National FSA Training
Module 10:Adoption likelihood analysis
Module 10 Adoption likelihood analysis
Objectives
Objective of the module is to enable participants to understand:
 Importance of variability in research domain
 Factors influencing technology adoption
 Ways of predicting of predicting maximum rate of adoption
 Ways to increase maximum rate of adoption
Content
10.1
10.2
10.3
10.4
10.5
10.6
Introduction
Factors influencing technology adoption
Dimensions of technology adoption likelihood analysis (the four A's)
Estimating potential or maximum adoption rates
The mathematical relations of predicting maximum adoption
Ways to increase potential adoption rates
Key words
Adaptability analysis, variability, research domain, technology adoption, maximum rate of
adoption, affordability, acceptability, attractiveness, accessibility, flexible recommendations
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10.1
Module 10:Adoption likelihood analysis
Introduction
The major concern of Farming Systems Research approaches since its inception into our
research programmes has been to ensure encompassing farmers' experiences and environmental
circumstances in all research stages. Thus the assessment of circumstances in the target area,
farmer management, resource quantities and qualities as well as some policy issues need to be
thoroughly analysed. It is therefore important that the FSA envisages incorporating farmers
from the very start of the technology development process (i.e. from diagnostics through
adoption to evaluation). In this manner, researchers may be able to avoid or minimise the
incidence of research results that perform poorly on-farm or the rejection of on-station
technologies that might have performed well on-farms but were never released.
A research domain, which can be chosen on biophysical, socio-economic or political
characteristics, recognises the fact that farmers' circumstances are variable and this should be
taken into consideration during the research process (Stroup et al, 1991). For appropriate and
sustainable agricultural innovation it is essential that efforts be made to ensure that
recommended agricultural technologies will be adopted by the intended farmer categories
within the recommendation domains. The eventual adoption of the recommended technologies
should be the constant concern of the research in all its various phases. Adoption likelihood
analysis is a strong tool used before and during the trial process to foster maximum likelihood of
recommended technologies to be adopted.
10.2
Factors influencing technology adoption
Agricultural technologies are location specific and react to environmental changes. The
characteristics if the intended user group, economic supports system and political or
administrative conditions surrounding the target area influence the scaling up of a technology.
This calls for a need to draw attention and assess these factors for better understanding of the
likelihood of intended technologies to be adopted and scaled up. Development of user-oriented
technology has to be well targeted to farming system zones and socio-economic categories. Also
the economic support systems and politico-administrative conditions for widespread adoption
have to be indicated. Therefore, when technologies are planned and tested, priorities must be set
based on potential benefits and risks for different groups of farmers and the ease with which
farmers may be able to adopt them. The next sections present some of the major factors that
may influence adoption of technologies. These factors are grouped in four sections: farmer
characteristics (see also Module 4), farm resources, compatibility with farming system and the
political-economic environment.
(a) Characteristics of farmers
 Wealth: Farmers with more resources (land, labour, capital) generally take advantage of a
new technology. Wealthier farmers have better access to extension information and
financial resources (own funds or credit) and can afford to take some risks.
 Age: A farmer's age may influence adoption in one of several ways. Older farmers may have
more experience, resources, or authority that would allow them more possibilities for trying
a new technology. On the other hand, it may be that younger farmers are more likely to
adopt a new technology, because they have had more schooling than the older generation or
perhaps they have been exposed to new ideas elsewhere.
 Gender: Women often have a specific role within the farmer household. They have certain
tasks, grow specific groups and/or have well defined roles in livestock keeping. Because
women play a key role in most agricultural systems, it is important that adoption studies
consider the degree to which a new technology reaches and affects women farmers (see also
Module 4 and Module 7).
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Module 10:Adoption likelihood analysis
Education: Many adoptions studies show a relationship between technology adoption and
the educational level of the farmer. The more complex the technology, the more likely it is
that education will play a role.
Ethnic, religious and community factors: In many cases a technology is introduced to an
area that includes farmers of different customs and traditions. These differences may be
most notable between communities or between members of several groups living in the
same community. Adoption patterns may differ among these groups.
Production goals: The introduction of new crop varieties to resource-poor farmers requires
an understanding of their food consumption patterns and marketing behaviour, which is
largely related to the socio-economic position of a household. Farmers’ major production
goals can be early harvesting (to overcome food shortage period), storage (food security)
and marketing (to get cash income). These goals can strongly determine the adoption of
technologies. If farmers market a considerable proportion of their harvest, then the
characteristics for market acceptability should be identified.
