RAAIS: Rapid Appraisal of Agricultural Innovation Systems (Part I).
A diagnostic tool for integrated analysis of complex problems and
innovation capacity
Marc Schut a,b,*, Laurens Klerkx a, Jonne Rodenburg c, Juma Kayeke d, Léonard C. Hinnou e,
Cara M. Raboanarielina f, Patrice Y. Adegbola e, Aad van Ast g, Lammert Bastiaans g
a Knowledge, Technology and Innovation Group, Wageningen University, P.O. Box 8130, 6700 EW Wageningen, The Netherlands
b International Institute of Tropical Agriculture (IITA), Quartier INSS/ Rohoro, Avenue d’Italie 16, BP 1893, Bujumbura, Burundi
c Africa Rice Center (AfricaRice), East and Southern Africa, P.O. Box 33581, Dar es Salaam, Tanzania
d Mikocheni Agricultural Research Institute (MARI), P.O. Box 6226, Dar es Salaam, Tanzania
e Institut National des Recherches Agricoles du Bénin (INRAB), P.O. Box 02 BP 238, Porto-Novo, Benin
f Africa Rice Center (AfricaRice), P.O. Box 01 B.P. 2031, Cotonou, Benin
g Crop Systems Analysis Group, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands
A R T I C L E I N F O
Article history:
Received 25 February 2014
Received in revised form 13 August 2014
Accepted 19 August 2014
Available online 22 October 2014
Keywords:
Agricultural research for development
(AR4D)
Farming systems research
Integrated assessment
(Participatory) research methods
System diagnostics
Wicked problems
A B S T R A C T
This paper introduces Rapid Appraisal of Agricultural Innovation Systems (RAAIS). RAAIS is a diagnostic
tool that can guide the analysis of complex agricultural problems and innovation capacity of the agri-
cultural system in which the complex agricultural problem is embedded. RAAIS focuses on the integrated
analysis of different dimensions of problems (e.g. biophysical, technological, socio-cultural, economic,
institutional and political), interactions across different levels (e.g. national, regional, local), and the con-
straints and interests of different stakeholder groups (farmers, government, researchers, etc.). Innovation
capacity in the agricultural system is studied by analysing (1) constraints within the institutional, sectoral
and technological subsystems of the agricultural system, and (2) the existence and performance of the
agricultural innovation support system. RAAIS combines multiple qualitative and quantitative methods,
and insider (stakeholders) and outsider (researchers) analyses which allow for critical triangulation and
validation of the gathered data. Such an analysis can provide specific entry points for innovations to address
the complex agricultural problem under study, and generic entry points for innovation related to strength-
ening the innovation capacity of agricultural system and the functioning of the agricultural innovation
support system. The application of RAAIS to analyse parasitic weed problems in the rice sector, con-
ducted in Tanzania and Benin, demonstrates the potential of the diagnostic tool and provides
recommendations for its further development and use.
© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND
license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
1. Introduction
The Agricultural Innovation System (AIS) approach has become
increasingly popular as a framework to analyse, and explore solu-
tions to, complex agricultural problems (e.g. Hall et al., 2003; World
Bank, 2006). The AIS approach evolved from a transition from
technology-oriented approaches, to more systems-oriented ap-
proaches to agricultural innovation (e.g. Klerkx et al., 2012a). Within
the AIS approach, innovation is perceived as a process of com-
bined technological (e.g. cultivars, fertilizer, agronomic practices)
and non-technological (e.g. social practices such as labour organi-
zation or institutional settings such as land-tenure arrangements)
changes (Hounkonnou et al., 2012; Leeuwis, 2004). Such changes
occur across different levels (e.g. field, farm, region), and are shaped
by interactions between stakeholders and organisations inside
and outside the agricultural sector (Kilelu et al., 2013; Klerkx et al.,
2010).
Adopting an AIS approach to study complex agricultural prob-
lems has important implications for research. First, it requires an
analysis that acknowledges and integrates the different dimen-
sions, levels and stakeholders’ interests associated with the problem
under review. Second, it necessitates a holistic understanding of the
innovation capacity of the agricultural system in which the complex
problem is embedded (Hall, 2005). Third, it requires insight in the
structural conditions provided by the agricultural innovation support
system that can enable or constrain innovation in the agricultural
* Corresponding author. Tel.: +257 720 787 40.
E-mail address: m.schut@cgiar.org; marc.schut@wur.nl (M. Schut).
http://dx.doi.org/10.1016/j.agsy.2014.08.009
0308-521X/© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/
3.0/).
Agricultural Systems 132 (2015) 1–11
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system (Klerkx et al., 2012b; World Bank, 2006). Fourth, it re-
quires a thorough understanding of the interactions between
complex agricultural problems, innovation capacity in the agricul-
tural system and the agricultural innovation support system.
Despite the recent development and application of a variety of
methods that can support AIS analyses (e.g. World Bank, 2012), the
potential of the AIS approach to address complex agricultural prob-
lems remains underutilized in many fields of study (e.g. Schut et
al., 2014a). Four main reasons for this were identified. First, methods
used for the analysis of complex agricultural problems generally have
a narrow focus, rather than a holistic view. They support the anal-
ysis of a specific dimension (e.g. the economic dimension in Beintema
et al., 2012), level (e.g. the national level in Temel et al., 2003), or
stakeholder group (e.g. farmers in Amankwah et al., 2012; Totin et al.,
2012). Second, studies that do include analysis of multiple dimen-
sions of problems (e.g. Singh et al., 2009), interactions across different
levels (e.g. Douthwaite et al., 2003) or multi-stakeholder dynam-
ics (e.g. Hermans et al., 2013) often have limited attention for the
integrated analysis of these features of complex agricultural prob-
lems. Third, studies that integrate the analysis of multiple dimensions
of problems, interactions across different levels andmulti-stakeholder
dynamics (e.g. Lundy et al., 2005; van Ittersum et al., 2008) have
limited attention for understanding innovation capacity in the ag-
ricultural system and the functioning of the agricultural innovation
support system. A fourth reason is that the majority of AIS studies
are conducted ex-post (e.g. Basu and Leeuwis, 2012), lack a clear
structure to delineate system’s boundaries (Klerkx et al., 2012b), or
are based on comprehensive studies which take considerable time
(e.g. Jiggins, 2012). Although such studies provide a better under-
standing of the drivers of innovation in agricultural systems, their
diagnostic ability to identify entry points for innovation to over-
come complex agricultural problems is limited.
