regional State, Ethiopia.
Source: MOA and CDE (1999).










2.2 The Study Districts
The location of Yelmana Densa District in West Gojam Zone is shown in Map 2. District boundaries
are Bahir Dar in the north, East Gojam in the southeast, South Gonder in the east, Mecha in the west,
Sekela in the southwest, and Kuarit in the south. According to the Department of Agriculture of
Yelmana Densa District, the topography and terrain of the district consists of plateaus, hills, and flat
lands. Total land area of the district is estimated to be 144,707 ha, accounting for about 10.6% of the
total area of West Gojam Zone. Generally, Yelmana Densa District comprises altitudes ranging from
1,500-3,200 meters above sea level (masl). The district is classified into three traditional agroclimatic
zones: dega (high altitude) covers 24% of the area and ranges between 2,400-3,200 masl, woina dega
(mid altitude) at 1,800-2,400 masl encompasses about 57% of the area, and kolla (lowland) at 500-
1,500 masl covers 19% of the area.

Farta District contains the city of Debre Tabor; the district boundaries are Libo Kemkem and Ebinat
Districts in the north, Estie to the south, Fogera and Lai Gaint in the east, and Fogera District in the
west. Farta District comprises altitudes ranging between 1,500 and 4,135 masl. The study area
includes medium- and high-altitude areas of Farta District, lying between 1,500 and 2,800 masl.

The mean maximum temperatures in Yelmana Densa District range from 22.10C in August to 28.80C
in April. The mean minimum temperatures range from 5.20C in January to 11.60C in September. The
rainfall pattern in the study area is unimodal. According to data from the Adet Research Center
meteorological station, the mean annual rainfall ranged from 860 mm in 1986 to 1,771 in 1998: the
long-term mean annual rainfall is about 1,291 mm. Rain usually starts in March, but the effective
rainy season is from May to October with the peak in July-receiving a monthly mean of 331 mm of
rainfall. The mean seasonal rainfall during the growing period (May to October) is 1,193 mm. From
mid-October to January, dry weather prevails and extends to May.

The mean annual rainfall measured at the
Debre Tabor meteorological station (i.e., the
capital city of Farta and South Gonder) is
1,651 mm. The mean annual rainfall Wag
during the main rainy season (June to North Gnder
September) is 1,337 mm. These data
indicate that the amount and North walla
seasonal distribution of rainfall are I
sufficient for crop production. *West Goiam South Gonder
Data from the meteorology station
at Debre Tabor reveal that air South Wll
temperatures exhibit monthly
East Goj\m
mean maxima of 18.40C and
minima of 4.90C. 1. Farta Semien Shewa
2. Yelmana Densa
According to the 1994 census, the
total population of Yelmana Densa Map 2. Location of the two study districts in the Amhara
District is 275,004, or 13.8% of the National Region zonal administrative divisions, Ethiopia.
total population of West Gojam Source: MOA and CDE (1999)











(Table 2). The census reported that 14,891 persons resided in urban areas and 260,113 in rural areas
of the district (CSA 1994), and population is growing by 2.23% in rural areas and 4.11% in urban
areas. The majority of the people of the district are from the Amhara ethnic group and the dominant
religion is Ethiopian Orthodox Christian.

According to the 1994 population census, Farta District has a population of 247,101 or 12.9% of the
population of South Gonder. The rural population comprises 12.7% and 98.2% of the population of
the zone and of Farta District, respectively. The majority of the population of Farta District is
Amhara and the dominant religion is Ethiopian Orthodox Christian.

Yelmana Densa and Farta Districts comprise mixed farming zones where crops are grown for food
and cash, and animals are kept for complementary purposes and to meet farmers' cash needs. The
most important crops grown in the two districts are tef, barley, maize, wheat, sorghum and millet;
other pulse and oil crops are also grown (Table 3).


Table 2. Rural and urban populations of Farta and Yelmana Densa Districts and South Gonder and West Gojam
Zones, Ethiopia
West Gojam South Gonder Farta Yelmana Densa
No. Percent No. Percent No. Percent No. Percent
Rural
Male 939,513 50.3 904,849 51.1 124,391 51.3 130,568 47.5
Female 927,915 49.7 864,570 48.9 118,286 48.7 129,545 49.8
Subtotal 1,867,425 93.5 1,769,419 92.6 242,677 98.2 260,113 94.6
Urban
Male 62,315 48.1 68,118 48.3 2,177 49.2 6,760 45.4
Female 67,240 51.9 72,911 51.7 2,247 50.8 8,131 54.6
Subtotal 129,555 6.5 141,029 7.4 4,424 1.8 14,891 5.4
Grand total 1,996,982 1,910,448 247,101 275,004




Table 3. Area and production of various crops in Yelmana Densa and Farta Districts, Ethiopia
Yelmana Densa Farta
Percent Percent
Area Percent Production of total Yield Area Percent of Production of total Yield
Crop (ha) of area (qt) production (qt/ha) (ha) area (qt) production (qt/ha)


Cereals 41,778 73.1
Tef 17,178 30.
Barley 8,842 15.
Wheat 4,682 8.
Maize 5,478 9.
Sorghum 3,796 6.
Millet 1,802 3.
Pulses 11,618 20.
Oil crops 3,225 6.1
Source: ??????????
Note: One quintal (qt) = 100 kg.


8
3
5
2
7
7
1
5
0


592,152
188,479
139,146
74,059
115,177
49,059
26,232
100,173
18,103


83.4
26.5
19.6
10.4
16.2
6.9
3.7
14.1
2.5


14.0
11.0
15.7
15.8
21.0
12.9
14.5
8.6
5.6


55,501
8,786
24,094
17,793
1,364
978
2,486
13,177
1,934


79
12
38
25
2
1
3
18
3


476,385
47,746
209,699
176,619
16,640
9,257
16,424
84,310
7,750


83
8
36
31
3
2
3
15
1


9
5
9
10
7
12
10
6
4









Aleligne (1988) indicated that during the 1980s wheat production was scanty in Yelmana Densa and
Farta Districts and was limited to high-altitude areas. Historically, farmers in the two districts grew
a small area of local wheat varieties. An informal assessment made in the two districts in 1995
revealed that improved bread wheat varieties had mostly been introduced after 1988. Before this
time, durum wheat (as reported by 48% of the farmers in the survey sample) was the dominant
wheat crop produced in the two districts. During the intervening 12 years, many improved bread
wheat varieties have been extended in northwestern Ethiopia in general and the study area in
particular. Bread wheat varieties have been demonstrated and popularized by district agricultural
offices through package demonstrations on the farms of producer cooperatives, and by
demonstrations on the research station and farmers' fields by Adet Research Center (Aleligne 1988;
Aleligne and Regassa 1992). On-farm variety trials showed that improved bread wheat varieties
could significantly increase wheat grain yields relative to farmers' varieties (Asmare et al. 1997).
The following varieties were extended to farmers: Dashen, Enkoy, ET13, HAR-1685, HAR-1709,
HAR-604, and HAR-710. Optimal cultural practices have been recommended, including a seed rate
of 150-175 kg/ha, application of 92-46 kg/ha of N-P205, control of weeds using 2,4-D (at 1 1 of
product/ha) and supplemental hand weeding depending on the locality (Asmare et al. 1995).

Currently, Yelmana Densa and Farta Districts are among the major bread wheat growing areas of
ANRS. Of the total land allocated to cereals, pulses and oil crops, wheat accounts for 8.2% in
Yelmana Densa and 25% in Farta District (Table 3). Despite the importance of wheat in these
districts, the degree of adoption of improved bread wheat technologies and the current production
status are not well known. As indicated in the previous section, this study was initiated to
investigate and assess the technical, social, and economic factors affecting the adoption of
improved bread wheat varieties and inorganic fertilizer and to draw implications for research,
extension, and policy.






