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Multi-stakeholder platforms (MSPs) are seen as a promising vehicle to achieve agricultural development impacts. By increasing collaboration, exchange of knowledge and influence mediation among farmers, researchers and other stakeholders, MSPs supposedly enhance their 'capacity to innovate' and contribute to the 'scaling of innovations'. The objective of this paper is to explore the capacity to innovate and scaling potential of three MSPs in Burundi, Rwanda and the South Kivu province located in the eastern part of Democratic Republic of Congo (DRC). In order to do this, we apply Social Network Analysis and Exponential Random Graph Modelling (ERGM) to investigate the structural properties of the collaborative, knowledge exchange and influence networks of these MSPs and compared them against value propositions derived from the innovation network literature. Results demonstrate a number of mismatches between collaboration, knowledge exchange and influence networks for effective innovation and scaling processes in all three countries: NGOs and private sector are respectively over-and under-represented in the MSP networks. Linkages between local and higher levels are weak, and influential organisations (e.g., high-level government actors) are often not part of the MSP or are not actively linked to by other organisations. Organisations with a central position in the knowledge network are more sought out for collaboration. The scaling of innovations is primarily between the same type of organisations across different administrative levels, but not between different types of organisations. The results illustrate the potential of Social Network Analysis and ERGMs to identify the strengths and limitations of MSPs in terms of achieving development impacts.
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RESEARCH ARTICLE
Social network analysis of multi-stakeholder
platforms in agricultural research for
development: Opportunities and constraints
for innovation and scaling
Frans Hermans
1
, Murat Sartas
2,3,4
, Boudy van Schagen
5
, Piet van Asten
6
, Marc Schut
2,3
*
1Leibniz Institute for Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Strasse 2,
Halle (Saale), Germany, 2Knowledge, Technology and Innovation Group, Wageningen University, EW
Wageningen, The Netherlands, 3International Institute of Tropical Agriculture (IITA), Kacyiru, Kigali,
Rwanda, 4Swedish University of Agricultural Sciences (SLU), Department of Urban and Rural Development,
Ulls va
¨g Uppsala, Sweden, 5Bioversity International, Quartier Kabondo, Rohero 1, Avenue 18 Septembre
10, Bujumbura, Burundi, 6International Institute of Tropical Agriculture (IITA), Kampala, Uganda
*m.schut@cgiar.org
Abstract
Multi-stakeholder platforms (MSPs) are seen as a promising vehicle to achieve agricultural
development impacts. By increasing collaboration, exchange of knowledge and influence
mediation among farmers, researchers and other stakeholders, MSPs supposedly enhance
their ‘capacity to innovate’ and contribute to the ‘scaling of innovations’. The objective of this
paper is to explore the capacity to innovate and scaling potential of three MSPs in Burundi,
Rwanda and the South Kivu province located in the eastern part of Democratic Republic of
Congo (DRC). In order to do this, we apply Social Network Analysis and Exponential Random
Graph Modelling (ERGM) to investigate the structural properties of the collaborative, knowl-
edge exchange and influence networks of these MSPs and compared them against value
propositions derived from the innovation network literature. Results demonstrate a number of
mismatches between collaboration, knowledge exchange and influence networks for effec-
tive innovation and scaling processes in all three countries: NGOs and private sector are
respectively over- and under-represented in the MSP networks. Linkages between local and
higher levels are weak, and influential organisations (e.g., high-level government actors) are
often not part of the MSP or are not actively linked to by other organisations. Organisations
with a central position in the knowledge network are more sought out for collaboration. The
scaling of innovations is primarily between the same type of organisations across different
administrative levels, but not between different types of organisations. The results illustrate
the potential of Social Network Analysis and ERGMs to identify the strengths and limitations
of MSPs in terms of achieving development impacts.
PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 1 / 21
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OPEN ACCESS
Citation: Hermans F, Sartas M, van Schagen B, van
Asten P, Schut M (2017) Social network analysis of
multi-stakeholder platforms in agricultural research
for development: Opportunities and constraints for
innovation and scaling. PLoS ONE 12(2):
e0169634. doi:10.1371/journal.pone.0169634
Editor: Frank van Rijnsoever, Utrecht University,
NETHERLANDS
Received: March 15, 2016
Accepted: December 20, 2016
Published: February 6, 2017
Copyright: ©2017 Hermans et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: This work was funded under the
framework of the Consortium for Improving
Agricultural Livelihoods in Central Africa (CIALCA),
which is funded by the Belgian Directorate General
for Development Cooperation and Humanitarian
Aid (DGD). CIALCA forms part of the CGIAR
Research Program on Integrated Systems for the
Humid Tropics (Humidtropics), and the CGIAR
Introduction
Multi-stakeholder platforms (MSPs) are increasingly seen as promising vehicles for agricul-
tural innovation and development [1,2]. In the field of agricultural research for development,
MSPs are expected to contribute to a structural and long-term engagement among stakehold-
ers for overcoming complex agricultural problems [3]. Key characteristics of complex prob-
lems in agricultural systems are their multiple dimensions (biophysical, technological, socio-
cultural, economic, institutional and political), and their embeddedness across different scales,
hierarchical levels and interdependent actors. As a result, complex problems possess inherent
uncertainties that defy prediction and linear innovation pathways [46]. They often are a mix
of socio-political issues where different world views, norms and values collide with different
interests. Consequently, proposed solutions in different scenarios can result in turning differ-
ent stakeholders into winners or losers.
The continuous engagement of various stakeholders in exploring innovations to address
these complex agricultural problems is essential for three reasons. First, stakeholder groups
can provide various complementary insights about the biophysical, technological and institu-
tional dimensions of the problem, broadening the knowledge base. By engaging in a social
learning process with each other, stakeholders can negotiate what type of innovations are tech-
nically feasible, economically viable, and social-culturally and politically acceptable [4,7,8].
Second, through their interaction and participation, stakeholder groups become aware of their
different interests, needs and objectives, but also of their fundamental interdependencies and
the need for concerted action at different levels to overcome their constraints and reach their
objectives [911]. Third, stakeholders are more likely to accept or support the implementation
of innovations when they have been part of its development process [12,13].
Multi-stakeholder approaches, including MSPs, can therefore play an important role in facili-
tating innovation to overcome complex problems and achieving development impacts [14,15].
Two key objectives for working with MSPs for agricultural development are (1) to enhance ‘capac-
ity to innovate’ in stakeholder networks, and (2) to contribute to the scaling of innovations to
achieve development impacts [16]. Over the past 5–10 years, there has been increasing enthusiasm
and optimism on the role of MSPs for agricultural innovation and scaling in developing countries.
Consequently, MSPs have been implemented on a case-by-case basis at selected sites. However,
very little evidence has been systematically collected on whether and how MSPs actually support
functions that can foster innovation and scaling.
The objectives of this paper are to explore (i) the capacity to innovate and (ii) the potential for
scaling of innovations of three MSPs situated in the different governance contexts of Burundi,
Rwanda and the South Kivu Province located in eastern Democratic Republic of Congo (in the
remainder of this paper referred to as DRC for short). To achieve these objectives we will study
these MSPs from a network perspective by analysing the linkages between different types of
stakeholder organisations and how the structure of these linkages inhibits or facilitates innova-
tion and scaling of innovation.
