Priority Setting in Agriculture Research:
A brief conceptual background
||Participatory approaches; Policies/Programmes.
||Manicad, G. (1997), "Priority Setting in Agricultural Research:
A brief conceptual background." Biotechnology and Development Monitor,
No. 31, p. 26.
National and international research institutes and NGOs have a growing
interest in structured and more transparent methods of priority setting.
In practice, they increasingly face similar problems in priority setting.
Aside from selecting and applying appropriate methods, they have to ensure
that various stakeholders are well represented. This is crucial for the
results and implementation of identified priorities.
Until the 1980s, priority setting for agriculture research involved
less transparent and structured procedures. Due to privatization policy
in the 1990s the budget for public research has decreased significantly.
Additionally, there has been growing pressure to show research results
to justify expenditure. Since then, administrators have increasingly faced
more openly expressed, sometimes conflicting demands of producers, agri-business,
consumers, scientists, donors and politicians. Hence, there is a demand
for new methods to assist in priority setting.
While there is growing attention for priority setting procedures, agricultural
research programmes are considering the integration of biotechnology in
their research programmes. Although there are a few examples in priority
setting in biotechnology, its relevance needs to be assessed in the broader
context of agriculture research. Therefore, this article looks at priority
setting in general agricultural research.
Literally, prioritizing is choosing what has to be done first. Resource
allocation is the usual over-riding economic objective of priority setting.
The political rationale of structured and transparent procedure(s) includes:
Many donors increasingly look at research as an instrument of social policy.
For example, food security and the criteria of participation of small-scale
farmers in setting the research agenda are imposed on more traditional
priority issues such as economic, scientific, technical and management
consensus building within and amongst different institutes;
enhancing accountability amongst policy makers, researchers and end-users;
motivating people's performance by relating their inputs to project results;
helping donors in project assessment.
Priority setting is a process of choosing between alternative sets
of research activities on the basis of which would best meet the overall
programme objectives. The process generally involves: (1) defining problems
and solutions; (2) identifying criteria and selection method(s); (3) assessing
and comparing technology alternatives; and finally (4) approving and implementing
the best alternatives based on available funds.
Essential to this process is information to define context, problems
and solutions. Various information is gathered from and assessed by potential
actors in the priority setting. For instance, farmer-oriented research
needs to integrate technical, socio-economical, cultural, and agro-ecological
The outcome of priority setting is largely determined by the actors
involved in the procedure. Therefore, the decision as to who will be included
and the manner and extent of their involvement greatly influences the results.
Identification of participants in priority setting is guided by research
objectives and proposed target groups. For example, the prime target group
of the International Potato Center (CIP) (see the article by Ghislain, Nelson and Walker) is mainly the National Agriculture Research Centres (NARCs);
and the NARCs are one of the main participants in CIP's priority setting.
The results might have differed significantly if their secondary target
group, the potato farmers, had participated in the exercise. Furthermore,
within farmer groups, awareness of their general characterization is crucial.
This could include expertise in local technology and experimentation, economic
resources, gender, farming objectives, and environment.
Top-down versus bottom-up
Priority setting procedures can be broadly classified into top-down
and bottom-up approaches. The top-down approach is dominated by officials
and experts based on government goals and technical information provided
at the programme levels (research leaders, scientists). National policies
are also greatly influenced by priorities and support from International
Agriculture Research Centres (IARCs) and by donors. Generally, at top
government levels, policies are oriented at funding of agricultural versus
non-agricultural research. At ministry levels, strategies are decided by
comparing the various agricultural research programmes as they contribute
to national goals. At the bottom-end of the hierarchy, at programme levels,
resources are allocated based on technical considerations. Ideally, key
actors from the ministry and top management of the research institute should
participate at the top and bottom levels of the hierarchy, to facilitate
communication and for a good match of policy and technical considerations.
The bottom-up approach essentially involves farmers, together with
scientists, in the priority setting of problems and solutions. Farmers'
needs, knowledge and priorities are solicited to formulate research agendas
and identify research priorities. In other methods such as Participatory
Technology Development (PTD), farmer participation goes beyond diagnosis
and priority setting. PTD includes farmer-led experiments, development
and evaluation. Although bottom-up approaches originate from NGOs, the
methodologies are now increasingly adapted by some NARCs and IARCs. Two
examples are briefly described.