(b) Farm resources
 Farm size: Farm size is a common variable examined in adoption studies and is often a
good proxy for wealth. It's often assumed that larger-scale farmers will be more likely to
adopt a technology, especially if the innovation requires an extra cash investment. On the
other hand, certain technologies are more appropriate for the intensive management
characteristics of smaller farms (or at least of farms with a higher ratio of labour to land).
Farm size may also be related to access to credit facilities, which may facilitate adoption.
 Labour: Technologies have different labour characteristics; some save labour, while others
significantly increase it. In planning adoption studies researchers need to pay sufficient
attention to labour-related issues: changes in labour requirements, timing of activities and
peak periods during the year, labour availability within the household, off farm
employment, availability of hired labour.
 Equipment and machinery: If a technology involves equipment or machinery, the degree of
adoption may depend on the farmers who have it or are able to acquire it.
 Land tenure: Land tenure can also affect farmers' ability to adopt innovations. An adoption
study should find out whether the recommendation is suited for farmers without secure
access to land. In many cases, renters or sharecroppers will be less interested in
technologies that have long-term effects, such as soil fertility maintenance, because they do
not have any guarantee to use the land in the future. Sometimes tenants do not have a free
choice of crops and varieties. For instance, they may be obliged to plant varieties that
provide crop residues for the landowner's animals.
(c) Compatibility with farming system
Technologies must be compatible with the farming system at large, if they are to find
acceptance. Often a technology as such appears to make sense on its own, but is still rejected,
not because of any intrinsic disqualification, but because it cannot be incorporated in the
farming system. Reasons for non-compatibility can be:
 Labour: it is important to know if the labour demand of a new technology coincides with a
particular busy time of the year or could take advantage of a period when labour is
available. It's important to remember that the labour profile for a certain farming system is
determined not only by operations on the target crop but also by demands from various
other activities of farmer households.
 Other crops: intercropping and relay cropping are common practices in many farming
systems. New varieties or practices for one crop may thus have to be compatible with the
presence and management of other crops.
 Agriculture and livestock interaction: Crop production can be important for animal
production and vice versa. New crop management techniques may have an effect on the
production of by-products destined for animals. The use of damaged or spoiled grains or
tubers for animal feed may diminish farmers' interest in certain crop protection
technologies.
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Module 10:Adoption likelihood analysis
Biological circumstances: the weeds, diseases, and insect pests prevalent in the area or in
specific fields may affect the adoption of technology. New varieties for example may be
more or less susceptible to diseases or insect pests, and certain management practices (such
as planting time) may reflect farmers' attempts to avoid these problems.
Soils: Land quality and soil type may be important factors influencing the acceptance of a
new technology. Selection of sites for on-farm experiments should take into account
variations in soil type or land type. Not only may management practices differ by type of
soil, but other conditions, such as slope or moisture retention capacity, are often important
as well.
Rainfall patterns: These can limit the crops that can be grown and also regulate planting and
harvesting schedules. The possibility of drought or flooding or seasonal temperature
changes can make farmers reluctant and wary about investing in some technologies.
(d) Political-economic environment
 Information and training: For farmers to adopt a technology they must first know about it.
The information may come from many sources e.g. the extension services, researchers,
other farmers, policy makers, radio, television, newspapers or magazines, extension
bulletins, field days/tours, farmers exchange visits, agricultural shows etc.
 Credit: This is also a very important factor in determining adoption. If a recommendation
implies a significant investment for farmers, a credit program may facilitate adoption. If the
majority of adopters use credit to acquire the technology, then there is a strong indication of
credit's role in diffusing the technology. Similarly, farmers who do not adopt may complain
of a lack of cash or credit as the principal factor limiting their adoption.
 Supply system: If a recommendation/technology involves the use of purchased inputs,
adoption may be limited because effective supply systems and maintenance services are not
in place or farmers do not have information on prices, selling points, available stock, the
reliability of the retailer and the quality of the input.
 Marketing: If markets are inefficient, there is hardly any incentive to invest in improved
technology. Seasonal variation in market prices may also affect the acceptability of
technologies that change the timing of harvest (e.g. a technique that allows earlier planting).