Key-concepts
Innovation is defined as the successful combinations of ‘hardware’, ‘software’ and ‘orgware’
that have been implemented and brought into use to serve a specific public or private purpose
[17,18]. In this view innovations not only require new technologies or tools (‘hardware’), but
also new knowledge, processes and new modes of thinking (‘software’) and a reordering of
institutions and of organisations (‘orgware’). Innovations thus emerge from the complex inter-
actions among a diverse set of public, private and civil society actors engaged in generating,
exchanging and using knowledge within a so-called Agricultural Innovation System (AIS)
Social network analysis of multi-stakeholder platforms in agricultural research for development
PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 2 / 21
Research Program on Roots, Tubers and Bananas
(RTB). We would like to acknowledge
Humidtropics, RTB, and the CGIAR Fund Donors
(http://www.cgiar.org/about-us/governing-2010-
june-2016/cgiar-fund/fund-donors-2/) for their
provision of core funding without which this
research could not have been possible. The
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.
[19,20]. The AIS framework has broadened views of agricultural innovation processes in two
important ways. First, it has recognized that actors beyond those directly involved in the agri-
cultural production chain and the agricultural research, extension and education system play a
role in innovation processes (e.g. service providers, financial sector, civil society). Second, it
stresses the importance of the constraining and enabling influence of institutions (defined as
the ‘formal and informal rules of the game’) in innovation processes [2123].
Within this AIS framework, the capacity to innovate is defined as the ability of different
groups of stakeholders to continuously identify and prioritize problems and opportunities in
the dynamic environment that they are in, and take risks and experiment with different new
combinations of technical and institutional configurations and assess the trade-offs from these
options [24]. Within an AIS framework the scaling of an innovation refers not only to the suc-
cessful adaptation and adoption of technologies but also includes the successful implementa-
tion of new institutional arrangements to expand their impact [25,26]. Two different types of
scaling are relevant in this regard. Outscaling refers to the horizontal diffusion process of inno-
vations among organisations at the same administrative level (e.g. within district, provincial,
national or supranational levels). This is more or less similar to the classic (technology) adop-
tion and diffusion model of Rogers [27]. Upscaling of an innovation refers to the institutional
uptake or embedding of processes or technologies by organisations at higher administrative
levels (e.g. across district, provincial, national or supranational levels). This process requires
institutional entrepreneurship and political influence to change rules and regulations [2830].
Collaboration among stakeholders is a central element of the MSP approach that seeks to
enhance both the capacity to innovate and the scaling of an innovation. The capacity to inno-
vate benefits from the interaction between a variety of stakeholders with access to different
sources of knowledge and power that can strengthen their collective agency [4,31,32]. At the
same time, collaboration connects actors and organisations within and across different admin-
istrative levels which is important for out- and upscaling [33,34].
In this paper we take a network perspective on collaboration which means that we focus
on the structure of the relationships between collaborating partners within an MSP. Collab-
oration processes are essentially relational in nature: they require the creation and mainte-
nance of a connection between one or more actors or organisations. In AIS, understanding
the changes in collaboration resulting from interventions such as a MSP requires monitor-
ing changes in stakeholder networks [19]. Given the relational nature of MSP activities,
Social Network Analysis (SNA) offers a framework to study and model different aspects of
agricultural innovation and scaling [35,36]. SNA can enable a better understanding of the
complexity and multi-dimensionality of multi-stakeholder innovation processes [37,38].
The central question of this paper is: What relational pattern of collaborative ties in a MSP
fosters (1) the capacity to innovate and (2) the scaling of innovations? We will answer this
question by comparing the collaborative networks of the MSPs in three countries in Central
Africa: Burundi, Rwanda and DRC. As these MSPs were relatively ‘young’ (approximately 1
year at time of data collection), we assess their innovation and scaling potential, rather than
their performance. We focus specifically on collaborative ties between organisations because
those ties are also the conduits for knowledge exchange and influencing that are crucial for
innovation and scaling processes. Based on our analysis of the innovation literature we pro-
pose that:
1. Capacity to innovate requires:
a. Broad, multidisciplinary networks with a diversity of stakeholders from business, gov-
ernment, civil society and knowledge institutes who contribute to effective social
Social network analysis of multi-stakeholder platforms in agricultural research for development
PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 3 / 21
learning processes, identify and analyse complex problems and explore innovations to
address them [20,3942].
b. The availability of powerful and influential persons or organisations within the network
that can support agenda setting, mobilize resources, provide legitimacy and a mandate
to create space (or niches) for innovation, and counteract resistance to change [9,43
45].