Based on the above review of the availability, scope and use of
methods for AIS analyses, we have developed and tested a diag-
nostic tool that can support the Rapid Appraisal of Agricultural
Innovation Systems (RAAIS). RAAIS fits within a tradition of ‘rapid
appraisal approaches’ used in the field of agriculture, including the
Participatory (Rapid) Rural Appraisal (Chambers, 1994), Rapid Ap-
praisal of Agricultural Knowledge Systems (RAAKS: Engel, 1995) and
the Rapid Appraisal of Potato Innovation Systems (Ortiz et al., 2013).
RAAIS integrates and builds upon existing (agricultural) innova-
tion system concepts and combines multiple methods of data
collection. The objectives of RAAIS are to provide a coherent set of
(1) specific entry points for innovation to address complex agri-
cultural problems, and (2) generic entry points that can enhance
innovation capacity of the agricultural system and the perfor-
mance of the agricultural innovation support system. The aim of
this paper is to provide a conceptual framework (Section 2) and a
methodological framework (Section 3) for RAAIS. Based on its ap-
plication in a study on parasitic weeds in rice production in Tanzania
and Benin, we reflect on the extent to which RAAIS is able to meet
its objectives, and provide recommendations for further develop-
ment and use of RAAIS (Section 4), followed by themain conclusions
(Section 5).
2. Conceptual framework for RAAIS
The agricultural innovation system – including both the agri-
cultural system and its innovation capacity and the agricultural
innovation support system – may be very good at tackling some
complex agricultural problems, but may be incapable to deal with
others (Hung and Whittington, 2011; Markard and Truffer, 2008).
It underlines that understanding complex agricultural problems, in-
novation capacity in the agricultural system, and the functioning
of the agricultural innovation support system requires integrative
analysis. Despite of their interrelated character, we deem it
useful for analytical purposes to first address them separately
(Sections 2.1, 2.2 and 2.3), before showing their embeddedness
(Section 2.4).
2.1. Complex agricultural problems
Complex agricultural problems are defined as problems (1) that
have multiple dimensions (Schut et al., 2014b), (2) that are em-
bedded in interactions across different levels (Giller et al., 2008),
and (3) where a multiplicity of actors and stakeholders are in-
volved (Funtowicz and Ravetz, 1993). Regarding the first, complex
agricultural problems are an interplay of biophysical, technologi-
cal, social-cultural, economic, institutional and political dimensions.
To exemplify this, we use a case by Sims et al. (2012), who analyse
constraints for the upscaling of conservation agriculture in sub-
Saharan Africa. They demonstrate how import taxes on steel, but
not on imported agricultural machinery (institutional dimension),
disadvantage manufacturers in developing locally adapted agricul-
tural equipment such as no till planters (technological dimension)
for effective soil conservation for sustainable cropmanagement (bio-
physical dimension). Concerning the second, the dimensions of
complex agricultural problems often have different implications
across different levels. Mitigating the impact of agro-industrial biofuel
production on food security, for instance, will require different strat-
egies when approached at the national level (e.g. policies avoiding
agro-industrial biofuel production in regions where pressure on ag-
ricultural land is already high) or at the farm household level (e.g.
balancing the allocation of household labour to on-farm crop pro-
duction and off-farm plantation work) (Schut and Florin,
under review). Nevertheless, the different levels are interrelated, and
consequently, coherent multi-level strategies are required. Regard-
ing the third, complex agricultural problems are characterised by
the involvement of a multitude of actors, stakeholders and the
organisations they represent (Hounkonnou et al., 2012; Ortiz et al.,
2013) (Table 1). Actors include about anyone that can be related di-
rectly or indirectly to a problem, or the potential solution.
Stakeholders are those actors or actor groups with a vested inter-
est in addressing the problem (McNie, 2007) and their participation
in exploring solutions to complex agricultural problems is per-
ceived as a critical success factor (e.g. Giller et al., 2011). Stakeholder
participation can provide enhanced insights in the different dimen-
sions of the problem, and the types of solutions that are both
technically feasible, and socio-culturally and economically
acceptable (Faysse, 2006). However, stakeholder groups are no ho-
mogeneous entities and often focus on their own, rather than a
common, interest (Leeuwis, 2000).
Table 1
Examples of stakeholder groups and diversity within stakeholder groups.
Stakeholder groups Diversity within stakeholder group
1. Farmers Smallholder farmers, agro-industrial
farmers
2. Non-governmental
organisations (NGO) and
civil society organisations
(Inter)national agricultural networks and
associations, cooperatives, development
organisations, donors
3. Private sector Input and service providers (e.g. seed
and agro-dealers, private extension
services), agricultural entrepreneurs (e.g.
processors, traders, retailers, transport
companies)
4. Government Politicians, policymakers, extension and
crop protection officers
5. Research and training National agricultural research institutes,
agricultural education and training
institutes, universities, international
research institutes
2 M. Schut et al./Agricultural Systems 132 (2015) 1–11
2.2. Innovation capacity in the agricultural system
The agricultural system is defined as the “operational unit of ag-
riculture” including all actors and organisations at local, regional
and national levels involved in the production, processing and com-
mercialization of agricultural commodities (Spedding, 1988).
Consequently, innovation capacity in the agricultural system is
defined as the ability of these actors and organisations to develop
new andmobilise existing competences (including knowledge, skills
and experiences) to continuously identify and prioritise con-
straints and opportunities for innovation in a dynamic systems
context (Leeuwis et al., 2014).