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10.3
Module 10:Adoption likelihood analysis
Dimensions of technology adoption likelihood analysis (the four A's)
To assess whether a technology, once developed, may likely be adopted, one needs to collect
data in different domains or dimensions. According to Mafuru and Van de Meerendonk (1999),
both adoption likelihood analysis (ex-ante) and adoption analysis (ex-post) have to cover the socalled 4 A's:
 Acceptability
 Affordability
 Accessibility
 Attractiveness.
Acceptability
This refers to the technical characteristics of the recommended technology i.e. does the
recommended technology technically delivers what it promised during the trial. Several issues
or questions should be considered before embarking on technology development. For example,
does the technology likely to deliver what is intended? Is the proposed variety likely to be
superior to the existing ones? In other words, how does the innovation expected to perform
compared to the existing alternatives. Is only when there is an indication of possible profits to
the end user that the target groups are likely to adopt the technology proposed for innovation?
At later stages, the achievement of this can be assessed using the Rapid Technology Assessment
tool (See Module 18 - Adoption and Impact Assessment of Agricultural Technologies).
Affordability
This refers to the economic characteristics of the recommended technology i.e. can the intended
target group within the recommendation domains afford the costs involved in acquiring and
maintaining the recommended technology. Is the investment required not prohibitive? Are
recurrent costs not required at the farmers' liquidity problem? For example, is the intended
recommended wheelbarrow in practise not too expensive and does it require too many repairs?
Or does the recommended rice variety in practise require investments from the farmers at the
moment that the household has no money to spend? During the adoption stage, this can be
assessed using the Household Income and Expenditure analysis tool.
Accessibility
This refers to the local availability of the recommended technology i.e. can the target group
within the recommendation domains avail of the technology. For example, is their sufficient
local supplying of the recommended wheelbarrows and can local craftsmen do repairs
(sufficient availability of spare parts)? Is the planting material as well as other inputs for the
recommended rice variety sufficiently locally available at the moment that the farmer needs it?
Does the farmers have the information and knowledge on how to apply the new technology?
Attractiveness
This refers to the economic competitive position of the recommended technology visa versa the
practised technology, i.e. does the farmers get substantial extra revenue if he/she decides to
innovate. Farmers who are asked to innovate are considering the marginal rate of return of the
innovation. In case they cannot compare with an existing practised technology directly, the
farmers will calculate acceptable minimum rates of return using the expected output of the new
technologies. Partial budget analysis is used to measure the attainment of this consideration at
the adoption stage.
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10.4
Module 10:Adoption likelihood analysis
Estimating potential or maximum adoption rates
The likelihood that farmers will adopt a technology depends on how well the technology is
adapted to the local conditions. These local conditions can be classified into three categories:
(a) production environment (i.e. the biophysical conditions)
(b) farmer categories of the recommendation domain and
(c) production goals of the farmers.
Production environment
This includes all factors, which directly determine crop and livestock production, such as soil
types and soil fertility, pests and diseases, and also management practices. Together, they
compose the production environment of the crop.
Production Environment I
Technology
Production Environment II
The number of production environments depends on the level of aggregation (or: level of
analysis). Considering a large area like say the whole country, a tremendous number of
production environments of a certain crop can be distinguished. Within one agro-ecological
zone, the number of production environments is often limited to, say, less than five. New
technologies have to fit these production environments if a good rate of adoption is anticipated.
Farmer category
Target groups can further be sub-divided into groups or categories depending on criteria that are
decisive for their socio-economic position and utilisation of the technology output (see Module
4).
Production goal
Production goals of farmers determine to a large extent their technology choice. They form an
indirect influence on crop production. Production goals depend on the socio-economic context
in which the farmers operate, and on their individual ideas and interests. This context includes
farmers’ access to the means of production, family consumption, and cash demands, gender, age
and other farmer characteristics and relationships between farmers. These factors influence
production goals directly.
Production Goal I
Technology
Production Goal II
Production Goal III
Interactions
Producer categories and production environments are interrelated. One category of farmers may
‘occupy’ a favourable production environment because of the history of human settlement and
the inheritance rules. Another category, e.g. households that recently settled in the village, may
have access to less favourable production environments. Resource-rich households may adjust
an unfavourable production environment into a favourable one by management practices and
application of inputs. Adoption of technologies depends therefore not only on the number of
production environments or farmer categories, but also on the systematic combination between
the two.