3. Wheat Technology Generation

and Dissemination



Although the Debre Zeit Agricultural Research Center, established under the then Agricultural
College of Alemaya, was the pioneer institution for wheat research in Ethiopia, an effective national
wheat research program was organized in the country with the establishment of the Institute of
Agricultural Research in 1966. In addition to a number of research centers and subcenters of IAR
and Debre Zeit, other important agricultural research and development institutions which came
into being in 1967 (i.e., the National Crop Improvement Conference, and the Chilalo and Wolita
Agricultural Development Units) contributed directly or indirectly to wheat research in the
country. Subsequently, a wide range of wheat germplasm was introduced to the country. Research
on wheat focused on screening varieties and developing optimal cultural practices for seed rate,
time of planting, fertilizer type and rate, and weed control (Hailu 1991).










Starting in 1987, bread wheat research was nationally coordinated from the Kulumsa Research
Center. Durum wheat research was coordinated from Debre Zeit starting from the 1950s. Wheat
research was carried out by a team composed of researchers with specializations in breeding,
agronomy, pathology, entomology, weed science, soil science, and agricultural economics. The major
objective of the wheat research program of IAR (now EARO) has been to develop high-yielding
varieties with improved and appropriate management and protection technologies for different
agroecologies. During the last 50 years, several wheat varieties were developed for the agroecologies
of the country (Annexes 1 to 3). Agronomic and crop protection recommendations were also
developed for both large- and small-scale farms.

In ANRS, three research centers were established in different agroecologies. Adet Agricultural
Research Center (ARC) was established in 1986, as a center of the former IAR, with the main
objective of improving the living standards of small-holder farmers in northwestern Ethiopia
through research. Since its establishment, Adet ARC has generated a number of improved
agricultural technologies, including crop varieties, agronomic practices, and crop protection
practices. Adet ARC has no subcenters, but it conducts multilocation trials using 10 IAR/ADD (2.5
ha) testing sites that represent administrative zones rather than agroecologies.

Wheat research started at Adet in 1986 in collaboration with the national wheat research program of
IAR(EARO) and the regional office of the CIMMYT Wheat Program (based in Ethiopia). Wheat
research included variety development and adaptation, pest and disease control, crop management
practices, and technology transfer. To date, one improved wheat variety has been released
specifically for ANRS from the breeding program based at Adet ARC. Currently farmers use a range
of improved wheat varieties developed by the Kulumsa, Debre Zeit and Holetta research centers,
which historically have been responsible for the introduction and distribution of germplasm and
other breeding materials for wheat variety development and release.

The use of crop inputs such as fertilizer, pesticides, and improved farm implements is essential to
realize the full genetic potential of high yielding improved wheat varieties. A strong and efficient
national, regional, and district agricultural extension service that stimulates the adoption of
recommended scientific farming techniques and ideas is a prerequisite for successfully diffusing
technology.

The extension strategy known as the Participatory Demonstration and Training Extension System
(PADETES), which has been implemented in the study area, encourages farmers to adopt such
technologies in association with the appropriate complementary production inputs. This aggressive
technology transfer program is filling the major gaps that existed in various previous extension
systems. The PADETES approach facilitates access to agricultural technologies developed by EARO,
improves access to inputs by providing credit, and includes intensive, practical training of extension
staff (to the development agent level) and farmers. Furthermore, the mobility of extension workers
is improved through the provision of vehicles, motorcycles, bicycles, and pack animals to facilitate
the implementation of the program. The other strength of the program is the effort made to build
stronger linkages between research, extension, and input distributors-a key issue for successful
agricultural technology transfer. The extension program uses large demonstration plots, usually
0.25-0.50 ha, to demonstrate improved farming practices. Regular visits to demonstration plots










provide ample opportunity to discuss problems encountered in the process with farmers. In this
strategy, the most important recommendations for wheat production include seed of improved
varieties, seedbed preparation, optimum seed rate, methods of fertilizer application, fertilizer type
and rate, and use of pesticides.

The PADETES program includes farm households on the basis of accessibility, population density, and
settlement pattern. At present, the Development Agent (DA) to farmer ratio is 1:800 in Farta and
1:1,078 (1999/2000) in Yelmana Densa District. The major tasks of DAs include organizing
demonstration trials, assisting farmers in obtaining agricultural inputs, and channeling farmers'
problems to the relevant organizations,
particularly to the District Department of Table 4. Amount of improved seed quintalss) distributed in
Agriculture. In 1999/2000, the Farta District Yelmana Densa and Farta Districts, Ethiopia, 1995-99
Department of Agriculture distributed 2,553 District and
quintals (qt) of diammonium phosphate (DAP) year Maize Tef Wheat Total
and 1,964 quintals of urea. In Yelmana Densa, Yelmana Densa
19,019 qt of DAP and 11,418 qt of urea were 1996 9 215 283 507
distributed in the same year. The total amount of 1997 24 153 275 452
1998 138 231 328 697
improved seed distributed in Yelmana Densa 1999 271 221 54 546
District by the Department of Agriculture in 1998 Farta
was 697 qt. Seed of improved wheat varieties 1995 9 105 114
1996 1,158 1,158
accounted for 47% of the seed distributed. In 1997 4 36 245 285
Farta, the total amount of seed distributed to 1998 3 114 117
farmers in 1999 was 530 qt, and seed of 1999 10 120 400 530
improved bread wheat varieties accounted for Source: Yelmana Densa and Farta District Agricultural Departments.
about 75% of this seed (Table 4). Note: One quintal (qt) = 100 kg.


4. Methodology



4.1 Sampling Procedure
The study was conducted during 1999 in West Gojam and South Gonder, two administrative zones of
ANRS. These zones were selected on the basis of their large wheat production area, number of
growers, potential for wheat production, accessibility, and representativeness of the farming system.
Once the zones were selected, the same procedure and selection criteria were used to select the study
districts, namely Yelmana Densa and Farta.

A two-stage sampling procedure was used to select farmers for the study. Peasant associations (PAs)
were selected using a random sampling procedure. In the course of selecting sample PAs, precaution
was taken not to select inaccessible and non-wheat growing PAs in either district. Following the
selection of the PAs, 100 sample farmers were randomly selected in each of the two districts using a
sampling frame developed in conjunction with the staff of development centers and/or PA offices.










4.2 Data Collection
Data were collected from primary and secondary sources. Secondary sources included published
and unpublished information about agricultural production in particular and the study areas in
general. This information was collected from regional-, zonal- and district-level offices of
agriculture, planning bureaus, and knowledgeable individuals. Primary data, which pertained to
the 1998 cropping season, were collected from sample farmers using a structured questionnaire
administered during April 1999 in Farta and October 1999 in Yelmana Densa. Before starting the
actual data collection, the questionnaire was pre-tested, enabling the modification of some of the
questions which were either irrelevant to the current situation or out of context. Experienced
enumerators were hired to administer the questionnaire. They were trained in the content of the
questionnaire, methods of data collection, and on the appropriate way to approach farmers.




4.3 Analytical Procedure
Small-farm families are in general conservative decision-makers. As they endeavor to adjust to the
prevailing physical, social, and economic environment, they test and choose carefully among
alternative technologies and production strategies and then adapt them to their particular farming
conditions and needs.

Following data collection, the data were coded and entered into SPSS Version 9 computer software
for analysis. Analytical techniques applied included t-test, chi-square test, and correlation analysis
as well as logistic regression models. Frequency and means were computed for different variables.
The t-test was run to detect statistically significant differences in the continuous variables
representing the characteristics of farmers who adopted improved wheat varieties versus those who
had not adopted. The chi-square test was run to detect any systematic association between adoption
and specific farm characteristics. Of the two related multifactorial analysis techniques, logistic and
probit analysis (Amemiya 1981; Feder et al. 1985), that are particularly useful for analyzing data
generated by adoption studies, a logistic adoption model was utilized to determine the factors
affecting the adoption of improved wheat varieties and chemical fertilizer.

