© FAO/Alessia Pierdomenico
Agrifood systems cover the journey of food from farm to table – including when it is grown, fished, harvested, processed, packaged, transported, distributed, traded, bought, prepared, eaten, and disposed of. They also encompass non-food products that constitute livelihoods and all of the people as well as the activities, investments and choices that play a part in getting us these food and agricultural products. In the FAO Constitution, the term “agriculture” and its derivatives include fisheries, marine products, forestry, and primary forestry products (FAO, 2022).
Boundary organizations perform brokerage activities to better connect policymakers with knowledge producers. They can take many forms within either the policymaking system, the knowledge system, or at arm’s length from both sides. They can perform different functions: disseminating, translating, synthesizing and communicating evidence for policy; managing requests for evidence; facilitating access to evidence; training knowledge producers and decision-makers for evidence-informed policymaking; building partnerships; rewarding policy impact; and creating processes and posts for evidence for policy.24
Evidence in general refers to “data, information, and knowledge from multiple sources, including quantitative data such as statistics and measurements, qualitative data such as opinions, stakeholder input, conclusions of evaluations, as well as scientific and expert advice”.25
Evidence-informed decision-making (EIDM) emphasizes that decisions should be informed by the best available evidence from research, as well as other factors such as context, public opinion, equity, feasibility of implementation, affordability, sustainability, and acceptability to stakeholders. It is a systematic and transparent approach that applies structured and replicable methods to identify, appraise, and make use of evidence across decision-making processes, including for implementation (WHO, 2021).
Food security is the situation that exists when all people, at all times, have physical, social, and economic access to sufficient, safe, and nutritious food that meets their dietary needs and food preferences for an active and healthy life. Four traditional dimensions can be identified (food availability, economic and physical access to food, and food utilization), as well as the two additional dimensions of agency and sustainability that are proposed by the HLPE-FSN but are not formally agreed upon by FAO or other bodies, nor is there an agreed language on the definition (HLPE, 2020; FAO, IFAD, UNICEF, WFP and WHO, 2021).
Interdisciplinary science refers to the specific combination of different fields and/or disciplines to frame research questions, to observe, analyze, and explain a problem. Interdisciplinary science aims at cross-fertilization and mutually enriching collaboration between different types of expertise, within and between disciplines. True interdisciplinary collaboration treats all participating disciplines on an equal footing and develops approaches which transcend established scientific fields. The further apart some disciplines are, the more challenging interdisciplinary science is. Empirically, a genuinely interdisciplinary collaboration between the natural sciences, the social sciences and the humanities is still more the exception than the norm today (UNESCO, 2017).
Knowledge is central to any discussion of learning and may be understood as the way in which individuals and societies apply meaning to experience. It can therefore be seen broadly as the information, understanding, skills, values and attitudes acquired through learning. As such, knowledge is linked inextricably to the cultural, social, environmental and institutional contexts in which it is created and reproduced.26
Knowledge management refers to the systematic processes, or range of practices, used by organizations to identify, capture, store, create, update, represent and distribute knowledge for use, awareness and learning across the organization.27
Science signifies the enterprise whereby humankind, acting individually or in small or large groups, makes an organized attempt, by means of the objective study of observed phenomena and its validation through sharing of findings and data and through peer review, to discover and master the chain of causalities, relations or interactions; brings together in a coordinated form subsystems of knowledge by means of systematic reflection and conceptualization; and, thereby furnishes itself with the opportunity of using, to its own advantage, understanding of the processes and phenomena occurring in nature and society.28
Science communication describes a variety of practices that transmit scientific ideas, methods, knowledge and research to nonexpert audiences in an accessible, understandable or useful way (FAO, 2023).
Science diplomacy refers to the use of scientific collaborations among nations to address common problems and build constructive international partnerships. It encompasses various activities and initiatives that involve scientific expertise and resources to foster diplomatic relations and achieve foreign policy objectives. Science diplomacy includes the following three dimensions.29
Science–policy interface has become increasingly prominent within the UN sphere over the past decades. Although various definitions of the term exist in the literature30, an SPI is generally understood as an institutional arrangement, forum, process or organization whose task is to facilitate the dialogue between knowledge (including scientific advice) and policymaking. This is consistent with the FAO definition of science–policy interface as organized dialogue between scientists, policymakers and other relevant stakeholders in support of inclusive science-based policymaking (FAO, 2022).
Science–policy model is a mental model or concept that describes idealized modes of interaction between scientists, policymakers, and other stakeholders in the context of an SPI. A science–policy model employs a range of epistemological and normative background assumptions regarding, e.g., the nature of knowledge or the legitimacy of decision-making processes. These assumptions are not always transparent and explicit. Examples of science–policy models include the policy-oriented, the production-focused, and the integrated model. Due to their idealized nature, actual SPIs will typically represent mixtures of science-society models.
Transdisciplinary science is the methodology that addresses topics across and beyond disciplines, through a comprehensive and holistic framework. In this context, it engages disciplines and interdisciplinary research, but should also consider the collaboration between professional scientists and diverse non-academic stakeholders, either individuals or institutions, in order to benefit from and contribute to their understanding of a problem and their specific knowledge. Transdisciplinarity involves interaction at every step of a scientific endeavour (UNESCO, 2017).