Following the typical system boundaries used in generic (i.e. non-
agricultural) studies of innovation systems (Carlsson et al., 2002;
Papaioannou et al., 2009; Wieczorek and Hekkert, 2012), we con-
ceptualise the agricultural system as a combination of interrelated
institutional, sectoral and technological subsystems. The institu-
tional subsystem comprises different types of institutions, which
are the formal and informal rules and structures that shape per-
spectives and practices (Leeuwis, 2004). In this paper we examine
six types of institutions; policy, research, education and training,
extension, markets and politics across different aggregation levels
(e.g. national, regional or district) (e.g. Cooke et al., 1997; Freeman,
1988, 1995). The sectoral subsystem is defined around a commod-
ity or segments of a value chain (e.g. rice or cocoa) (e.g. Blay-Palmer,
2005; Gildemacher et al., 2009). The analysis of the sectoral sub-
system seeks to understand interactions between, for instance, access
to credit, inputs and services, agricultural production, post-
harvest activities, trade, marketing and consumption related to the
functioning of that value chain (e.g. Thitinunsomboon et al., 2008).
Within the agricultural system, different sectoral subsystems can
exist and interact. Technological subsystems are defined around an
existing or novel technology (e.g. irrigation, mechanised weeding)
or field of knowledge (e.g. integrated pest management) to address
a particular problem that may well cut across different sectoral sub-
systems (Carlsson and Stankiewicz, 1991; Chung, 2012; Hekkert et al.,
2007).
2.3. The agricultural innovation support system
The agricultural innovation support system provides the struc-
tural conditions that can enable (when present) or constrain (when
absent or malfunctioning) innovation within the agricultural system
and its subsystems (Klein Woolthuis et al., 2005; van Mierlo et al.,
2010; Wieczorek and Hekkert, 2012) (Table 2). Structural condi-
tions include (1) adequate knowledge infrastructure in the form of
research, education and extension, physical infrastructure and assets
such as roads and vehicles, and functional communication and
finance structures, (2) institutions comprise clear regulatory frame-
works and their proper implementation and enforcement, (3)
interaction and collaboration between multiple stakeholders in the
agricultural system, and (4) stakeholder capacities (e.g. literacy and
entrepreneurship) and adequate human and financial resources (e.g.
number of extension officers and funds for their backstopping). The
analysis of the presence and functioning of these structural con-
ditions contributes to a better understanding of what constraints
or enables innovation capacity in the agricultural system (e.g. limited
multi-stakeholder collaboration), as well as how the structural con-
ditions provided by the agricultural innovation support system
stimulate or hamper this (e.g. incentive structures for different stake-
holder groups to collaborate).
The set-up of the agricultural innovation support system may
be good at supporting incremental ‘system optimisation’ that re-
produce the current state of affairs, but less good at supporting
‘system transformation’ that can lead to radical innovations. For
example, the presence of an effective top-down, technology-
oriented agricultural extension system can enable the dissemination
of crop protection solutions through a technology transfer ap-
proach. However, the existence of this system can form a constraint
for the promotion of agro-ecological approaches through partici-
patory, farmer-led experiments. Consequently, to achieve system
transformation, both the agricultural system and the agricultural
innovation support system should undergo continuous adapta-
tion (Hall et al., 2004; Spielman, 2005).
2.4. Interactions between complex agricultural problems, innovation
capacity in the agricultural system and the agricultural innovation
support system
The integrated analysis of complex agricultural problems, in-
novation capacity of the agricultural system and the performance
of the agricultural innovation support system can provide a coher-
ent set of specific and generic entry points for innovation (Fig. 1).
Specific entry points for innovations relate to those innovations that
directly contribute to addressing the complex agricultural problem
under study. Generic entry points for innovation related to strength-
ening the innovation capacity of agricultural system and the
functioning of the agricultural innovation support system. For
example, to reduce fruit waste in developing countries, existing tech-
nologies for conserving fruits can be adapted to fit the local context
(specific entry point for innovation of the technological subsys-
tem). This may trigger access to export markets (specific entry point
for innovation of the sectoral subsystem) and require certification
policies to supply such fruit export markets (specific entry point for
innovation of the institutional subsystem). To support the devel-
opment, implementation and enforcement of certification policies,
the establishment of a national agricultural certification bureaumay
be required (generic entry point for innovation). The existence of
such a bureau can provide an incentive for investing in the export
of other agricultural products, for instance, vegetables, that, in turn,
can trigger the development or adaptation of conservation tech-
nologies to reduce vegetable waste. The above example shows how
structural adaptations of the agricultural innovation support system
can enhance innovation capacity to addressing the complex agri-
cultural problem under review (fruit waste), but can also have a spill-
over effect on addressing other complex agricultural problems
(vegetable waste). Furthermore, the agricultural innovation support
system can provide conditions that support innovation in the ag-
ricultural sector more generally, for instance through innovation
Table 2
Structural conditions that enable or constrain innovation in systems (based on Klein
Woolthuis et al., 2005; van Mierlo et al., 2010; Wieczorek and Hekkert, 2012).
Structural
conditions for
innovation
Description
Infrastructure
and assets
Knowledge, research and development infrastructure;
physical infrastructure including roads, irrigation schemes
and agricultural inputs distribution; communication and
financial infrastructure.
Institutions Formal institutions including agricultural policies; laws;
regulations; (food) quality standards; agricultural
subsidies; Monitoring and Evaluation (M&E) structures;
organisational mandates; market (access) and trade
agreements; informal institutions such as social-cultural
norms and values.
Interaction and
collaboration
Multi-stakeholder interaction for learning and problem-
solving; development and sharing of knowledge and
information; public-private partnerships; networks;
representative bodies (e.g. farmers association); power-
dynamics.
Capabilities and
resources
Agricultural entrepreneurship; labour qualifications;
human resources (quality and quantity); education and
literacy rates; financial resources.