The logistic model used in this study estimated the probability of adoption of improved bread
wheat varieties by using one of two values for adoption versus nonadoption. If the response of the
1th farmer to the question of adoption is denoted by a random variable zi and a corresponding
probability (i.e., probability of adoption or nonadoption) by pi such that the probability of adoption
(zi = 1) = pi and the probability of nonadoption (zi = 0) = 1 pi, the logistic model is specified by:



Logit (p,) = Log (p, /1-Pi) = Bo + B, Xi + B2 X2i + ... + B Xi = hi

so that: P= ei' / (1 e),



where li is known as the logistic transformation of pi.










Other transformations that are commonly used are probit or inverse normal transformation and
complementary log transformation (Collet 1991). In many practical situations, probit and logit
transformation give very similar results, both being characterized by symmetry about hi = 0. The
logistic model was used in this study because it is computationally simpler to estimate and
interpret, particularly for the logarithm of the odds ratio. The logit model assessment is based on a
maximized log likelihood, log L (B), from which a deviance is calculated. This is useful for
comparing two nested models:



D = 2 [L (B', Z)- LO (B', Z)]



Goodness of fit of the model is assessed by residual plots, which may help to identify outliers. In a
binary data set, outliers correspond to misclassification of the observed response.

A farmer's decision either to adopt or reject a new technology is influenced by the combined effect
of a number of factors related to farmers' objectives and constraints (CIMMYT 1993). In this study,
three aspects were considered in the analysis of factors associated with the adoption of improved
bread wheat varieties and chemical fertilizer:

1. Farmers' socioeconomic circumstances (e.g., age, formal education, etc.);
2. Farmers' resource endowments (e.g., size of family labor, farm size and livestock ownership);
and
3. Institutional support systems available to farmers (e.g., credit, extension, and availability of
inputs).


A number of variables were hypothesized to influence the adoption of improved bread wheat
varieties and the use of inorganic fertilizer, as explained below.

Level of education (EDUCLEVL). Level of education is assumed to increase a farmer's ability to
obtain, process, and use information relevant to the adoption of improved bread wheat varieties
and fertilizer. Education was therefore expected to increase the probability of adoption of improved
bread wheat varieties and fertilizer.

Farming experience (FARMEXP). The previous experience of farmers can be expected to either
enhance or diminish their level of confidence. It is anticipated that with more experience, farmers
could become risk-aversive regarding the adoption of specific wheat varieties. Thus, this variable
could have either a positive or a negative effect on farmers' decisions to adopt a specific
wheat variety.

Access to credit (CREDSTS). Access to credit can relax farmers' financial constraints and, in some
cases, is tied to a particular technology package. In this study, access to credit was expected to
increase the probability of adopting improved bread wheat varieties and fertilizer.










Extension contact (EXTCONT). Agricultural extension services provided by the Department of
Agriculture at all levels of ARNS represent the major source of information for farmers. Contact
with extension agents (development agents) was hypothesized to increase a farmer's likelihood of
adopting improved bread wheat varieties and fertilizer.

Total livestock units (TOTALTLU). The number of livestock owned by a farmer was hypothesized
to be positively related to the adoption of an improved bread wheat variety. The total livestock unit
(TLU) index aggregates livestock numbers using the following weighting factors: oxen = 1; cows,
heifers, and bulls = 0.8; and goats and sheep = 0.4.

Distance to nearest development center (DISTCNTR). The further an extension office is located
from farmers' homes, the less likely it is that farmers will have access to information. Thus, this
factor could be expected to be inversely related to the adoption of an improved wheat variety and
fertilizer.

Attend field day (ATTFDAY), participate in demonstration plot (PARTDEMO), and attend formal
training (ATTECRS). Farmers who have attended field days, visited demonstration plots, and
participated in formal agricultural training are expected to have a positive attitude to the adoption
of improved wheat varieties and fertilizer. It was hypothesized that participation in the above-
mentioned activities could be expected to be an exposure variable and would be positively related
to the adoption of an improved wheat varieties and fertilizer.

Farm size (FARMSZ). Land shortage caused by population pressure is acute in the study areas.
Increasing the production and productivity of wheat depends on increased cropping intensity by
using seed of improved wheat varieties and fertilizer. Therefore, farm size was hypothesized to be
inversely related to the adoption of an improved wheat variety and fertilizer.

Distance to market center (DISTRMKT). Distance to market center was hypothesized to be
negatively related to the probability of adoption of improved bread wheat varieties, since
households near market centers tend to have easier market access to dispose of their production.

Use of chemical fertilizer (USEFERT). Use of chemical fertilizer was hypothesized to be positively
related to the probability of adoption of an improved bread wheat variety because such varieties are
known to exhibit superior response to chemical fertilizer.

Ownership of radio (RADIOWN). Radio ownership and the ability to receive broadcast
agricultural programs was expected to influence a farmer's awareness and hence adoption of
improved bread wheat varieties and fertilizer.

Timely delivery of fertilizer (FERTIME). Timely delivery of fertilizer was expected to influence the
decision to adopt improved bread wheat varieties and fertilizer.










5. Demographic, Socioeconomic, and Institutional

Characteristics of Wheat Farmers in the Study Area



5.1 Demographic Characteristics
Table 5 summarizes the demographic characteristics of wheat farmers in the study area. The mean
age of adopters of improved wheat varieties was 41 years. Age was one of the demographic
characteristics assumed to influence the decision to adopt new technologies, but this study found no
significant difference in age between farmers who had adopted improved wheat varieties and those
who had not. Level of education was also assumed to influence the adoption decision, since literate
farmers would have a greater ability to obtain, process, and use information about improved
technologies. However, no significant difference was found in the level of education between
adopters and nonadopters of improved wheat varieties. The majority of the adopters of improved
wheat varieties (75%) were literate, of whom 16% had elementary education, 48% participated in a
literacy campaign, and 3% and 8% reached junior and senior high school, respectively. The chi-
square analysis showed no systematic association between the level of education and the adoption
of improved wheat varieties, however.

The average number of years of farming experience of adopters of improved wheat varieties was
20.7, whereas that of nonadopters was about 25.7 (Table 5). The magnitude of the standard deviation
(S.D.) of farm experience indicated a considerable variability (i.e., some adopters have little
experience while others have many years of farming experience). In this analysis, it was
hypothesized that with more farming experience, a farmer can become more or less averse to the
risk implicit in adopting a new technology. The study showed no significant difference, however, in
years of farming experience between adopters and nonadopters of improved wheat varieties.


Table 5. Demographic characteristics of wheat farmers in Yelmana Densa and Farta Districts, Ethiopia
Adopters Non-adopters
Characteristic Mean S.D. Mean S.D. t statistic
Age of household head 41.02 12.39 41.88 14.03 0.261 NS
Experience of farming (years) 20.69 15.98 25.69 20.50 1.231 NS
Total number of persons living in the household 5.83 2.18 6.44 2.03 1.076 NS
Children under 8 years 1.76 1.34 1.94 1.39 0.502 NS
Children between 8-13 1.14 1.07 1.56 1.26 1.478 NS
Adult males 14-60 years 1.53 0.80 1.56 0.96 0.163 NS
Adult females 14-60 years 1.31 0.60 1.19 0.54 -0.794 NS
DeDendents 61 years and above 0.13 0.38 0.25 0.15 1.210 NS


Percent of adopters


Level of education
Illiterate
Read and write
Primary school
Junior high school
Senior high school


Percent of non-adopters

50.0
31.0
19.0


Note: NS = not significant.


X2 statistic

5.895 NS


I J










The average household size of adopters was 5.83 persons, consisting of 30% children less than 8
years, 20% children between 8 and 13 years, 26% adult males, 22% adult females, and 2% aged
dependents. As expected, children less than 13 years of age dominate the family composition, as in
other parts of the country. The number of aged dependents is unusually small compared to
numbers reported in similar studies. Family size was hypothesized to influence farmers' adoption
behavior, in that farmers with a large family size were expected to be more likely to adopt improved
wheat technologies to increase productivity. The statistical analysis showed no significant
difference, however, in the family size of adopters versus nonadopters of improved wheat varieties.