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Technology
10.5
Module 10:Adoption likelihood analysis
Marketing
Resource rich farmers (20%)
Food security
Resource poor farmers (50%)
Early harvest
Female farmers (30%)
The mathematical relations of predicting maximum adoption
In this section we attempt to show that it is possible to calculate the maximum adoption rate of a
technology before any action is undertaken to test or diffuse it. This priority estimation needs a
good understanding of the farmer population and the production environments for which the
technology is meant to be. Calculation of the maximum adoption rate facilitates priority setting
of research and extension. It reveals that, in many cases, one-dimensional or blanket
recommendations have low maximum adoption rates, and that flexible recommendations are
needed to obtain more satisfactory rates of adoption.
(a) One-dimensional functions
Function 1: Technology adoption is a function of the production environments in the target zone
in which the technology is applicable.
MAR = Ef(1,n)
MAR = Maximum Adoption Rate (%)
Ef(1,n) = Frequency of production environments
Example: If there are three production environments for maize with an importance of 20 (A),
30 (B) and 40% (C) in a target zone, the maximum adoption rate for a B-environment-specific
variety is 30%.
Production Environment A (20%)
Technology
Production Environment B (30%)
Production Environment C (40%)
MAR = E (b environment) = 30%
Example: Within one agro-ecological zone there are two distinctly different, production
environments for maize (homesteads and annual crop fields). A survey revealed that 60% of all
farmers in the zone grow maize in their homesteads and 40% in their annual crop fields. A
blanket recommendation for fertiliser application, derived from monocropped maize trials in
annual crop fields will therefore have a maximum adoption rate of 40%.
Production at homesteads (60%)
Fertiliser in maize
Production in annual crop fields (40%)
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Module 10:Adoption likelihood analysis
MAR = E (annual crop fields) = 40%
Function 2: Technology adoption is a function of farmer categories that produce a specific crop
in the target zone.
MAR
= Cf(1,n)
MAR = Maximum Adoption Rate (%)
Cf(1,n) = Frequency of farmer categories
Example: If there are two farmer categories (A and B) with a frequency of 30% and 70%
respectively, the maximum adoption rate for a category A-specific variety is 30%.
Farmer category A (30%)
Technology
Farmer category B (70%)
MAR = C (category A) = 30%
Example: Within one agro-ecological zone three farmer categories can be defined. All three
categories grow maize, but for specific purposes. Resource-rich farmers (20%) produce for
marketing (long maturing) , resource-poor farmers (50%) have the goal of food security (long
maturity as well) and female farmers (30%) wish to have an early harvest to fill the gap before
other crops mature. A blanket recommendation of a long cycle variety that fits both resource
rich and resource poor for marketing and food security respectively has therefore a maximum
adoption rate of 70%.
Technology
Marketing
Resource rich farmers (20%)
Food security
Resource poor farmers (50%)
Early harvest
Female farmers (30%)
MAR = C (resource rich) + C (category A) = 20% + 50%
Function 3: Technology adoption is a function of production goals of farmers related to a
specific group in the target zone.
MAR = Gf(1,n)
MAR = Maximum Adoption Rate (%)
Gf(1,n) = Frequency of production goals
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Module 10:Adoption likelihood analysis
Example: If there are two equally (i.e. 50% each) important production goals of farmers, the
maximum adoption rate of a goal-specific variety is 50%.
Goal category A (50%)
Maize variety
Goal category B (50%)
MAR = C (category A) = 30%
(b) Two-dimensional functions
Function 4: Technology adoption is a function of the relation between farmer categories and
production goals.
MAR = Cf(1,n) x Gf(1,n) /100
MAR = Maximum Adoption Rate (%)
Cf(1,n) = Frequency of farmer categories
Gf(1,n) = Frequency of production goals
Example: There are two farmer categories with three production goals:
Technology X is applicable for production goals 1 and 2
Category A = 30%
Category B = 70%
G1 = 20%
G2 = 80%
G3 = 0%
G1 = 60%
G2 = 0%
CAG rate = (20x30) + (80x30) / 100 = 30%
CBG rate = 42%
Total MAR = 30 + 42 = 72% (overall)
G3 = 40
A technology which is applicable for two goals will have a maximum adoption rate of 30% by
category A farmers and 42% by category B farmers. This accumulates to a total maximum
adoption rate of 72%.