Individual professionals are at the heart of the analysis – those directly involved in science-for-policy practices. The analysis aims to study their practices, attitudes and awareness, motivation, and skills in relation to science for policy and in the different types of organizations they are in (knowledge producing, knowledge brokering, knowledge using organizations).
| Knowledge producers (public research institutes, universities, research centres, etc.) | Knowledge users (government administrations, agencies, parliament administrations, etc.) | Knowledge brokers (academies, think tanks, strategic advisory bodies to/near government, policy engagement networks/entities near universities, etc.) |
|---|---|---|
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Proposed methodological approach: multiple-choice surveys aimed at staff in beneficiary organizations and key stakeholders.
| Knowledge producers | Knowledge user | Knowledge brokers |
|---|---|---|
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Proposed methodological approach: open question survey aimed at staff in beneficiary organizations and key stakeholders.
| Knowledge producers | Knowledge user | Knowledge brokers |
|---|---|---|
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Proposed methodological approach: multiple-choice/open question surveys aimed at staff in beneficiary organizations and key stakeholders; interviews with HR and middle management in beneficiary organizations.
| Knowledge producers | Knowledge user | Knowledge brokers |
|---|---|---|
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Proposed methodological approach: self-assessment surveys aimed at staff in beneficiary organizations and key stakeholders; interviews with HR and middle management in beneficiary organizations to compare staff perceptions with management ones.
Individuals are embedded in organizations whose values and culture, structures, processes, composition and profile of teams, and HR policies affect the individual capacity for science-for-policy practices.
| Knowledge producers | Knowledge user | Knowledge brokers |
|---|---|---|
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Proposed methodological approach: Desk research; interviews with beneficiary organizations.
| Knowledge producers | Knowledge user | Knowledge brokers |
|---|---|---|
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Proposed methodological approach: Desk research focused on beneficiary organizations and key stakeholders.
| Knowledge producers | Knowledge user | Knowledge brokers |
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Proposed methodological approach: Desk research focused on beneficiary organizations and key stakeholders; interviews with management of dedicated units/entities.
| Knowledge producers | Knowledge user | Knowledge brokers |
|---|---|---|
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Proposed methodological approach: Desk research focused on beneficiary organizations and key stakeholders; interviews with management/staff in charge of organizational operations.
| Knowledge producers | Knowledge user | Knowledge brokers |
|---|---|---|
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Proposed methodological approach: interviews/focus groups.
| Knowledge producers | Knowledge user | Knowledge brokers |
|---|---|---|
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Proposed methodological approach: Interviews/focus groups with HR units and management.
Inter-organizational relations are critical for science for policy to facilitate interactions between individuals and organizations at the science–policy interface across sectors (science to policy and vice versa) but also within sectors (intra-sectoral coordination in academia and inter-ministerial, inter-agency coordination within government). These relations are shaped by rules, processes and networks.
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Proposed methodological approach: Desk research for the mapping; interviews/focus groups to understand how (well) the relations work.
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Proposed methodological approach: Desk research for the mapping; interviews/focus groups to understand how (well) the relations work.
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Proposed methodological approach: Desk research for the mapping; interviews/focus groups to understand how (well) the relations work.
Beyond these organizational and inter-organizational levels, it is also important to consider a broader policy perspective such as the scientific and the politico-institutional systems, the wider economy, education systems, and more. The wider systems, and the policy frameworks shaping it, affect to what extent science for policy is pursued by scientific organizations, government bodies, etc. It is important to understand whether policy frameworks and broader environmental conditions affect science-for-policy activities.
R&I policies shape what scientific institutions do in terms of investments, reforms, incentives, and more. Of particular importance are:
There are basic questions to address:
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© FAO/Luis Tato
The design of an SPI depends on a range of considerations, including its aim (see Section 3.1), location and governance (see Section 4.3). The way in which these aspects are managed depends not only on factual circumstances such as the available funding, but also on the normative and epistemological background assumptions of the various SPI members. This includes assumptions regarding the nature of knowledge, the principles of good decision-making, and the relationship between science and society in general. The sum of an actor’s assumptions on these philosophical themes constitutes the actor’s science–policy model. Although these mental models are not always fully conscious, they can have profound effects on an actor’s expectations towards the SPI and on decisions made in the SPI process. Science-policy models are also central to interpreting and implementing the CRELE-IT principles (see Section 3.2).
While their relevance is well documented in the academic literature (Guba and Lincoln, 2000; Van Zwanenberg and Millstone, 2005; Pielke, 2007; Hulme, 2009; Kowarsch, 2016), science–policy models tend to remain implicit in SPI practices. However, there are several reasons why a more explicit approach may be beneficial to SPIs:
Several options exist for incorporating such reflection processes into SPIs. The Swiss Academy of Arts and Sciences provides a website31 where SPI organizers can find various methods with short descriptions and step-by-step instructions (see also Pohl and Wuelser, 2019). Among these methods, the Toolbox Dialogue Approach is particularly well-suited to explore the deep-rooted, philosophical beliefs of actors with diverse backgrounds (Eigenbrode et al., 2007; Hubbs, O’Rourke and Orzack, 2020). The approach demonstrates a positive track record of several hundreds of workshops. Yet it should be noted that the approach has been developed in the context of cross-disciplinarity rather than in an SPI context. Recently, a similar approach has been proposed that does not demonstrate the same track record, but that is specifically tailored to science–policy models (Dressel, 2022).