5.2 Socioeconomic Characteristics
5.2.1 Farm land. Mean farm size per household was 5.13 eka (one eka is equivalent to 0.25 ha), of
which 75% was cultivated, 6.6% was for grazing, 1.8% was fallow, and 16% was homestead (Table
6). Nonadopters possessed significantly more land than adopters in terms of total farm size and
cultivated land (Table 6). The area of land allocated by adopters and nonadopters for wheat
production was 1.33 and 0.83 eka, respectively. Tef, wheat, barley, and maize are the most important
crops grown in terms of area covered (Table 6). In terms of the number of growers (i.e., the
frequency of production), barley was the first-ranked crop, followed by wheat, tef, and maize.

5.2.2 Labor. Involvement in off-farm jobs was one of the socioeconomic characteristics hypothesized
to influence the decision to adopt improved wheat technologies, in that households involved in off-
farm jobs may be able to afford to invest in improved technologies. However, the chi-square
analysis revealed that adoption of an improved wheat variety was not systematically associated
with involvement in off-farm activities (Table 7). Petty trading is the major off-farm job for adopters
of improved wheat varieties and nonadopters. About 32.7% of adopters and 35.1% of nonadopters
reported off-farm work during the survey year. The average annual income earned by adopters and
nonadopters from off-farm activity was about ETB 442 and ETB 1,100, respectively (Table 7).

About 77.4 % of adopters and 66.7% of nonadopters reported that they faced a labor shortage
during farm operations. To overcome this problem, 47.3% and 26.4% of adopters and 25% and 12.5%




Table 6. Size of land holding (eka) of wheat farmers in Yelmana Densa and Farta Districts, Ethiopia
Adopters Non-adopters
Characteristic No. Mean S.D. No. Mean S.D. t statistic
Total farm size 180 5.00 2.38 15 6.95 2.41 3.037*
Cultivated land 171 3.87 2.25 14 5.86 2.73 3.134*
Area of tef 100 2.90 1.96 14 2.96 1.15 0.901 NS
Area of barley 150 1.29 0.77 16 1.28 0.60 0.985 NS
Area of maize 90 1.63 0.95 14 1.46 0.50 0.261 NS
Area of wheat 124 1.33 0.89 3 0.83 0.29 -0.962 NS
Fallow land 8 1.91 2.98 1 0.25
Grazing land 79 0.72 0.47 7 0.61 0.28 -0.604 NS
Note: Indicates significance at the 5% level. NS = not significant. One eka = is equivalent to 0.25 ha.










Table 7. Socioeconomic characteristics of wheat farmers in Yelmana Densa and Farta Districts, Ethiopia
Adopters Non-adopters
Characteristic No. Percent No. Percent X2 statistic
Do you face labor shortage?
Yes 120 77.4 26 66.7 1.935 NS
No 35 22.6 13 33.3
How do you overcome labor shortage?
Community labor 70 47.3 2 25.0 4.902t
Hired labor 39 26.4 1 12.5
Community and hired labor 39 26.4 5 62.5
Do you have off-farm work?
Yes 50 32.7 13 35.1 0.81 NS
No 103 67.3 24 64.9
If yes, type of work:
Trading 27 58.7 5 38.5 10.67 NS
Laborer 4 8.7 2 15.4
Carpenter 3 6.5 1 7.7
Civil servant 6 13.0 2 15.4
Weaving 1 2.2 1 7.7
Trading carpenter 1 2.2
Adopters Non-adopters
N Mean S.D. N Mean S.D.
Community and hired labor used for
different operations (person-days) 139 24.14 24.98 7 14.00 10.63
Estimated off-farm income/year (ETB) 51 441.96 572.97 2 1100 1,272.79
Note: t indicates significance at the 10% level. NS = not significant.



of nonadopters used community and hired labor, respectively, for wheat production. The chi-square
statistic showed a systematic association between the adoption of improved bread wheat varieties and
the use of community and hired labor (Table 7). The most important community labor arrangements
are locally called Wobera/Debo and Wonfel. The total community and hired labor used for different
operations were estimated at 24 and 14 work-days for adopters and nonadopters of improved wheat
varieties, respectively (Table 7).

5.2.3 Livestock. The mean number of animals per household was 3.73 cattle, 0.94 equines (i.e.,
composed of mules, horses, and donkeys), 1.97 small ruminants, and 2.01 chickens. Households also
owned a mean number of 0.53 beehives. In terms of ownership, only 15% of the households had
beehives (ranging in number from 1 to 20), 64% had chickens, 18% had mules, 26% had horses, and
36% had donkeys. On the other hand, 22.5% of the households did not own oxen, 43.5% owned one ox,
32% owned two oxen, while only 2% owned 3 oxen, and no household owned more than 3 oxen. The t-
test revealed that there is no significant difference in the number of oxen owned by farmers who have
adopted improved wheat varieties and those who have not (Table 8). Mules and horses, which are
wealth indicators in some areas of Ethiopia, are relatively abundant in the study area; nonadopters
apparently own fewer horses than adopters (Table 8).

The number of livestock units owned by a farmer was hypothesized to affect the adoption of improved
technologies, since TLUs represent a ready source of cash for purchasing farm inputs. However, there










Table 8. Number of livestock owned by adopters and non-
adopters in Yelmana Densa and Farta Districts, Ethiopia


Livestock type
Cows
Oxen
Bulls
Heifers
Calves
Goats
Sheep
Chickens
Beehives
Mules
Horses
Donkeys


Adopters
Mean S.D.
0.92 0.92
1.13 0.80
0.43 0.64
0.66 0.83
0.65 0.69
0.28 0.88
1.73 1.85
2.01 2.67
0.57 2.12
0.19 0.43
0.32 0.56
0.46 0.70


Non-adopters
Mean S.D.
0.69 0.69
1.19 0.66
0.31 0.60
0.56 0.89
0.50 0.52
0.31 1.01
1.00 1.41
2.19 2.20
0.006 0.25
0.006 0.25
0.006 0.25
0.31 0.48


Note: t = indicates significance at the 10% level. Number of adopters
number of non-adopters = 16.


t statistic
-1.277
0.263
-0.723
-0.425
-0.823
0.143
-1.544
0.264
-0.960
-1.156
-1.8581
-0.805
=180;


was no significant difference between adopters
and nonadopters with regard to most livestock
types (Table 8). The mean estimated number of
oxen, cows, and small ruminants was the same
for both adopters and nonadopters; however,
adopters seem to be superior to nonadopters
with respect to the number of beehives, mules,
and horses owned.




5.3 Institutional Characteristics
5.3.1 Access to extension. Table 9 summarizes
the institutional characteristics of wheat farmers
in the study area. Access to information or
extension messages was one institutional
characteristic hypothesized to influence a
farmer's decision to adopt a new technology.
One can gain access to information about new
technologies through various means, such as
attending field days, visiting demonstration
fields, participating in formal training, listening
to agricultural programs on the radio, and
through communicating with neighbors,
relatives, and community leaders. Of these, the
main source of information for wheat production
practices is the extension service of the Bureau of
Agriculture at the regional, zonal, and
district levels.