3M. Schut et al./Agricultural Systems 132 (2015) 1–11
policy or funding schemes that affect multiple institutional, sectoral
and technological subsystems.
3. Methodological framework for RAAIS
3.1. Selection criteria for methods
RAAIS is a diagnostic tool that combinesmultiplemethods of data
collection. Building on existing experiences with rapid appraisal ap-
proaches and (participatory) innovation systems analysis, five criteria
for the selection of methods have been identified.
1. Methods should be diverse, rigorous, and be able to generate
both qualitative and quantitative data. This enhances the
credibility and strength of the analysis (Spielman, 2005).
Qualitative data provide the basis for the identification
and analysis of the different dimensions of complex agricultur-
al problems, and structural conditions enabling or constraining
the innovation capacity. Such data may also provide narratives
regarding the underlying causes and historical evolution
of constraints. Quantitative data analysis can build on this by pro-
viding (descriptive) statistics and trends on, for instance, the
distribution of constraints across different levels, stakeholder
groups or study sites.
2. Methods should facilitate both ‘insider’ and ‘outsider’ analysis.
Insider analysis implies data analysis by stakeholders who can
provide highly detailed explanations of specific phenomena based
on their knowledge and experiences. However, insiders such as
farmers or policymakers often have an incomplete or insuffi-
cient critical view of the broader agricultural system or the
agricultural innovation support system. Consequently, it is im-
portant to complement insider analysis by outsider analysis of
data by researchers (vanMierlo et al., 2010). By combining insider
and outsider analysis, the delineation of the systems boundar-
ies is done in a participatoryway, by stakeholders and researchers.
3. Methods should be able to target different stakeholder groups
across different levels. When studying complex agricultural prob-
lems, it is essential to include different groups of stakeholders,
their perceptions on what constitutes the problem, and what are
perceived feasible or desirable solutions (Faysse, 2006; Ortiz et al.,
2013).
4. Methods should be able to target stakeholders individually, in
homogeneous groups and in heterogeneous groups so as to
capture individual, group and multi-stakeholder perceptions on
problems and solutions. Discussion and debate in both homo-
geneous and heterogeneous stakeholder groups generally provide
a rich analysis of complex problems and potential solutions. Fur-
thermore, multi-stakeholder interaction may reveal asymmetric
power-relationships that are necessary to understand innova-
tion capacity in the agricultural system. On the other hand,
power-relationships, group pressure, or mutual dependencies
between stakeholders may result in situations where sensitive
Fig. 1. Schematic representation of the dynamic interactions between complex agricultural problems (multiple dimensions, multi-level interactions and multi-stakeholder
dynamics), innovation capacity of the agricultural system (including its institutional, sectoral and technological subsystems), and the structural conditions within the ag-
ricultural innovation support system that can enable or constrain innovation capacity in the agricultural system (infrastructure and assets, institutions, interaction and collaboration,
and capabilities and resources). RAAIS provides insight into the current state of the system (on the left). RAAIS provides specific and generic entry points for innovation
that can guide a transition towards the desirable state of the system (on the right) in which the complex agricultural problem is addressed, and the innovation capacity in
the agricultural system has increased. Generic entry points for innovation can have a spill-over effect in terms of addressing other complex agricultural problems than the
one under review.
4 M. Schut et al./Agricultural Systems 132 (2015) 1–11
questions are avoided, or receive socially desirable responses.
Methods that target stakeholders individually are more likely to
provide insights in such questions (International Institute for
Sustainable Development, 2014).
5. Methods together should provide sufficient detail on the complex
agricultural problem under review, the innovation capacity in
the agricultural system, and the functioning of the agricultural
innovation support system (World Bank, 2012).
Combining different types of methods and data collection tech-
niques provides an opportunity to triangulate and validate data.
Depending on the nature of the agricultural problem under review
and the available resources and time, different types of data col-
lectionmethods can be used for RAAIS, taking into account the above
criteria for method selection.
3.2. Methods of data collection
Based on the five criteria, four complementary methods for data
collection were selected to be part of RAAIS (Table 3).
3.2.1. Multi-stakeholder workshops
Multi-stakeholder workshopsmainly focused on the insider anal-
yses of innovation capacity in the agricultural system and the
structural conditions provided by the agricultural innovation support
system. A participatory workshop methodology facilitates differ-
ent groups of stakeholders to – individually and in homogeneous
and heterogeneous groups – identify, categorise and analyse con-
straints for innovation in the agricultural system. Depending on the
type of problem, workshops can be organised with stakeholders rep-
resenting national, regional and/or district levels or, for instance,
across different study sites where a specific problem is eminent. To
keep the workshops manageable, and to stimulate interaction and
debate, the participation of a maximum of 25 participants per work-
shop is proposed; for instance consisting of five representatives of
the five different stakeholder groups in Table 1. As much as possi-
ble, each group should be a representative sample with respect to,
for instance, gender, age, income, or ethnic groups. The work-
shops should be held in the language that all participants speak,
and be facilitated by someone who is familiar with the cultural
norms, has affinity with the problem, and understands the reali-
ties of the different stakeholder groups. The proposed workshop
methodology consists of 13 Sessions subdivided into three catego-
ries, with each their own focus: (1) identifying constraints, (2)
categorising constraints, and (3) exploring specific and generic entry
points for innovation. Figure 2 and Table 4 provide an overview of
the 13 Sessions, their sequence and relations, and their specific ob-
jective in RAAIS.
Workshops are designed to take approximately 1 day. Besides
the facilitator, a note-taker documents the outcome of the differ-
ent sessions and captures discussions among participants. Workshop
facilitation and note-taking protocols ensure that the workshop
organisation, facilitation and documentation is standardized, which
is essential for comparing or aggregating the outcomes, for in-
stance, across different study sites.
A crucial element in the workshops is the use of coloured cards.