In the SPI context, a reflection process can be organized along the following steps, which build on the established Toolbox Dialogue Approach and the more recent approach by Dressel (2022):
© FAO/Giulio Napolitano
For each key question, rate the statements A, B and C on a scale from 1 to 5. Consider all statements individually (statements are not mutually exclusive).
Scale: 5 = strongly agree, 4 = agree, 3 = neither agree nor disagree, 2 = disagree, 1 = strongly disagree.
1-A: Valid knowledge is based solely on scientific evidence, established scientific methods and standardized peer review.
1-B: Valid knowledge is based primarily on scientific evidence, established scientific methods and standardized peer review. Non-scientific sources, such as traditional, local or practitioner knowledge, may be considered to a limited extent.
1-C: Valid knowledge is based on both scientific and non-scientific sources. Data from traditional, local or practitioner sources must be considered in addition to scientific evidence, established scientific methods and standardized peer review.
2-A: Value-judgements can and should be excluded from the process of knowledge production.
2-B: While it may not be possible to exclude value-judgements completely from the process of knowledge production, their influence should at least be minimized.
2-C: Since excluding value-judgements is neither possible not desirable, they have a legitimate place in the process of knowledge production.
3-A: While knowledge is rarely perfect, we can be confident that uncertainty will be minimized by advances in knowledge. The best response to uncertainty is therefore more and better research.
3-B: Uncertainty is an important feature of knowledge. Since uncertainty will never be eliminated completely, the best response is to continue research, but to avoid overconfidence.
3-C: Knowledge is inherently uncertain. While future research may be helpful, the best response to uncertainty is to invite stakeholders to evaluate and improve the existing knowledge.
4-A: Knowledge production should mainly be driven by scientists. Non-scientists may be consulted occasionally, but scientists should have full authority due to their expertise.
4-B: Non-scientists should be consulted regularly by scientists to understand the needs of practice. However, scientists should remain in charge when it comes to data and evidence.
4-C: Non-scientists are as important in knowledge production as scientists. They should engage in an open and equitable dialogue where scientists and non-scientists enjoy the same authority.
5-A: A solid knowledge base is the most important factor for good policy. Other factors, such as political or ethical considerations, should play a secondary role.
5-B: Good policy rests on many factors, including political or ethical considerations. While knowledge is relevant as well, its influence should not be overestimated.
5-C: The line between knowledge and political or ethical considerations is blurry. Rather than assuming a hierarchy, these factors should be seen as equally important for good policy.
6-A: Scientists and other experts should improve policy by advocating for the best course of action. Policymakers and the public should pay special attention to their advice.
6-B: While scientists and other experts should provide policy-relevant knowledge, they should remain politically neutral, as they are not legitimized to prescribe courses of action.
6-C: Scientists and other experts are as legitimized as everyone else to advocate for or against specific policies. Their advice is valuable, but does not carry any special authority.
7-A: A decision-making process is legitimate if it generates good policies. The legitimacy of such a process is based mainly on the quality of the resulting decisions.
7-B: A decision-making process is legitimate if the involved decision-makers are properly authorized. Legitimacy depends mainly on whether those making a decision are entitled to do so.
7-C: A decision-making process is legitimate if all stakeholders had an opportunity to contribute to the process. Inclusion of all affected parties is the main source of decision legitimacy.
8-A: Science, politics, and other sectors are distinct societal spheres. Interaction between these spheres is most effective when scientists take the initiative by speaking truth to power.
8-B: Science, politics, and other sectors are distinct societal spheres. Effective interaction occurs when decision-makers take the initiative by defining a problem and requesting the specific knowledge required to solve it.
8-C: The boundaries between science, politics, and other sectors are fluid. The most effective mode of interaction is a continued dialogue where all sides can take the initiative.
Analysis: For each key question, statements A, B, and C represent the background assumptions associated with one science-policy model discussed in this guidance (see Section 3.3). Key questions 1, 2, 3 and 4 focus on epistemological assumptions; key questions 5, 6, 7 and 8 focus on normative and socio-theoretical assumptions. The sum of a respondent’s ratings in either of these categories describes the degree to which the respondent subscribes to the respective model. Results can be analyzed on an aggregated basis to determine the group’s overall support for each model, or on an individual basis to determine discrepancies and convergencies between participants.
Statement categories: A = production-focused model, B = policy-oriented model, C = integrated model
Required citation:
FAO 2024. Guidance on strengthening national science–policy interfaces for agrifood systems. Rome, FAO. https://doi.org/10.4060/cd3125en
The designations employed and the presentation of material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations (FAO) concerning the legal or development status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or products of manufacturers, whether or not these have been patented, does not imply that these have been endorsed or recommended by FAO in preference to others of a similar nature that are not mentioned.
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