Table 9. Institutional characteristics in Yelmana Densa and
Farta Districts, Ethiopia

Percent Percent
of of non-
Characteristic adopters adopters 2 statistic


Participated in demonstration
of wheat varieties?
Yes
No
Attended a field day?
Yes
No
Attended a formal training
course in agriculture?
Yes
No
Visited by extension agent in 1999?
Yes
No
Own a radio?
Yes
No
Member of a producer co-op?
Yes
No
Contact farmer?
Yes
No
Member of informal
organization?
Yes
No
Office bearer?
Yes
No
Usual types of visits made by
extension agents:
Individual contact
Group contact
Both
Member of Tsigie?
Yes
No
Member of Mahiber?
Yes
No
Member of Edir?
Yes
No
Member of Senbete?
Yes
No


100.0


9.219**


0.222NS


3.946*


100.0


41.7 50.0
58.3 50.0


0.418NS


1.924NS


0.243NS


1.488NS



0.938NS


0.283NS



4.662T



1.213NS


0.546NS


6.998**


0.906NS


Note: t indicates significance at P<0.1; indicates significance at P<0.05; ** indicates
significance at P<0.01; and NS = not significant.
Tsigie, Mahiber, Edir, Senbete ARE THESE FARMER GROUPS, CREDIT CLUBS, CO-OPS,
COMMUNITY GROUPS????










About 42.7% and 32.6% of adopters reported attendance at demonstrations and field days,
respectively, whereas 20.1% of adopters reported attendance at a formal training course on
improved wheat production practices. The chi-square analysis revealed a systematic association
for both participation in a demonstration and attendance at a formal training course with the
adoption of an improved wheat variety (Table 9). The types of contacts made by extension agents
with farmers were identified as individual, group, and both individual and group: 21.6% and
68.9% of adopters were visited individually and as part of a group, respectively, during the
survey year.

About 20.6% of adopters and 6.3% of nonadopters owned a radio. However, no systematic
association was found between the adoption of improved wheat varieties and the ability to listen
to agricultural programs on the radio.

Distance to a development center was hypothesized to influence the adoption of improved
wheat technologies. Compared to than households farther away, households near a development
center are considered more likely to have access to development agents, new technologies, and
information. However, no significant difference was observed in the distance to a development
center from the residence of adopters versus nonadopters. The average time taken to reach the
nearest development center was about 30 minutes; on average, it takes a farmer about 40 minutes
to reach the nearest market center. Household members travel an average of 1.9 km to the nearest
development center, an average of 2.9 km to the nearest market place, and 1.72 km to a
main road.

5.3.2 Credit availability. Access to credit was hypothesized as one of the major institutional
factors influencing the decision of a farmer to adopt new technologies. In the study area, it was
found that 73.6% of the adopters of an improved wheat variety and 46.7% of nonadopters
reported obtaining credit from the state (i.e., the Bureau of Agriculture at all levels). The chi-
square analysis showed a systematic association between adoption of an improved wheat variety
and access to credit (Table 10), indicating that farmers with access to credit have a higher
probability of adopting improved bread wheat varieties than those households with no access to
credit. The main purpose for which both categories of farmers take credit is to purchase chemical
fertilizer (Table 10).

About 86% of nonadopters and 63% of adopters reported obtaining credit from other (i.e., non-
State) sources, primarily from relatives and local moneylenders. The main purpose for taking
credit from the informal sector is for home consumption. However, it is important to note that
66.7% of adopters partially fulfill their fertilizer requirement by borrowing from the informal
sector. The most important credit problems cited in the study area were the unavailability of
loans from either formal or informal sources, high interest rates, and unfavorable loan
repayment terms.










Table 10. Credit availability in Yelmana Densa and Farta Districts, Ethiopia
Percent of Percent of non-
Credit characteristic adopters adopters c2 statistic
Obtained credit from the state? 4.923*
Yes 73.6 46.7
No 26.4 53.3
Purpose of credit obtained from the state: 2.576 NS
To purchase fertilizer 55.0 85.7
To purchase seed and fertilizer 37.1 14.3
Obtain credit from others? 2.889t
Yes 63.2 85.7
No 36.8 14.3
Other credit sources: 22.391*
Relatives 49.2 33.3
Local money lenders 10.2 33.3
Purpose of credit from other sources: 9.107 NS
For home consumption 21.4 33.3
For purchasing fertilizer 66.7 9.5
Problems of getting credit? 7.819**
Yes 80.0 57.5
No 20.0 42.5
Nature of credit problems: 9.392NS
Bank loan not available 7.4 11.1
Ministry of Agriculture loan not available 8.8 33.3
Loan from informal sources not available as required 22.1
Repayment terms unfavorable 22.2 14.7
Interest rates too high 17.6 11.1
Note: t indicates significance at P<0.1; indicates significance at P<0.05; ** indicates significance at P<0.01; and NS= not significant.





6. Adoption of Improved Wheat Varieties and

Chemical Fertilizer



The logistic curve, which captures the historical trend of adoption over time, was constructed using
data on the proportion of farmers adopting improved wheat varieties and chemical fertilizer over a
given period. The basic assumption in constructing each logistic curve is that adoption increases
slowly at first and then increases rapidly to approach a maximum level (CIMMYT 1993).
Mathematically, the logistic curve can be expressed by the following formula:

Y= K/(1 + eab

where:

Y, = the cumulative percentage of adopters by time t;
K = the upper bound of adoption (percentage);
b = a constant related to the rate of adoption; and
a = a constant term related to the time when adoption begins.











6.1 Adoption of Improved Wheat Varieties
The rate of adoption of improved wheat varieties increased from less than 1% in 1981 to 72% in
1998. The adoption rate had increased dramatically in the six years since the national extension
package program was started. The preferred improved wheat varieties are ET-13, Dashen, Enkoy,
HAR-1685, and HAR-1709 (in descending order of importance). About 98% of the farmers included
in the study knew about improved wheat varieties. In addition, 98% practiced crop rotation, but
only 17% fallowed their wheat fields, mainly due to the shortage of cultivated land. The major
actors in the dissemination of information on improved wheat varieties were extension agents (54%)
and neighbors (20%). Other sources of information included relatives, researchers, traders, and
producer and service cooperatives (in decreasing order of importance).

The most important initial source of seed of improved wheat varieties in the study areas is the
District Department of Agriculture. The reasons cited for adopting improved wheat varieties were
many, but the most frequent reason was that improved wheat varieties yield better with fertilizer
(81%). Nonadopters have more cultivated land than adopters (Table 6); adopters might have been
induced to adopt the improved technology package to overcome their shortage of land by
increasing unit productivity.


Some of the institutional arrangements seemed
to facilitate the adoption of improved wheat
varieties. For example, a larger proportion of
adopters than nonadopters had previously
belonged to producer cooperatives (17% vs.
12%), were contact farmers (28% vs. 12%), were
members of other informal organizations (93%
vs. 88%), had obtained credit from the State
(73% vs. 44%), and had participated in
demonstrations (42% vs. 1%). The major reasons
cited for ceasing production of improved wheat
varieties were: unavailability of seed of
improved wheat varieties in sufficient quantity,
expense of seed of new varieties, unavailability
of credit, and poor yield performance compared
to local varieties.


on I


1970


1980 1990
Year started planting improved wheat varieties


Figure 1. Adoption of improved bread wheat varieties in
Yelmana Densa and Farta Districts, Ethiopia.


6.2 Adoption of Chemical Fertilizer
Ninety percent of the respondents reported using chemical fertilizer at least once during their
farming experience. Use of chemical fertilizer in the study area dated to 1973. The logistic regression
analysis indicated that the rate of adoption for chemical fertilizer had increased from less than 1% in
1976 to 77% in 1998. The adoption rate had increased markedly in the six years since the national
extension package program was implemented.


*... Observed .*
Logistic
*---------------------------------------------






.----------------------------- --- ---------
---- ----- -.----- -----
*










Over 93% of adopters of improved wheat
varieties also used chemical fertilizer on their
farms. The major crops to which chemical
fertilizer was applied for the first time were tef,
wheat, and barley. During the survey year (i.e.,
the 1998 cropping season), 69.8% of adopters of
improved wheat varieties and 27% of
nonadopters applied chemical fertilizer on
wheat. Diammonium phosphate was the
principal fertilizer used by both adopters and
nonadopters. The analysis of the relationship
between adoption of an improved wheat
variety and use of chemical fertilizer showed
that the two factors are systematically related
(c2 = 11.485; P<0.01).