At the start of the workshop (Session 1), each of the stakeholder
groups is assigned a different colour. During Session 2, each par-
ticipant individually lists five constraints or challenges they face in
their work and writes them down on their coloured cards. If five
stakeholder groups are equally represented, this results in 125 cards.
During Session 3, the participants discuss within their stakehold-
er groups the listed constraints, explore overlapping issues and jointly
develop a stakeholder group top-5. If necessary, constraints can
be reformulated based on discussions within the group. Each of the
stakeholder groups use their top-5 throughout the rest of the Ta
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5M. Schut et al./Agricultural Systems 132 (2015) 1–11
sessions during the workshop; hence 25 cards (five cards per stake-
holder group) (Photo 1–4).
The use of the coloured cards facilitates the analysis of differ-
ent sessions during and after the workshops. As the cards are coded
and recycled throughout the successive sessions, photographs can
be taken to capture the results (for example Photo 1 and 4). Such
photographs can be analysed after the workshop, and can also be
used to validate the note-taker’s data. Furthermore, the cards provide
insight into the relations between constraints identified by differ-
ent stakeholder groups (Photo 2 and 3). Combining the results from
different sessions can stimulate integrative analyses, for instance,
combining data resulting from Sessions 5 and 6 provides insight in
the structural conditions for innovation across different levels. Sim-
ilarly, the outcome of Sessions 7 and 11 can be compared to
triangulate the data, as both seek to identify key constraints for in-
novation in the agricultural system.
3.2.2. Semi-structured in-depth interviews
To guide the semi-structured interviews, a topic list is pre-
pared and fine-tuned for each interview. Using a topic list provides
a degree of flexibility to identify and to anticipate interesting
storylines related to the problem under review, and allows valida-
tion of data that was gathered during previous interviews or during
the workshops. Interviews should take a maximum of 1 hour, en-
suring a high level of attentiveness of both the respondent as well
as the interviewer. Sampling of interview respondents should follow
a stratified approach, to ensure that stakeholders representing dif-
ferent study sites, different stakeholder groups, and different
administrative levels are included. Within those strata, respon-
dents can be selected purposive or based on snowball sampling
where interview respondents make suggestions for who else should
be included in the sample (Russell Bernard, 2006). The sample size
can be based on the concept of “saturation,” or the point at which
no new information or themes are observed in the interview data
(Guest et al., 2006). Interviews can be recorded and transcribed elec-
tronically. From an ethical point of view, interviewees should give
permission for interviews to be recorded, and researchers should
ensure confidentiality of all interview data. Recordingmay not always
be desirable, as the voice recorder can create a barrier between the
researcher and the respondent, especially when it comes to dis-
cussing politically sensitive issues. Instead of recording, detailed notes
can be taken and transcribed electronically. The transcribed inter-
views can be coded. Ideally, interviews are conducted and coded
by two researchers, which will enhance the quality of the
analysis.
3.2.3. Surveys
Based on the workshops and the interviews, some of the con-
straints may be eligible for broader study among specific groups
of stakeholders through the use of surveys. Such surveys may
provide more insights in, for example, the socio-economic impacts
of climate change on smallholder agriculture in specific regions,
the quality of agricultural extension received by farmers in ad-
dressing complex agricultural problems, or access to agricultural
inputs for male or female headed households. Surveys are not nec-
essarily limited to farmers, but can also be conducted with any of
the other stakeholder groups involved. For the data to be comple-
mentary, surveys should be completed in the same study sites as
where the workshops were organised and among a representative
sample of the targeted stakeholder group. To achieve that, a strati-
fied random sampling strategy can be used to identify respondents
across different study sites, levels or stakeholder groups. A (effi-
cient) sampling method that allows for optimal allocation of
resources can be used to determine the sample size (e.g. Whitley
and Ball, 2002).
3.2.4. Secondary data collection
Secondary data are written data with relevance for the analy-
sis of the complex agricultural problem, innovation capacity of the
agricultural system or the functioning of the agricultural innova-
tion support system. Examples are policy documents, project
proposals and reports, laws or legal procedures, project evalua-
tions, curricula for agricultural education and training, (agricultural)
census and organisational records such as charts and budgets over
a period of time. The sampling of secondary data is not clear cut.
Key agricultural documents such as agricultural policies or agri-
cultural research priorities should be included. These documents
can refer to other relevant data. Furthermore, secondary data is often
provided during, or following interviews. Insights from secondary
data can be verified in interviews with stakeholders (e.g. the extent
to which policy is implemented and enforced).
4. RAAIS’ ability to provide specific and generic entry points
for innovation and lessons learnt from its application
We tested RAAIS through a case study aimed at analysing con-
straints and opportunities for innovation to effectively address
parasitic weeds in rain-fed rice production systems in Tanzania
(April–October 2012) and Benin (June–August 2013). The results from
RAAIS in Tanzania are elaborated in Schut et al. (2014c). Data were
gathered across national, zonal, regional and district levels. Multi-
stakeholder workshops (with 68 participants in Tanzania and 66
participants in Benin) were organised in three study sites (dis-
tricts) in Tanzania and Benin where parasitic weeds are eminent.
In-depth interviews were held with representatives of national-,
zonal-, regional- and district-level representatives of farmer coop-
eratives and associations, NGO/ civil society, private sector,
government and research and training institutes (42 in Tanzania,
65 in Benin). Across the three study sites in the countries, a
Session 
2
Session
3
Session
1
Categorising
constraints
Exploring specific and 
generic entry 
points for 
innovation 
Identifying constraints
Fig. 2. The relation between the 13 Workshop Sessions and their sequence, sub-
divided over the three categories. The dotted arrows indicate relations between the
different sessions in terms of triangulation and validation of data.