2. The potential for upscaling and outscaling of innovations depends on the characteristics of
the collaborative network. More specifically:
a. Dense collaborative networks facilitate the exchange and dissemination of information.
b. To ensure upscaling, organisations at different administrative levels have to be con-
nected to each other, so that information and other resources can flow easily across dif-
ferent levels.
In Table 1 these network characteristics have been summarized. It has to be noted that
these network characteristics are formulated fairly broadly and we do not want to suggest that
there is an optimum network configuration that maximizes the innovation or scaling potential
in all governance contexts. A diverse group of stakeholders who draw on different sources of
knowledge is important to solve complex problems, but when the cognitive distance between
actors becomes too large it becomes difficult to establish common ground [46,47]. Similarly,
there comes a point when the density of a network can become problematic. Isaac [40], for
example, explains how knowledge networks with high density may result in collective action
(essential for scaling) but little new information (essential for innovation), whereas a low den-
sity network may invite new information (essential for innovation) but the exchange of such
information may be impeded (essential for scaling). Powerful actors in the network can facili-
tate change, but they are also likely to be invested in the status quo and therefore can stifle
innovations that threaten their power base [48].
It is clear, therefore, that the effectiveness of an innovation network depends on the context
and the actors involved. In this regard it is helpful to think about these characteristics as the
opposite of some well-known innovation failures: sparse, disconnected innovation networks
represent a barrier, or a systemic innovation failure [21], and without influential organisations
present in the network no changes can be made at all. Furthermore, we assume that within the
context of Central Africa, the lack of innovation capacity and the resulting innovation failures
have to do with a dearth of linkages between organisations and coordination efforts across
scales, and as such the MSP have been established with the particular aim to remedy some of
these network failures [49,50].
Based on the innovation network characteristics in Table 1, we can identify a number of social
processes at the micro level that might result in empirically observable innovation networks with
high capacity to innovate and high scaling potential. We assume that when ‘opposites attract’,
Table 1. Network characteristics to evaluate capacity to innovate and scaling potential of MSPs.
Network
objective
Network process
Knowledge exchange Influence
1. Capacity to
innovate
1a. Broad networks with multidisciplinary partners
enhance social learning
1b. Centrality of influential organisations within the network facilitates
institutional entrepreneurship, agenda setting and creation of space for
experimentation
2. Scaling of
innovation
2a. Dense collaborative networks facilitate the exchange
and dissemination of information (outscaling)
2b. Multi-level networks facilitate the institutionalisation of an innovation
(upscaling).
doi:10.1371/journal.pone.0169634.t001
Social network analysis of multi-stakeholder platforms in agricultural research for development
PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 4 / 21
(a process that is in the network literature is also referred to as heterophily), it will result in a
broad and diverse network connecting different types of organisations. With regard to the capac-
ity to innovate we hypothesize that in MSPs:
1. Organisations (e.g. farmer, government, NGO, business, research) tend to form more
network links with different types of organisation.
2. Organisations that are perceived as being influential will be preferred collaboration part-
ners for stakeholders in MSPs. Therefore, influential partners should end up in more cen-
tral positions in the collaborative network.
To ensure processes of outscaling and upscaling, information and knowledge has to flow
among organisations located within and across different levels. With regard to scaling we can
hypothesize that:
3. Information travels easier in denser innovation networks, which is beneficial for scaling.
4. Organisations with knowledge will make for more attractive collaboration partners.
5. Organisations tend to form more network links across administrative levels as compared
to organisations operating at the same level (local, provincial, national or supranational).
In the remainder of this paper we will use these five hypotheses to identify and explain
the characteristics, similarities and differences of the MSP networks in Burundi, DRC and
Rwanda.