Function 5: Technology adoption is a function of the relation between farmer categories and
production environments.
MAR = Cf(1,n) x Ef(1,n) /100
MAR = Maximum Adoption Rate (%)
Cf(1,n) = Frequency of farmer categories
Ef(1,n) = Frequency of production environments
Example: There are two farmer categories with two production environments:
Technology X is applicable for production environment 2
Category A = 30%
Category B = 70%
E1 = 90%
E2 = 10%
E1 = 30%
CAE rate = (10x30) / 100 = 3%
CBE rate = 49%
Total MAR = 3 + 49 = 52%
E2 = 70%
A technology which is applicable for production environment 2 only will have a maximum
adoption rate of 3% by category A farmers and 49% by category B farmers. This accumulates
to a total maximum adoption rate of 52%.
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Module 10:Adoption likelihood analysis
(c) Three-dimensional function
Function 6: Technology adoption is a function of the relation between farmer categories,
production goals and production environments.
MAR = Cf(1,n) x Gf(1,n) x Ef(1,n) /10 000
MAR = Maximum Adoption Rate (100)
Cf(1,n) = Frequency of farmer categories (%)
Gf(1,n) = Frequency of production goals (%)
Ef(1,n) = frequency of production environments (%)
Example: There are two categories of farmers with three production goals and three production
environments:
Category A = 30%
G1 = 20%
G2 = 70%
E1 = 70%
E2 = 0%
G3 = 10%
E3 = 30%
Category B = 70%
G1 = 60%
G2 = 0%
E1 = 10%
E2 = 50%
G3 = 40%
E3 = 40%
CAG1E1 rate = (30 x 20 x 70) /10000 = 12.6%
CAG1E2 rate = (30 x 20 x 0) /10000 = 0.0%
CAG1E3 rate = (30 x 20 x 30) /10000 = 1.8
CBG1E1 rate = (70 x 60 x 10) /10000 = 4.2%
CBG1E2 rate = (70 x 60 x 50) /10000 = 21.0%
CBG1E3 rate = (70 x 60 x 40) /10000 = 7.1%
CAG2E1 rate = (30 x 70 x 70) = 14.7%
CAG2E2 rate = (30 x 70 x 0) / 10000 = 0.0%
CAG2E3 rate = (30 x 70 x 30) /10000 – 6.3%
CBG2E1 rate = (70 x 0 x 10) /10000 = 0.0%
CBG2E2 rate = (70 x 0 x 50) /0000 = 0.0%
CBG2E3 rate = (70 x 0 x 40) /10000 = 0.0%
CAG3E1 rate = (30 x 10 x 70) /10000 = 2.1%
CBG3E1 rate = (70 x 40 x 10) /10000 = 2.8%
CAG3E2 rate = (30 x 10 x 0) /10000 = 0.0%
CBG3E2 rate = (70 x 40 x 50) /10000 = 14.0%
CAG3E3 rate + (30 x 10 x 30) /10000 = 0.9%
CBG3E3 rate = (70 x 40 x 40) /10000 = 11.2%
If the technology is applicable for production goals 1 and 2 and for production environments 1 and 2,
then MAR = 52.2% (12.6 + 14.7 + 4.2 + 21.0)
If the technology is applicable for production goal 2 and production environment 1, then MAR = 14.7%.
If the technology is applicable for production goals 2 and 3 and production environment 3 then MAR =
18.4% (6.3 + 0.9 + 11.2).
Summary on how to calculate Maximum Adoption Rate
The following procedure allows you to calculate the Maximum Adoption Rate of the technology
you intend to test:
1. Define the adoption rate you wish to realise.
2. Define your target zone.
3. Define the number of production environments in which your target crop is grown in
your target zone. Estimate their frequencies.
4. Define the production goals of your target crop
5. Define the farmer categories that produce your target crop and estimate their relative
importance.
6. Relate production goals to farmer categories
7. Relate production environments to farmer categories
8. Calculate your Maximum Adoption Rate.
Calculation of the Maximum Adoption Rate depends to a large extend on the available
information you have. It stands to reason that equation six estimates the MAR best. You can
adapt the above procedure to the information you dispose of and choose the corresponding
equation (1 – 6).