6 M. Schut et al./Agricultural Systems 132 (2015) 1–11
socio-economic farmer survey (152 in Tanzania, 182 in Benin) was
held to study the impact of parasitic weeds on rain-fed rice farming
(see N’cho et al., 2014 for more information). In Tanzania, a farmer-
extensionist survey (120 farmers, 30 agricultural extension officers)
was held to explore the effectiveness of the national agricultural ex-
tension policy across the three study sites (see Daniel, 2013 for more
information). Additionally, for both countries, secondary data in-
cluding crop protection, extension and general agricultural policy,
national research priorities, agricultural census and agricultural train-
ing curricula were analysed. Data gathering and initial analysis took
around three months for each of the countries, and involved two
researchers. We first conducted the in-depth interviews, followed
Table 4
The 13 Workshop Sessions subdivided over the three categories, and their specific activities and objectives in the RAAIS.
Categories Sessions Activities Objective(s)
Identifying
constraints
1. Opening and
participant
introduction
Participants (1) introduce themselves and
receive information about the workshop
methodology; and (2) are subdivided over
different stakeholder groups (e.g. groups
identified in Table 1)
• To ensure an equal representation of participants over the
different stakeholder groups
2. Individual
brainstorming about
constraints
Participants individually identify five
constraints they face in their work
• To make an inventory of general constraints in the agricultural
system faced by stakeholders
3. Developing a top-5 of
constraints in
stakeholder groups
Participants (1) discuss constraints within
respective stakeholder group; (2) develop an
stakeholder group top-5 of constraints; (3)
present the top-5 to other stakeholder groups;
and (4) discuss within and between
stakeholder group(s)
• To gain insights in the key constraints in the agricultural system as
faced by different stakeholder groups
• To create awareness and stimulate learning among stakeholders
Categorising
constraints
4. Categorising
constraints along
different types of
institutions
Participants (1) categorise top-5 constraints as
policy-, research-, education and training-,
extension-, markets- and/ or politics-related;
(2) present results to the other groups; and (3)
discuss within and between the stakeholder
group(s)
• To gain insights in how key constraints relate to the different types
of institutions (institutional subsystem)
• To create awareness and stimulate learning between stakeholders
5. Categorising
constraints along
structural conditions
that can enable or
constrain innovation
Participants (1) categorise top-5 constraints
along the structural conditions drivers of
innovation (Table 2); and (2) discuss within
and between the stakeholder group(s)
• To gain insights in how the stakeholder constraints relate to
structural conditions provided agricultural innovation support
system and whether these enable or constrain innovation capacity
• To create awareness and stimulate learning between stakeholders
6. Categorising
constraints across
different
(administrative) levels
within the institutional
subsystems
Participants (1) categorise top-5 constraints
across different administrative levels (e.g.
national, regional, district); (2) discuss results
with other stakeholder groups; and (3) discuss
within and between the stakeholder group(s)
• To gain insights in how key constraints relate to different
institutional (administrative) levels
• To identify and analyse interactions between different levels
• To create awareness and stimulate learning between stakeholders
7. Identifying
relationships between
constraints, and
identifying key
constraints
Participants (1) jointly discuss and identify
relations between the different constraints; (2)
identify constraints or challenges that are
central in the analysis; and (3) discuss within
and between the stakeholder group(s)
• To analyse relationships between different constraints
• To identify key constraints
• To create awareness and stimulate learning between stakeholders
• Identify generic entry points for enhancing the innovation
capacity in the agricultural system
8. Categorising
constraints along the
sectoral subsystem
Participants (1) categorise stakeholder group
top-5 constraints along the segments of the
value chain; and (2) discuss within and
between the stakeholder group(s)
• To analyse constraints along the sectoral subsystem
• To create awareness and stimulate learning between stakeholders
9. Categorising
constraints along
different technological
subsystems
Participants (1) categorise top-5 constraints
along different technological or knowledge
fields; and (2) discuss within and between the
stakeholder group(s)
• To analyse constraints along different technological subsystems
• To create awareness and stimulate learning between stakeholders
Exploring
entry
points for
innovation
10. Exploring constraints
stakeholder groups can
solve themselves
versus problems that
can only be solved
with or by others
Participants (1) categorise top-5 constraints as:
‘can be solved within the stakeholder group’,
or ‘can only be solved in collaboration with
other stakeholder groups’; and (2) discussion
within and between the stakeholder group(s)
• To identify constraints that require collaboration between
stakeholder groups
• To create awareness and stimulate learning between stakeholders
• Identify entry points for innovation in the agricultural system
11. Exploring constraints
that are easy/ difficult
to solve
Participants: (1) categorise top-5 constraints as
relatively ‘easy’ or ‘difficult’ to address; and (2)
discuss within and between the stakeholder
group(s)
• To explore which constraints require system optimisation (easy to
address) and those that require system transformation (difficult to
address)
• To create awareness and stimulate learning between stakeholders
• To triangulate data with Session 7 (are key constraints perceived
to be easy/ difficult to address)
• Identify entry points for enhancing the innovation capacity in the
agricultural system
12. Exploring constraints
that are structural/
operational
Participants categorise top-5 constraints along
a four-step gradient, ranging from ‘very
structural’, ‘structural’, ‘operational’ and ‘very
operational’ challenges and constraints
• To distinguish between structural constraints that require specific
innovation, and more structural problems that require generic
innovation.
• To create awareness and stimulate learning between stakeholders
• To triangulate data with Sessions 7 and 11 (relation between key
constraints how these are perceived by stakeholders)
• Identify generic entry points for enhancing the innovation
capacity in the agricultural system
13. Identifying priorities
and solution strategies
Participants (1) jointly discuss and develop an
overall top-5 of constraints; and (2) jointly
identify potential strategies to address these
constraints
• To explore opportunities for addressing systems constraints
through multi-stakeholder collaboration
• To explore similarities and differences with the key systems
constraints identified in Session 7
• Identify key entry points for innovation
7M. Schut et al./Agricultural Systems 132 (2015) 1–11
by the multi-stakeholder workshops. In Tanzania, both the socio-
economic farmer survey and the farmer-extensionist survey were
held after the interviews and workshops. In Benin, the socio-
economic farmer survey was held preceding the in-depth interviews
and workshops. Secondary data collection occurred throughout the
fieldwork. Below, we will further reflect on the main objectives of
RAAIS, as well as provide recommendations for further improve-
ments and use of RAAIS, using our experiences from Tanzania and
Benin.