Methodology
Study sites
Data for this study were gathered within the framework the CGIAR Research Program on
Integrated Systems for the Humid Tropics (Humidtropics) that has adopted the MSP approach
for achieving its development outcomes. Research for Development Platforms and Innovation
Platforms are core tools of the Humidtopics intervention strategy to bring together relevant
actor groups and organisations, and to stimulate working together towards the realization of
development outcomes. The collaboration between Innovation Platforms and Research for
Development Platforms is expected to facilitate awareness about local innovations (tested in
Innovation Platforms) at the (sub-)national level (in the Research for Development Platforms),
which can stimulate that lessons and innovations can go to scale.
Data were collected with three Research for Development Platforms in the Action Sites of
Burundi, DRC and Rwanda. Note that the DRC study site is about double the area but half as
populous as those of Rwanda and Burundi. The selection of these three sites is based on several
interesting similarities and differences when it comes to agricultural innovation [51]. Similari-
ties are related to key agro-ecological and demographic features and agricultural productivity
challenges. In general, the region is densely populated; agricultural pressure on land is high,
and farm sizes are small (<2 ha) [52]. In these highly populated areas, soil fertility is one of the
main constraints of agricultural production, driven by (i) absence of nutrient inputs, (ii) soil
erosion and (iii) sub-optimal agricultural practices [49,53]. Differences can be found in the
governance context with the position and role of the government being different in the three
countries: DRC (very decentralised) and Rwanda (very centralised) forming the two ends of
the spectrum. As a result there are also differences in the effectiveness of public governance
(i.e. formulation and implementation of policy), which is generally perceived as low in eastern
DRC, medium in Burundi, and high in Rwanda [54,55].
Social network analysis of multi-stakeholder platforms in agricultural research for development
PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 5 / 21
Data collection, cleaning and analysis
Humidtropics initiated MSPs for innovation and scaling based on three types of stakeholder
mappings. The first approach was to identify long-term established partners of the CGIAR
centres in Burundi, eastern DRC and Rwanda. The second approach was participatory stake-
holder mapping based on Humidtropics workshops for which potential partners were invited.
The third approach was to prepare dissemination materials about the program and distribute
them in different locations [56] to encourage organisations to join the MSP. As such, different
types of organisations were provided the opportunity to form part of the MSP.
Data gathering for the network analysis was done during MSP meetings in Burundi, Rwanda
and DRC in August 2014. Data were gathered during a regular MSP meeting where ongoing
research and development activities are being discussed among the participants. Data collection
was done by the authors and was harmonised across the three countries by using a detailed pro-
tocol. A name generator (open nomination) was employed based on the following question:
“Compose a list of the names of all organisations with whom you collaborate”. In subsequent
steps respondents were asked to identify within their initial list the 5 organisations that they
viewed the most important for knowledge exchange (question asked was: “go through the list
and circle the 5 organisations in the list that are most important for knowledge exchange”), and
the 5 organisations that they viewed to be the most influential (question asked was: “Go through
the list again and now underline the 5 organisations that you think are most influential”). In
addition, the following information was collected: 1) name, gender, age and (multiple) affilia-
tion(s) (questions asked were: “Write your name on the sheet of paper”, “Indicate your gender
and age” and “Write down ALL the names of organisations/ companies/ institutes etc. that you
represent”). The data collected therefore represent a ‘one-wave snowball sample’ of the plat-
form. Since the data were gathered at the same meeting, there is some overlap between the orga-
nisations mentioned by the participants: some of the named ‘alters’ also appear on the ‘ego’ list.
For Burundi, the overlap between egos and alters was 7.0%, for DRC 7.8% and for Rwanda
5.6%.
In total, 45 respondents representing three MSPs contributed to data gathering (Table 2).
Average age of the respondents was approximately 43 years. 78% of the respondents were
male.