100

80

S60

S40

E
I 20

0


-201 1 I
1960 1970 1980 1990 20(
Year started using chemical fertilizer
Figure 2. Adoption of chemical fertilizer in Yelmana Densa
and Farta Districts, Ethiopia.


The major source of chemical fertilizer reported by 40% of both adopters and nonadopters was the
Bureau of Agriculture at all levels. Only a few respondents mentioned the Amalgamated or Ambassel
companies as the source of chemical fertilizer. Of those applying fertilizer, about 87% and 63% of
adopters and nonadopters reported receiving chemical fertilizer on time. The study revealed that
receiving fertilizer on time is significantly associated with the adoption of improved wheat varieties
(c2 = 20.242; P<0.05).







7. Factors Affecting Wheat Technology Adoption



7.1 Logistic Regression of Improved Bread Wheat Varieties
Each of the explanatory variables hypothesized (see section 4) to potentially influence adoption of
improved wheat varieties was fitted into a logistic model (Table 11), and their individual contributions
to the model were assessed on the basis of changes in deviance. Variables contributing significantly to
the model were selected and the main effect and interactions were further investigated. The goodness
of fit associated with adding each variable to the model was assessed by comparing the resulting
change in deviance due to the addition of each variable in the model with the corresponding chi-
square value. In this exercise, variables with multicollinearity were identified and dropped from the
model. For example, farmer's age was found to be highly correlated with years of farming experience
and was eliminated from the model.

Farm size influenced the adoption of improved bread wheat varieties positively and significantly.
Although the mean farm size for adopters was less than that for nonadopters, as indicated in Table 6,
there was a tendency detected in the logit analysis for the probability of adopting an improved wheat
variety to increase slightly as farm size increased. Thus farmers with larger farms have a slightly


....**** Observed
Logistic
-- ................/....--



...
--------------------------------- ,----------

-----------------------------r --------------



----------------------- --------------------
lot
.. .. .. .. .. .. .. ..Z "
,'










higher probability of adopting an improved Table 11. Parameter estimates from the logit model for the
wheat variety. The estimation of the regression adoption of improved wheat varieties
coefficient associated with farm size (Table 11) Source Coefficient S.E.
might have been influenced by the range of
farm sizes: for adopters, farm size ranged from Intercept -15.93 75.430
Use of chemical fertilizer 0.00396 0.039
2 to 16 eka; for nonadopters, farm size ranged e 0.5 0.03
Farm size 0.548** 0.203
from 3.5 to 11 eka. Thus in terms of the Extension contact 21.68** 6.610
distribution of farm size, 92% of adopters had Participation in demonstration 5.94* 4.450
farm sizes equal to or greater than the minimum Attended agricultural training course 2.96 5.770
land-holding for nonadopters (i.e., 3.5 eka). The Access to credit 0.495 0.990
Illiterate 4.70 8.670
fact that the mean farm size of adopters was less Elementary education 4.62 8.660
than that of nonadopters (Table 6) might have Junior high school education 0.84 22.100
been due to the 8% of adopters having less than
Note: indicates significance at P<0.05; ** indicates significance at P<0.01; and
3 eka. Furthermore, the number of nonadopters NS =not significant.
identified in the survey was much less than the
number of adopters (i.e., 15 compared to 180; see Table 6). Conversely, the logistic regression, which
estimates an overall trend, might have been influenced by the 3% of adopters having a farm size
greater than the maximum land-holding for nonadopters (i.e., 11 eka).

Participation of farmers in on-farm demonstrations also positively and significantly affected the
adoption pattern of respondents, possibly because improved varieties of bread wheat would have
been included in the demonstration. Attendance at training courses, access to credit, and the farmer's
education level contributed positively to adoption, but the association was very weak (i.e., not
significant at the 10% level). Contacts made with extension agents, service cooperative (SC)
representatives, or peasant association (PA) chairmen contributed significantly and positively to
adoption. Amount of DAP fertilizer used, although not significant at the 5% level, exhibited a slight
association with the decision to adopt an improved bread wheat variety. By using forward stepwise
regression (i.e., by successively adding variables to a model that already contained other significant
variables, and by testing the resultant change in deviance with the relevant chi-square values), the
final model contained participation in demonstrations, agricultural extension contact, and farm size
with a deviance of 32.69 (P < 0.01).

Other variables such as radio ownership contributed very little to the logistic model, suggesting that
information about agricultural technology is better diffused among farmers through other methods
such as extension contact and demonstration of improved wheat varieties. Number of livestock units,
distance to a development center, and years of farming experience, factors which were reported as
significant in other studies (Chilot et al. 1996), did not contribute to the adoption of improved bread
wheat varieties in the current study.

For the purpose of calculating the predicted probability of adopting improved bread wheat varieties,
farm size was recorded as low and high. Using this classification, the fit of the model was improved
(deviance = 40.5) as the classification gave a reasonable demarcation between poor, intermediate, and
better-off farmers. Consequently, the probability of adopting improved wheat varieties was about 1
for farmers who are in contact with extension agents, whether they have a large or small farm size










and whether or not they participated in demonstrations. Those farmers who are in contact with a PA
chairman and those participating in demonstrations, whether poor or better off, also exhibited a
higher probability of adopting an improved wheat variety. Participation in field demonstrations
generally seemed to increase the probability of adoption regardless of the type of farmer considered.




7.2 Logistic Regression of Chemical Fertilizer Use
As with the logistic model for adoption of improved wheat varieties, variables related to the
adoption of fertilizer that were significant at the 5% level were included in the model for fertilizer
use. When these eight variables (Table 12) were simultaneously fitted, attendance at an agricultural
training course, radio ownership, membership in a producer cooperative, farm size, total livestock
units owned, and access to credit had significant influence on the adoption of chemical fertilizer at
the 10% significance level or greater. Attendance at a training course, radio ownership, and
membership in a producer cooperative were the major factors affecting fertilizer adoption in a
positive manner (Table 12). However, participation in demonstrations and field days, although not
significant at the 10% probability level, also appeared to contribute positively to the adoption of
chemical fertilizer. Unfortunately, there may be conditions under which the adoption of an
improved wheat variety and the use of chemical fertilizer did not coincide: some variables which
were highly related to the adoption of improved wheat varieties did not appear to be related to the
use of chemical fertilizer, and vice versa. For example, radio ownership contributed highly to
fertilizer adoption but did not contribute to the adoption of improved wheat varieties.

Several combinations of variables were tested in the logit model using stepwise regression and
assessing the resulting changes in deviance. Consequently, the final model selected to describe the
adoption of fertilizer contained only participation in demonstrations and access to credit. The
inclusion of other variables may improve the model but will not affect the level of significance.
Participation in demonstrations and access to credit assumed major importance when fitted
simultaneously, and the change in deviance was considerable. When fitted with the entire set of
variables, however, these two factors did not contribute significantly to the model, presumably due
to multicollinearity with the other variables. A model with fewer variables as components is
preferred to simplify the calculation of the
probability of adoption. The result showed that Table 12. Parameter estimates from the logit model for the
the probability of using chemical fertilizer for adoption of chemical fertilizer
farmers who had attended demonstrations, Source Coefficient S.E.
regardless of their credit access, was the highest Intercept -77.2 5358.30
(0.999). For farmers who neither had access to Farm size -0.493t 0.30
Tropical livestock units owned -1.219* 0.53
credit nor had participated in demonstrations, Tropical livestock units owned 1.21 0.53
Participation in demonstration 17.08 3630.80
the probability was 0.81, demonstrating once Attended field day 15.3 3940.70
again the importance of the two factors in the Attended agricultural training course 16.2** 2.00
adoption of chemical fertilizer. Radio ownership 17.2** 2.80
Membership in producer cooperative 14.78** 3.30
Access to credit 1.57t 0.89
Note: t indicates significance at P<0.1; indicates significance at P<0.05; **
indicates significance at P<0.01; and NS = not significant.