Participatory Rural Appraisal (PRA) is a compilation of semi-structured
activities carried out by interdisciplinary teams in partnership with communities
and their local leaders. Although PRA deals with general community development,
it could be specifically designed for agriculture research. Farmers can
improve problem diagnosis and orient research to local issues and circumstances.
PRA's visual approaches, and the cross-checking with interdisciplinary
teams and community members can be an effective tool for implementing participatory
research and development. However, the approach can suffer from imbalance
The Interactive Bottom-Up (IBU) approach's (see the box in the article by Commandeur) main strength is the involvement of different actors (scientists,
farmers, governments, NGOs, donor) to a series of dialogues to assess problems
and prioritize solutions for small-scale farmers regarding biotechnological
innovations. Enhancing dialogue amongst different institutions is still
relatively pioneering work. The IBU approach utilizes interdisciplinary
perspectives to technology assessment and development, and assesses the
comparative advantage of biotechnology over other existing technologies.
However, IBU has its weaknesses. Firstly, although the IBU approach
proposes some sort of continuing involvement of farmers in technology generation,
this remains vague; both in concept and in practice. The IBU approach still
differentiates between farmers (as a source of information) and scientists
(as developers of technology). In other words, farmers communicate their
needs to scientists and the scientists, develop solutions for farmers.
Hence, farmers' involvement is largely of the contractual and consultative
types of participation. For the IBU approach, this type of dialogue and
engagement is dependent on the good-will of the scientists, as is true
for most participatory research methodologies.
Secondly, the IBU approach, as it has been adapted by the Special
Programme on Biotechnology of the Netherlands' Directorate General for
International Cooperation (DGIS), faces difficulties on how to respond
to farmers' problems that cannot or should not be solved by biotechnology.
For example, problems which are related to natural resource management
or community development. In this sense, institutional relations, to link
other development problems and activities effectively, need to be strengthened.
Methods of research priority setting
Once research problems are identified, there are several formally structured
methods to assist in assessing and prioritizing options. These methods
primarily deal with measurements, and do not substitute for the actors'
experiences and contextual judgement. The discussions within these procedures
are important in making decisions. Most of the methods described below
are more commonly used in top-down approaches, but can also be adapted
to bottom-up approaches. For instance, scoring and economic surplus methods
are increasingly used by some NGOs. In addition, there is a growing number
of government institutes and IARCs that use the PRA matrix ranking.
Scoring is easy to administer in a short time and does not require advance
quantitative skills. It is relatively transparent and can allow active
participation. Variations to the scoring method include the matrix ranking
and pair-wise ranking (comparing subjects or criteria in twos, using
all possible paired combinations to establish preference) are often used
in PRA and PTD. In this case, problems and solutions are prioritized based
on criteria which have been identified by farmers and are ranked accordingly.
Matrix ranking is often used in situations in which readily available objects
such as stones, leaves or seeds are used by farmers to represent variables,
quantity and preferences. This system is used to evaluate complex problems,
solutions and priorities.
Scoring. This is one of the two most commonly used quantitative
methods in agricultural research priority setting. This involves ranking
a set of research projects according to multiple criteria. Ranking results
from the total scores given to the projects.
However, in scoring and matrix ranking, problems can arise with regard
to how objectives and criteria are defined and valued. Additionally, a
set of selected criteria may be inconsistent. It is especially crucial
how ranks are validated and how the results are used to support decisions.
There are several limitations of this method. It requires substantial expenditure
in collecting, processing and analyzing economic and technical data. The
process does not allow for group decisions and lacks transparency. Furthermore,
it is not well-suited to ranking non-commodity research such as basic and
socio-economic research. However, to incorporate multiple objectives, economic
surplus can be combined with scoring.
Economic surplus. This is the other most commonly used method. It
is used to calculate internal rates of returns and net present values of
benefits generated from agricultural research projects. It can be used
to assess the benefits of research to specific commodities or to a broader
research programme. It can also be used to estimate the distribution of
research benefits to consumers and producers. The calculation of rates
of return to research can be compared to alternative public investments.