Data were entered and cleaned by the authors. Local MSP Facilitators supported the authors
in matching of organisational abbreviations and full names, French versus English abbreviations
and organisation names, deciphering handwriting and misspelling of organisation names and
abbreviations. Furthermore, the MSP Facilitators provided additional information on the type
of organisation (farmer organisation, NGO/ civil society, private sector, government, research
and training) and the principle administrative level at which these organisations are active
(supranational, national, provincial, district). The resulting networks are provided as csv- files
in the S1 File, that also includes the organisations’ names, abbreviations levels and typology.
Our participatory observations in the MSPs analysed in this paper enabled us to interpret the
data and results.
Table 2. Characteristics of respondents (M = male, F = female).
Country Respondents Average age Gender Total distinct affiliations
M F
Burundi 14 42 10 4 15
DRC 21 43 16 5 35
Rwanda 10 43 9 1 7
Total 45 43 35 10
doi:10.1371/journal.pone.0169634.t002
Social network analysis of multi-stakeholder platforms in agricultural research for development
PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 6 / 21
Social network analysis and Exponential Random Graph Models
We have applied exploratory social network analysis [57,58] in combination with Exponential
Random Graph Models (ERGMs). ERGMs belong to the class of statistical inference models
and are among the most popular and theoretically well-developed class of network models [59].
ERGMs are used for testing hypotheses about the social processes that might have led to the
creation and development of an empirically observed network. The statistics in these models are
based on the occurrence of certain micro-level patterns of ties that indicate specific mechanisms
of tie formation at work. Examples are preferential attachment (to popular nodes), reciprocity
between nodes (resulting in the formation of a double arrow), transitivity (friends of friends are
likely to become friends) resulting in a local triangle structure and processes of homophily in
which two nodes with the same trait are more likely to form a tie. ERGMs are used to test statisti-
cally whether the relative occurrence of such patterns is consistent with these underlying dynamic
processes of network formation. For a more detailed introduction into ERGMs see Lusher et al.,
Harris, and Lubell et al. [6062]. The analysis of network properties and ERGM specification was
done in R, using the statistical ‘statnet’ package (version 2016.9) [63], and the associated ‘ergm.
ego’ package (version 0.3.0) [64]. See S1 R–scripts for an overview of the used analysis code. The
‘ergm.ego’ package was developed especially for ego-networks. In such ego-networks the collected
data are considered to be a sample of a larger network of a known, or unknown size. In our case
we did not know the total size of the population network of the AIS in all the three countries. In
addition, the membership of the MSP is not fixed: it changes over time and the sample is therefore
necessarily only capturing a snapshot picture of the ego-networks of MSP participants of what–in
reality–is a dynamic process of collaboration and partnering. Nevertheless, the sample represents
a reliable picture of the typical ego-networks at the national levels in Burundi and Rwandan and
provincial level for DRC and as such can be used as input for ego network modelling.
The ergm.ego package is based on the finding by Krivitsky et al. [65] that it is possible to
obtain a “per capita” size invariant parameterization for dyad-independent statistics by using
an offset that preserves the mean degree (approximately equal to log(n), where n is the num-
ber of nodes in the network). Simulations have suggested this is also possible for some dyad-
dependent statistics. However, the processes of so-called ‘network self-organisation’ at the
level of the entire network (like triadic closure, degree assortativity and 4-cycles) are not incor-
porated in the ergm.ego package. In their description of the package, Krivitsky and Morris
[66] state that if the population network is not overly large the parametrization of such higher
order effects might not be necessary.
Terms were added in consecutive blocks (node level and dyad level) to examine their rela-
tive contribution to enhancing the goodness-of-fit of the models [67]. Three models were
tested and evaluated: starting with a simple random graph model (M0) (where all nodes have
an equal chance to form a tie), and adding complexity in subsequent models by adding terms
corresponding to our hypotheses at the node level (M1) and the dyad level (M2). We have
scaled all our results to a”pseudo-population” size of 1,000 for all three countries, following the
advice of Krivitsky and Morris [66].