8. Discriminate Analysis of the Adoption of Improved

Wheat Varieties and Fertilizer



Discriminate analysis was performed to derive classification rules mathematically based on the
categories of adopters and nonadopters and then to assign households into one of those two
categories. The analysis therefore had two purposes: first, to see how adopting and non-adopting
farmers could be separated based on the observed characteristics that were assumed to influence
adoption; second, to classify new observations (i.e., those not included in the study) into adopter or
nonadopter groups based on the criteria developed.

The theoretical basis underlying the use of discriminate classification is the formulation of decision
rules that partition a given group-in this case, the farmers under study-into sub-groups. A
considerable account of this work may be found in Fisher et al. (1996). In the case of a linear
discriminate, the allocation rule will allocate household M to its group for which:

Li (M) = log qi + pil-1 (M-'12 pi) is greatest,

Where:

L = discriminate function;
M = household;
qi = prior probability;
ti = group mean; and
1-1 = common dispersion matrix.


This equation is, in fact, equivalent to saying that households are classified into group i if the
posterior probability of belonging to group i is the greatest. In other words, the rule or the formula
developed calculates the likelihood that a given farmer will be an adopter or a nonadopter.

Posterior probability is the probability of allocating a newly identified farmer into the adopter or
nonadopter groups. Consequently, the posterior probability of being an adopter is more than that of
being a nonadopter, probably due to the larger number of households in the adopter group. In this
study, the classification rule works very well for both adopters and nonadopters, since about 83% of
the households are classified correctly and the error rate is very small (Table 13). Based on these
results and the fact that the households in the study were initially sampled at random, it may be
inferred that newly identified farmers will have a 67% probability of being an adopter of an
improved wheat variety. Parameter estimates for the discriminate function revealed that farm size
(coefficient = 0.722) and participation in demonstrations (coefficient = -0.672) played a key role in
distinguishing the two groups.










There is also strong evidence that the two groups are separated very well on the canonical scale. The
overall variability was explained by just the first component, indicating that there is a relatively
clear-cut classification between the two groups. There was also only a 5% overlapping of holdings
(i.e., a few households tended to have a comparable posterior probability of being classified as an
adopter or a nonadopter).

As in the case of the adoption of an improved wheat variety, the classification rule developed for
adoption of fertilizer based on the observed data worked fairly well for both adopters and
nonadopters, as the correct classification was about 84%. The error count rate is coincidentally small
and similar to that for the adoption of wheat varieties (Table 13). The posterior probability (i.e., the
probability that a randomly selected household in the study area uses chemical fertilizer) is about
71%, a slight improvement over that for the adoption of improved wheat varieties.

There is also a clear-cut separation between users and non-users of fertilizer, as only 1% of the study
farmers tended to have a comparable posterior probability of being allocated to either group. Such a
clear separation was highly influenced by variables that exhibited the largest canonical coefficient,
such as access to credit.


Table 13. Misclassification error rate estimates for adoption of improved wheat varieties and chemical fertilizer

Improved wheat variety Chemical fertilizer

Adopters Non-adopters Adopters Non-adopters

Error count rate 0.17 0.10 0.16 0.18
Posterior probability of
classification into a class 0.67 0.33 0.71 0.29


9. Multivariate Analysis of Variance (MANOVA)



The conventional approach of comparing two or more groups based on only one characteristic, say
farm size alone, is known as univariate analysis of variance. Often the question of comparing
groups requires a multivariate approach, however (Krzanowski 1988). In other words, the groups to
be compared could normally be affected by more than one variable simultaneously. For example, a
simultaneous effect of, for example, farm size, household size, access to credit, and other variables
may be reflected in the differences between adopters and nonadopters. This situation can be
handled through multivariate analysis of variance.

Multivariate analysis of variance was therefore fitted to the data taking into account the continuous
variables that were assumed to contribute to adoption, in order to fulfill the basic assumption of
analysis of variance. The continuous variables included in the separate analyses of the adoption of










improved wheat varieties and chemical fertilizer are distance from development center, distance
from market center, amount of DAP, age of head of household, farm size, and total livestock units
(TLU) owned. Consequently, all of the multivariate test statistics revealed significance, indicating
considerable differences between adopters and nonadopters of improved wheat varieties. On the
other hand, there was no significant difference between the chemical fertilizer users and nonusers in
terms of the above mentioned continuous variables.




10. Conclusions and Recommendations



The study revealed that the rate of adoption of improved bread wheat varieties has increased from
less than 1% in 1981 to 72% in 1998. The adoption rate increased markedly over the last six years
since the national extension package program commenced. About 98% of the farmers included in
the study knew about improved wheat varieties. The major actors in the dissemination of
information about wheat technology are extension agents and neighbors. Other sources of
information included relatives, researchers, traders, producers, and service cooperatives.

As far as the adoption of chemical fertilizer is concerned, the study revealed that about 90% of the
respondents had used chemical fertilizer at some point in their farming experience. The adoption
rate for chemical fertilizer increased from less than 1% in 1976 to 77% in 1998. Ninety-three percent
of adopters of improved bread wheat varieties used chemical fertilizer during the survey year. The
source of fertilizer used by 98% of the adopters of improved bread wheat varieties and 14% of
nonadopters was the Bureau of Agriculture. Few respondents mentioned the Amalgamated and
Ambassel companies as sources of chemical fertilizer.

The agricultural research system should put more emphasis on solving the problems of wheat
producers and increase the frequency of release of new varieties that resist diseases and pests, yield
well, and tolerate drought. To make the research effort more successful, seed of newly developed
varieties must be produced in sufficient quantities and quality for producers in the study area, the
region, and the nation at large. The steps taken by the government to establish the National Seed
Industry Agency and to allow the private sector to participate in seed production, processing, and
distribution are expected to increase the volume of seed produced. To achieve this goal, the
government must provide incentives and support to public and private seed companies, including
infrastructure and credit.

Research on bread wheat has established that the improved varieties released to date are responsive
to fertilizer and that farmers obtain an economic benefit from applying fertilizer. The mean fertilizer
application rate is lower than the recommended rate, however, despite the dramatic increase in
fertilizer use resulting from the implementation of the PADETES extension program. As observed by
the authors of this study, fertilizer application is constrained by a perceived high price of fertilizer
and by farmers' lack of knowledge about how to use it. An efficient marketing system for inputs and
outputs will benefit farmers by facilitating higher prices for marketed wheat and reducing the cost










of fertilizer. Since the input and output markets for crops, including bread wheat, have been
liberalized, there is a need to obtain updated information on the economics of using improved seed
and fertilizer. The government should provide the necessary support to develop rural roads and
other infrastructure such as storage facilities, which should enable inputs to be transported to
farmers more efficiently and at a lower unit transport cost.

To increase the flow of information to farmers (and the adoption of new technologies), the extension
package program (PADETES) needs further strengthening. More demonstration sites for improved
technologies, including wheat varieties and fertilizer application, should be organized to increase
awareness of the new technologies among farmers in the study area. The contact between extension
agents and farmers must be strengthened by reducing the ratio of farmers to development agents.
The extension program should enhance transport facilities for development agents to increase their
capacity to travel within their mandated area. In addition, frequent training must be organized for
development agents and supervisors about existing and newly developed improved agricultural
technologies and practices. This training would bolster the agents' confidence and ability to transmit
appropriate and useful information to farmers.

The most important credit problems cited in the study area were the unavailability of loans from
formal and informal sources, high interest rates, and unfavorable loan repayment terms. It has been
noted that with rising input prices, improved access to credit for peasant farmers has become
indispensable. The formal credit system needs to address the credit constraints faced by small-scale
farmers and increase awareness about the types of credit available for agricultural production. In
addition, the government should encourage farmers to form service cooperatives or farmers' groups
to reduce transaction costs and improve loan recovery rates.











