Priority Setting in Agriculture Research:
A brief conceptual background
Gigi Manicad
Keywords:  Participatory approaches; Policies/Programmes.
Correct citation: 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 merits.
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 information.
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 in representation..
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.
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. 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 theory.

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.

Gigi Manicad

Editor, Biotechnology and Development Monitor

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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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