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10.6
Module 10:Adoption likelihood analysis
Ways to increase potential adoption rates
In situations of systematic interactions between production environments, goals and farmer
categories, one-dimensional technologies, or blanket recommendations, have low Maximum
Adoption Rates. To increase the Maximum Adoption Rates flexible recommendations are
needed. Flexible recommendations combine one-dimensional technologies. The Maximum
Adoption Rate of a flexible recommendation is the cumulation of the Maximum Adoption Rates
of the one-dimensional technologies.
Example: Variety A has a MAR of 23%, variety B has a MAR of 34% and variety C has a MAR
29%. The MAR of a flexible recommendation, combining the three varieties, is 86%.
Flexible recommendations present one-dimensional technologies in such a way that farmers can
make their choice. In fact, the last step in the selection of technology is left to the farmer. He or
she only needs to be provided with the right information. This can be achieved by presenting the
‘profiles’ of each technology (descriptions of what a farmer can expect from each technology),
or by presenting ‘if ………… then ………….’ selection advise.
Table 10.x
Maize variety profiles
Variety/Characteristics
Length of cycle (days)
Resistance to streak virus
Resistance to
Yield on poor soils
Yield on fertile soils
Suitability for roasting
Suitability for ugali
Suitability for marketing
Table 10.x
KITO
90
very low
very low
low
medium
very good
medium
not good
TMV-1
100
very good
medium
low
medium
good
good
medium
STAHA
110
good
medium
low
medium
medium
very good
medium
KILIMA
120
low
high
high
high
not good
very good
good
UCA
140
low
medium
very high
not good
very good
good
good
Maize variety selection advice
If you ………….
have a field with poor soil fertility
have a field with good soil fertility
have no time for early weeding
then sow ….
Kilima
Kilima, STAHA or UCA
TMV-1, Kilima, UCA,
STAHA
need an early, average maize harvest
TMV-1
want to produce early maize for roasting Kito or TMV-1
want to produce maize for the market
Kilima or UCA
want to intercrop in annual fields
Kilima , TMV-1 or STAHA
want to plant maize in homegarden
Kilima or UCA
But don't sow ….
Kito, TMV-1 or STAHA
Kito or TMV-1
Kito
Kilima or UCA
Kilima or UCA
Kito
Kito
Kito
Maximum Adoption Rates, as calculated above, allow a scientist or extensionist to predict the
potential success of a technology introduction. It is a tool which can be used for improving
research and extension efficiency. It can also be used to assess the achieved adoption rate
compared to the expected one. Because of the inherent linear nature of the calculation it should
not be used beyond the scope of priority setting and adoption assessment.
Importance of calculation of Maximum Adoption Rates
Calculations of MAR before the research is fully undertaken are useful for the following
purposes:
 low potential technologies can be identified and eliminated;
 good potential technologies can be selected and targeted;
 expectations of future adoption become more realistic;
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Module 10:Adoption likelihood analysis
adoption evaluations have a better reference of what was expected at introduction.
Most scientists and extension staff do not have any idea of the potential adoption of the
technology they wish to diffuse. Basic information about production environments, goals and
farmer categories is often lacking. It is, however, strongly recommendable that any commodity
scientist or extension staff disposes of this basic information.
One-dimensional technologies often have a low Maximum Adoption Rate. On the contrary,
flexible recommendations have much higher adoption rates. The final step in the selection of
appropriate technologies is left to the farmer. By increasing farmers’ choice research and
extension will become more effective. If adoption rates are criteria for efficiency of research
and extension then flexible recommendations will not only benefit the farmers, but will also
satisfy donors and hence guarantee funding of research and extension.
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References
Hildebrand P.E. and J.T. Russell (1996). Adoptability analysis. A method for the design,
analysis and interpretation of on-farm research-extension. Iowa State University Press/Ames.
Stroup W.W., P.E. Hildebrand, C.A. Francis (1991). Farmer participation for more effective
research insustainable agriculture. Staff paper series SP91-2, September 1991. Food and
Resource economics department. Institute of Food and Agricultural Sciences. University of
Florida. Gainesville, Florida 32611.
Madulu R. (1998). On-farm introduction of maize streak virus disease tolerant varieties in
Sukumaland. Progress report presented during IPR 1998, Lake Zone.
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