At the node level we look at the degree (amount of ties) that organisations have within the
knowledge network to test hypothesis 3. The knowledge degree serves therefore as an indica-
tion for the perception of other actors that an organisation possesses complementary knowl-
edge. Following the AIS perspective we assume that such relevant knowledge is not limited to
research and extension organisations, but is also possessed by farmers, NGOs, businesses, etc.
To operationalise hypothesis 2 we take the indegree of organisations in the influence network
as a measure of their perceived power. Again: not only government organisations are deemed
to be powerful, but other types of organisations can also possess other forms of power [68]. At
Social network analysis of multi-stakeholder platforms in agricultural research for development
PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 7 / 21
the dyad level we look at ties between different types of organisations. A typology was made in
6 different categories of actors: 1) business, 2) farmer, 3) government, 4) non-governmental
organisations (NGOs), 5) research, training and extension, and 6) unknown. Hypothesis 1 is
thus tested by looking at the tendency for different types of organisations to form collaborative
ties. Finally, for scaling we look at the administrative level where organisations are (most)
active: 1) local, 2) provincial, 3) national, 4 supranational, or 5) unknown. Hypothesis 4 is
tested by investigating the tendency of actors working at different levels to form collaborative
ties.
Models were checked for potential degeneracy (see S2 File) and goodness-of fit through
visual inspection of the standard plots that the statnet package generates for this purpose, as
suggested by Hunter et al. [69]. Since all the models underestimated the number of organisa-
tions with a degree of 1, we fixed this amount in the models to increase the fit. To ease the
comparison of the plots, we have calculated a goodness-of-fit percentage following the example
of Harris et al. [70]. The calculated percentage is based on the proportion of the relevant degree
distribution that fall within the 95% confidence intervals of simulations based on the models.
The term relevant here is not defined for all degrees, but only those degrees where either the
results of the ergm.ego model, or the original measurement show a value unequal to 0.
Results
Descriptive network characteristics
Due to the slight overlap in egos and alters in each country, we can depict the different networks
in each country as if they are complete networks. We have thus constructed three networks for
each country (Fig 1): a collaborative network based on organisational ties (first row), a knowl-
edge exchange network (second row) and an influence network (third row). Even though ego-
networks are typically directed, we have defined collaboration and knowledge exchange as a
mutual relationship. These networks were therefore defined as undirected networks. No loops
were allowed in these networks which implies that a respondent cannot exchange knowledge or
collaborate with his or her own organisation. Influence was defined as a directed network and
respondents were allowed to consider their organisation as influential, thus including loops in
the networks. Below we will describe these networks and their implications for the capacity to
innovate and scaling in more detail.
Collaborative networks. The collaborative network is smallest in Rwanda and largest in
DRC (Table 3). In all three countries, the networks are dominated by NGOs in terms of com-
position. In Burundi and Rwanda, government organisations rank second. In DRC the second
place is taken by research and training organisations (21%), but the difference with govern-
ment organisations (18%) is rather small. Almost absent in all three countries are private sector
organisations.
In Table 4, the collaborative networks are broken down according to the administrative
level that the organisations operate at. In the collaborative networks in Rwanda and DRC, the
majority of organisations operate at the supranational level. In Burundi, the national level is
the best represented. In Rwanda and Burundi some levels are missing in the network. In
Rwanda, the provincial level is almost completely absent and in Burundi the district level is
almost completely absent.
Knowledge exchange networks. The knowledge networks in Burundi and DRC show
multiple components, which means that the knowledge networks are disconnected and thus
will inhibit scaling (Fig 1). Our data show that knowledge is being exchanged between different
types of stakeholder groups (Table 5). However, for all three countries farmers and businesses
are the smallest categories of organisations that knowledge is exchanged with. Somewhat
Social network analysis of multi-stakeholder platforms in agricultural research for development
PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 8 / 21
surprisingly, it is not the research and training organisations that dominate, but the NGOs that
make up the largest part of the composition of the knowledge networks in Burundi and
Rwanda. These NGOs often operate at the international level (Table 6).