References


Refs in text double names please

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Annex 1




Bread Wheat Varieties Tested and Released in Ethiopia since 1950


Decade and variety Origin Location where selected Year released


1950s
Kenya-1
Kenya-5
Kenya-6
1960s
Kenya-Supremo x Yaqui 48
Anguilera Kenya x Marroqui-Supremo x Yaqui-48
Yaktana-54
Kentana Frontana x Mayo-48
Frocor x (YT x KT) sib
Supremo Kenya x Yaqui-48
1970s
Laketch
Romany
Kanga
Penjamo-62
INIA-66
Mamba
Salamayo
Romany BC
Dereselign
Enkoy
Sonora-63
Cl-14393
K6290 Bulk
K6106-8
Genet-71
1980s
K6295-4A
ET12.D4
ET13.A2
KKBB
Pavon-76
Chenab-70
Blue Jay
K6106-9
Batu
Dashen
Gara
HAR-407
HAR-416
1990s
Mitikie (HAR-1709)
Kubsa (HAR-1685)
Wabe (HAR-710)
Galama (HAR-604)
Magal (HAR-1595)
Abola (HAR-1522)
Tusie (HAR-1407)
Katar (HAR-1899)
Tura (HAR-1775)
Shinna (HAR-1868)


Kenya

Mexico
Mexico
Kenya/Mexico
Kenya/Mexico
Kenya/Mexico
Kenya/Mexico

Mexico
Kenya
Kenya
Mexico
Mexico
Kenya/Ethiopia
Colombia
Kenya/Mexico
Mexico
Kenya/Ethiopia
Mexico
Ecuador
Kenya
Kenya
Chile/Mexico


Kenya
Ethiopia
Ethiopia
CIMMYT
CIMMYT
CIMMYT
CIMMYT
Kenya
CIMMYT
CIMMYT
CIMMYT
CIMMYT
CIMMYT

CIMMYT
CIMMYT
CIMMYT
CIMMYT
CIMMYT
CIMMYT
CIMMYT
CIMMYT
CIMMYT
CIMMYT


Paradise
Paradise
Paradise

Paradise
Paradise
D. Zeit
D. Zeit
D. Zeit
D. Zeit

D. Zeit
Holetta
DZ/Holetta
DZ/Holetta
DZ/Holetta
Holetta
Holetta
Holetta
D. Zeit
Holetta/DZ
Holetta
Holetta
Kulumsa
D. Zeit
Holetta


Holetta/Kulumsa
Holetta
Holetta
Holetta
Werer
Werer
Werer
Holetta
Holetta
Holetta
Holetta
Holetta
Holetta


Kulumsa
Kulumsa
Kulumsa
Kulumsa
Kulumsa
Kulumsa
Kulumsa
Kulumsa
Kulumsa
Adet










Annex 2


Durum Wheat Varieties Tested and Released in Ethiopia Since 1967

Variety Origin Year released Areas of adaptation

Marou DZARC 1967 Akaki, Gimbichu, and similar areas
Arendato DZARC 1967 Akaki, Gimbichu, and similar areas
Cocorit-71 CIMMYT 1976 Ada, Akaki, and Gimbichu areas
Gerardo CIMMYT 1976 Akaki, Gimbichu, and Ada areas
Ld-357 USA 1979 Gimbichu and similar areas
Boohai CIMMYT 1982 Ada, Lume, and well-drained areas
Foka CIMMYT 1993 Ada, Akaki, Ambo, and Arsi Robe
Kilinto DZARC 1994 Ada, Akaki, Ambo, and drought-prone areas
Bichena DZARC 1995 Bichena and similar areas
Arsi Robe DZARC 1996 Arsi Robe and similar areas
Sinana DZARC 1996 Sinana and similar areas
Quami DZARC 1994 Koka, Alem Tena, and similar areas
Asassa DZARC 1997 For Asassa and similar areas
Ginchi DZARC 1999 For Ginchi and similar areas

Note: DZARC Debre Zeit Agricultural Research Center, Ethiopia.


Annex 3


Bread Wheat Varieties Presently in Use in Ethiopia

Location where Year Maturity Altitude
Variety selected released (days) (m)
Dereselign Debre Zeit 1974 144 1,650-2,200
K6290 Bulk Kulumsa 1977 128-131 1,800-2,200
K6295-4A Kulumsa 1980 128-131 1,900-2,400
ET-13.A2 Holetta 1981 107-149 2,200-2,700
Pavon 76 Werer 1982 120-135 750-2,200
Mitikie (HAR 1709) Kulumsa 1993 125-135 2,000-2,600
Wabe (HAR 710) Kulumsa 1994 120-140 <2,200
Kubsa (HAR 1685) Kulumsa 1994 120-140 2,000-2,600
Galama (HAR 604) Kulumsa 1995 120-155 2,200-2,800
Abola (HAR 1522) Kulumsa 1997 128-131 2,200-2,700
Magal (HAR 1595) Kulumsa 1997 113-124 <2,200
Tusie (HAR 1407) Kulumsa 1997 125-130 2,200-2,500
Tura (HAR 1775) Kulumsa 1999 120-149 2,200-2,700
Katar (HAR 1899) Kulumsa 1999 110-134 2,000-2,400
Shinna (HAR 1868) Adet 1999 100-120 1,800-2,500










Annex 2


Durum Wheat Varieties Tested and Released in Ethiopia Since 1967

Variety Origin Year released Areas of adaptation

Marou DZARC 1967 Akaki, Gimbichu, and similar areas
Arendato DZARC 1967 Akaki, Gimbichu, and similar areas
Cocorit-71 CIMMYT 1976 Ada, Akaki, and Gimbichu areas
Gerardo CIMMYT 1976 Akaki, Gimbichu, and Ada areas
Ld-357 USA 1979 Gimbichu and similar areas
Boohai CIMMYT 1982 Ada, Lume, and well-drained areas
Foka CIMMYT 1993 Ada, Akaki, Ambo, and Arsi Robe
Kilinto DZARC 1994 Ada, Akaki, Ambo, and drought-prone areas
Bichena DZARC 1995 Bichena and similar areas
Arsi Robe DZARC 1996 Arsi Robe and similar areas
Sinana DZARC 1996 Sinana and similar areas
Quami DZARC 1994 Koka, Alem Tena, and similar areas
Asassa DZARC 1997 For Asassa and similar areas
Ginchi DZARC 1999 For Ginchi and similar areas

Note: DZARC Debre Zeit Agricultural Research Center, Ethiopia.


Annex 3


Bread Wheat Varieties Presently in Use in Ethiopia

Location where Year Maturity Altitude
Variety selected released (days) (m)
Dereselign Debre Zeit 1974 144 1,650-2,200
K6290 Bulk Kulumsa 1977 128-131 1,800-2,200
K6295-4A Kulumsa 1980 128-131 1,900-2,400
ET-13.A2 Holetta 1981 107-149 2,200-2,700
Pavon 76 Werer 1982 120-135 750-2,200
Mitikie (HAR 1709) Kulumsa 1993 125-135 2,000-2,600
Wabe (HAR 710) Kulumsa 1994 120-140 <2,200
Kubsa (HAR 1685) Kulumsa 1994 120-140 2,000-2,600
Galama (HAR 604) Kulumsa 1995 120-155 2,200-2,800
Abola (HAR 1522) Kulumsa 1997 128-131 2,200-2,700
Magal (HAR 1595) Kulumsa 1997 113-124 <2,200
Tusie (HAR 1407) Kulumsa 1997 125-130 2,200-2,500
Tura (HAR 1775) Kulumsa 1999 120-149 2,200-2,700
Katar (HAR 1899) Kulumsa 1999 110-134 2,000-2,400
Shinna (HAR 1868) Adet 1999 100-120 1,800-2,500




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