4.1. RAAIS’ ability to provide specific entry points for innovation to
address complex agricultural problems
RAAIS contributed to an integrated understanding of different
problem dimensions, multi-level interactions, andmulti-stakeholder
dynamics related to parasitic weed problems. With regard to the
different problem dimensions, interviews demonstrated a poten-
tial relation between, for example, the preference for growing local,
aromatic rice varieties (social-cultural dimension), the low capac-
ity of farmer to purchase certified seeds (economic dimension), and
the spread of parasitic weed seeds through the local rice seed system
(technological dimension). Additionally, analysis of workshop data
revealed how the untimely and insufficient availability of agricul-
tural inputs provided by the government (institutional dimension)
and limited interaction and collaboration among networks of key
stakeholders (political dimensions) form additional bottlenecks for
addressing such problems. It created awareness that describing and
explaining complex agricultural problems, and exploring and de-
signing solutions is unlikely to be successful if the different problem
dimensions are analysed and treated separately (Hall and Clark, 2010;
Spielman et al., 2009).
Data gathering across different levels (national, region, and dis-
trict level) enabled the analysis of the interactions and (mis)matches
between different levels (Cash et al., 2006). An example that emerged
during the workshops and the interviews is Tanzania’s national
export ban, that prohibits export of agricultural produce (e.g. of rice)
as long as the country has not been declared ‘food secure’. This na-
tional export ban influences local market prices, and consequently,
also farmers’ willingness and ability to invest in, for example, pur-
chasing agricultural inputs such as fertilizers and seeds (e.g. Poulton
et al., 2010). This, in turn, provided an opportunity to identify entry
points for innovation across different levels, which has been iden-
tified as a critical factor for addressing complex agricultural problems
(e.g. Giller et al., 2008, 2011). As expected, and confirming previ-
ous reports (e.g. van Mierlo et al., 2010), the participatory analysis
of multi-level interactions showed that stakeholders (insiders) often
identify constraints at the level they represent (Schut et al., 2014c).
This was complemented by our analysis as researchers (outsiders)
of the multi-level interactions regarding the parasitic weed
problems.
Photo 1–4. Photo 1 (top left): Top-5 of constraints of NGO/ civil society representatives and their categorisation under the different components of the institutional sub-
system (Session 4). Photo 2 (top right): The categorisation of the top-5 of the different stakeholder groups along different structural conditions that can enable or constrain
innovation (Session 5). Photo 3 (bottom left): The identification of relationships between different constraints (arrows), and key problem (circled cards) (Session 7). Photo
4 (bottom right): The categorisation of the top-5 of the different stakeholder groups along a four-step gradient ranging, from structural to operational constraints (Session
12). Photos were taken by M. Schut during multi-stakeholder workshops in Tanzania held in October 2012.
8 M. Schut et al./Agricultural Systems 132 (2015) 1–11
The involvement of different groups of stakeholders was essen-
tial for enhancing the credibility, validity and quality of RAAIS, as
well as for delineating the boundaries of the agricultural system and
the agricultural innovation support system, which is considered a
key challenge when using AIS approaches to analyse complex ag-
ricultural problems (Klerkx et al., 2012b). Furthermore, stakeholder
participation provided a better understanding of the feasibility and
acceptability of solutions for stakeholder groups. Althoughwe believe
that the stakeholder groups included in the testing of RAAIS (Table 1)
provide a good starting point, other stakeholder groups (for in-
stance the media) may be included in the sample (e.g. Ortiz et al.,
2013) depending on the type of complex agricultural problem under
review. The triangulation of data resulting from the differentmethods
enabled us to validate findings, and to verify strategic communi-
cation by stakeholders, for instance, to verify how the extension
system as described by policymakers in interviews, functioned in
reality according to surveyed farmers.
4.2. RAAIS’ ability to provide generic entry points for innovation
RAAIS demonstrates interactions between complex agricultur-
al problems, innovation capacity of the agricultural system –
consisting of institutional, sectoral and technological subsystems
– and the agricultural innovation support system. For example, ap-
plying fertilizer (technological subsystem) in rain-fed rice production
is seen as a promisingmanagement strategy to reduce infection levels
of Rhamphicarpa, one of the parasitic weeds involved in the study,
andmitigate negative effects of the parasite on rice yields (Rodenburg
et al., 2011). However, as was highlighted during the RAAIS work-
shops in both in Benin and in Tanzania, fertilizers are difficult to
access in rural areas. In Benin, there is no well-developed private
agro-dealer network and distribution infrastructure to support the
supply of agricultural inputs. Furthermore, interviews showed that
the public extension and input supply systems in Benin focus on
the cotton sector, rather than on cereal crops (sectoral subsys-
tems). In Tanzania, a private agro-dealer network and distribution
infrastructure exists, but structures controlling the quality of fer-
tilizers (institutional subsystem) are functioning sub-optimally
according to interviewed government officials. In some areas, fake
agro inputs are dominating the market, resulting in a limited trust
and willingness to invest in applying fertilizer according to farmer
representatives who participated in the workshops. The example
shows how the absence or poor performance of fertilizer distribu-
tion infrastructure, limited farmer-extensionist interaction and lack
of functional institutions for quality control (being structural con-
ditions for innovation) constrain the innovation capacity in the
agricultural systems and its technological (in this case fertilizer) and
sectoral (the rice value chain) subsystems. Another example is based
on secondary data analyses that demonstrated the lack of an op-
erational strategy to address parasitic weeds in Tanzania and Benin.
In both the interviews and workshops, stakeholders highlighted the
general lack of interaction and collaboration between stakehold-
ers in the agricultural sector (being a structural condition for
innovation) as one of the main reasons for the absence or poor im-
plementation of parasitic weed and other agricultural policies and
strategies.