AHP has only very recently been introduced in agriculture research and
it therefore still needs to be fully tested. However, those who have used
AHP find the method promising. It is a sophisticated extension of the scoring
model. AHP recognizes bias and inconsistencies in subjective judgments.
Hence, it is said to be more suitable for situations in which much of the
data is subjective in nature, such as biotechnology. For instance, informed
guesses have to be made with regard to uncertainty in cost, benefit and
impact. AHP involves a transparent decision making process and allows for
participation. AHP can be used in combination with economic surplus and
mathematical programming to improve resource allocation. However, it can
be a very tedious process, especially when considering numerous alternatives
at each level. AHP also requires knowledge of linear algebra and measurement
Simulation models. In these models, mathematical relationships among
variables are exposed to different market scenarios to asses the best outcome.
These models can incorporate many factors that affect research priorities
such as multiple goals and socio-economic constraints. The major disadvantage
is that it requires a large investment in facilities. Additionally, data
requirements are more extensive than for other economic models.
Analytical Hierarchy Process (AHP). This involves comparative judgement
of alternatives by identifying different levels (hierarchy) in decision
making (see also the article by Ghislain, Nelson and Walker). Such decision levels include
the overall goals, criteria, and alternative sets of research projects.
Pair-wise ranking of each levels are made, after which alternatives are
Issues in priority setting
Given the diversity of needs and capacity of each institutions, there
is no prescribed single-model to use in priority setting. Users should
choose the methodology which suits them and their objectives best. Moreover,
some of the methodologies are not mutually exclusive and could complement
each other. At times, top-down and bottom-up approaches can use over-lapping
methods. For instance, PRA matrix ranking and economic surplus methods
are used in both approaches. However, due to their complexity and costs,
methods such as simulation models tend to exclude the users in bottom-up
approaches. The effectiveness of priority setting is enhanced by the appropriateness
of method(s) and essentially by how the methods are used.
Representation. As other Monitor articles in this issue illustrate,
representation in the priority setting exercise greatly influences the
outcomes. Not only among, but also within target groups there is differentiation
in interest. For example, better-off farmers with access to irrigation
and better soil quality would naturally have different criteria in crop
and varietal selection than resource-poor farmers managing different farming
systems. In PRA, the problems and solutions selected within a community,
are greatly dependent on the representation of the community members. PRA
tools that rank households according to socio-economic status, and tools
to assess gender activities may indicate but cannot address power relations.
Nevertheless, if not considered, outside interventions can reinforce power
relations. In terms of addressing social differentiation, PRA is not a
substitute for community empowerment activities. In this sense, scientists
could actively seek cooperation from other local institutions such as NGOs
and farmers' organizations.
Time, resources and impact. Priority setting is not a stand-alone
activity. Inevitably, research activity is to be judged by its results;
not solely by how well priorities are articulated. Priority setting exercises
involve investment costs in human resources and expertise, data gathering
and analysis. As the Chilean case suggests (see page 12), the information
needs have to be assessed in terms of their usefulness to the exercise.
For example, the time involved in the collection of a large amount of information
may erode the relevance of the data. Moreover, participatory procedures
could be tedious. In terms of cost-effectiveness, cost of farmers' involvement
needs to be compared with for example, cost of technology research and
development and the corresponding gains or losses when farmers adapt or
reject the technology accordingly.
Editor, Biotechnology and Development Monitor
J. Alston, G.W. Norton and P.G. Pardey (1995), Science Under Scarcity:
Principles and practice for agricultural research evaluation and priority
setting. Ithaca and London: Cornell University Press.
J. Bunders and J. Broerse (eds.) (1991), Appropriate Biotechnology
in Small-scale Agriculture: How to reorient research and development.
Oxon: CAB International.
C. Falconi (1997), Methods for Assisting Priority Settings in Agricultural
Biotechnology Research. Draft Document, ISNAR, The Hague.
G. Manicad (1996), A Training Module: Participatory research.
ISNAR: The Hague.
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