The aforementioned examples demonstrate how RAAIS can
support the identification of generic entry points for innovation. Such
innovations can directly contribute to addressing the complex ag-
ricultural problem under review, but can also have a spill-over effect
in terms of addressing broader constraints that hamper the inno-
vation capacity in the agricultural system. For example, the lack of
stakeholder interactions and collaboration in the agricultural system
can provide an entry point for the adaptation of the structural
conditions in the broader agricultural innovation support system,
for example through investments in innovation brokers or
multi-stakeholder platforms (Kilelu et al., 2013; Klerkx et al., 2010).
Such structural adjustments can facilitate multi-stakeholder col-
laboration in tackling parasitic weed as well as other complex
agricultural problems.
4.3. Lessons learnt from applying RAAIS and recommendations for
further improvement
Based on our experiences in Tanzania and Benin, we recom-
mend conducting RAAIS in an interdisciplinary team of researchers
with expertise on different dimensions of complex agricultural prob-
lems and on different data collection methods (Hulsebosch, 2001).
Other suggestions include the experimentation with other combi-
nations of methods, and on different types of complex agricultural
problems. The workshop methodology could be made more inter-
active, in the sense of directly feeding back results of the sessions
to participants to stimulate reflection and validate analyses during
the workshops. Post-workshop surveys could provide additional
insight into whether stakeholders felt they could freely raise and
discuss their ideas and needs.
The multi-stakeholder workshops, but also the surveys, pre-
sented a rather static picture of the complex agricultural problem
under review and the innovation capacity of the agricultural system
in which the problem is embedded. However, initial workshops and
surveys could function as a baseline, to which future workshops and
surveys can be compared. Other methods such as secondary data
analysis or in-depth interviews present a more dynamic image of
how, for example, collaborations between stakeholders evolve over
the years. Our experiences in Tanzania and Benin show that ensur-
ing social differentiation amongworkshop participants, interviewees
and survey respondents (e.g. of different gender of age) was chal-
lenging, as, for example, the majority of workshop participants were
male. SpecificWorkshop Sessions could havemore attention for cat-
egorisation and priority setting by different gender or age groups.
The facilitation of the multi-stakeholder workshops ensured that
different stakeholder groups could raise and discuss their ideas
(Hulsebosch, 2001). Despite such efforts, unequal power relations
and differences in the ability to debate and negotiate that inher-
ently exist between groups may have played a role. In line with our
expectations, politically sensitive issues were more freely dis-
cussed in individual interviews as compared to multi-stakeholder
setting.
The combination of different methods of data collection was
essential. In terms of the sequence of data collection, we recom-
mend to first conduct and analyse the RAAIS multi-stakeholder
workshops to identify constraints, and subsequently conduct the
in-depth interviews and surveys that can provide more insight in
the distribution and underlying root causes of these constraints.
The workshops then provide a ‘fast-track’ approach to identifying
entry points for innovation, that can subsequently be validated and
explored in more detail using the in-depth interviews and stake-
holder surveys. This would furthermore increase the ‘rapidness’
of RAAIS as a diagnostic tool.
An updated version of the RAAIS multi-stakeholder workshops
has been used to identify constraints, challenges and entry points
for innovations related to the ‘sustainable intensification of agri-
cultural systems’ in Burundi, the Democratic Republic of Congo,
Rwanda, Nigeria and Cameroon under the CGIAR Research Pro-
gramme for the Humid Tropics (Humidtropics) (Schut and Hinnou,
2014). Several of the recommendations made in this paper, includ-
ing the revised sequence of methods for data collection and the use
of post-workshop participant questionnaires, have been imple-
mented and tested successfully. Some of the bottlenecks identified,
such as social differentiation (e.g. gender and age groups) among
workshop participants remained problematic and require further
attention. At the end of the Humidtropics RAAIS workshops,
9M. Schut et al./Agricultural Systems 132 (2015) 1–11
participants developed action plans to address the prioritised con-
straints (Workshop Session 13). This required an extension of the
workshops of half a day. The development and implementation of
the action plans forms an important element for continued stake-
holder collaboration in multi-stakeholder platforms.
5. Conclusions
This paper demonstrates the potential of RAAIS as a diagnostic
tool that can support and guide the integrated analysis of complex
agricultural problems, innovation capacity in the agricultural system,
and the performance of the agricultural innovation support system.
RAAIS combines multiple qualitative and quantitative methods, and
insider (stakeholders) and outsider (researchers) analyses which
allow for critical triangulation and validation of the gathered data.
Such an analysis can provide specific entry points for innovations
to address the complex agricultural problem under study, and generic
entry points for innovation related to strengthening the innova-
tion capacity of agricultural system and the functioning of the
agricultural innovation support system.
Recommendations for further improvement include using RAAIS
for the analysis of other types of complex agricultural problems, using
other combinations of methods of data collection, and providing di-
rectly feedback to workshop participants to stimulate reflection and
validate workshop outcomes. An adapted sequence of data collec-
tion methods in which workshops provide a ‘fast-track’ approach
to identifying entry points for innovation, followed up by more in-
depth interviews and stakeholder surveys would increase the RAAIS’
diagnostic capacity. The participatory development of concrete action
plans based on RAAIS can provide a basis for continued multi-
stakeholder collaboration to operationalise and implement specific
and generic entry points for innovation.
Acknowledgements
This research forms part of the PARASITE programme, funded
by the Integrated Programmes Scheme of the Netherlands
Organisation for Scientific Research – Science for Global Develop-
ment (NWO-WOTRO) (www.parasite-project.org). Additional support
is provided by the CGIAR Research Program on Climate Change, Ag-
riculture and Food Security (CCAFS). The CGIAR Research Programme
for the Humid Tropics (http://humidtropics.cgiar.org/) provided the
opportunity to further adapt, apply and improve RAAIS across dif-
ferent Humidtropics Action Areas. Special thanks to Elifadhili Daniel,
who conducted the farmer-extensionist survey.
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