However, even if NGOs are more present within the network, it is some of the research and
training organisations that hold the most central position in the knowledge network, as they
Fig 1. Overview of MSP networks for collaboration, knowledge exchange and perceived influence.
doi:10.1371/journal.pone.0169634.g001
Table 3. Collaborative network composition and characteristics.
Farmer organisations NGO Private sector Government Research and training Unknown Total nodes (g) Total ties (L)
Burundi 27(19%) 51(36%) 8(6%) 32(23%) 19 (13%) 5 (5%) 142 (100%) 237
DRC 45 (16%) 82 (29%) 24 (9%) 50 (18%) 59 (21%) 20 (7%) 280 (100%) 903
Rwanda 14 (13%) 36 (33%) 6 (6%) 32 (30%) 20(19%) 0 (0%) 108 (100%) 142
doi:10.1371/journal.pone.0169634.t003
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have the highest degree of the organisations within the knowledge network (Fig 2). In DRC
the research and training organisations have the largest share of the knowledge network, but it
is an NGO that has the highest degree. However, three research and training organisations are
also among the organisations with a central position in the knowledge network in DRC.
Influence networks. Tables 7and 8give an overview of the influence networks in terms
of composition and administrative scale. NGO, government and research organizations are in
all countries the most important source of power, although their ranking is slightly different
(Fig 3). In Burundi, the government is the largest type of actor in terms of influence. In Rwanda
and DRC the NGOs are the most important. Influence in these countries is thus mainly derived
from legislative power (government), monetary power (government/NGOs), or knowledge
(research institutes/ NGOs).
In all countries the international level is the most important in the influence network, fol-
lowed by the national level. The exception is DRC where the national level is not so present,
but this has to do with the fact that the MSP is organized at the provincial level (i.e. South
Kivu) and not at the national level.
Results of the ERGMs
Four models were tested and evaluated: starting with a simple random graph model (M0) and
adding complexity in subsequent models by adding terms corresponding to our hypotheses at
the node level (M1) and the dyad level (M2). Fig 4 shows the result of the goodness-of-fit per-
centage for the degree distribution across the 3 models. The differences between goodness-of-fit
between the M1 and M2 models is very small and they lie within the same range. In order to
compare the countries and test our hypotheses we have used the results of the M2 models for all
three countries. Fig 5 shows the goodness-of-fit for the model parameters for these model fits.
Table 9 gives an overview of the results of the full ERGM models (M2). An overview of the
ERGM results of M0 and M1 can be found in S1 Table. Regarding our first hypothesis, we find
that links between the same type of organisations are positively correlated at a significant level
in two of the three countries (Burundi and DRC). That means that instead of heterophily, we
find a tendency for homophily (‘birds of a feather flock together’) as indicated by the positive
estimates. Organisations of the same type have a 1.36 times chance to form a collaborative tie
in Burundi and a 1.56 times greater chance in DRC.
Table 4. Number and (percentage) of organisations per level in the collaborative network.
District Provincial National Supranational Unknown Total organisations
Burundi 3 (2%) 33 (23%) 57 (40%) 45 (32%) 4 (3%) 142 (100%)
DRC 65 (23%) 57 (20%) 36 (13%) 101 (36%) 21 (8%) 280(100%)
Rwanda 24 (22%) 1(1%) 23 (21%) 60 (56%) 0 (0%) 108 (100%)
doi:10.1371/journal.pone.0169634.t004
Table 5. Composition of the knowledge exchange networks.
Business Farmer Govern-ment NGO Research and training Unknown Total Share of collaborative network
Burundi 5 8 10 21 14 3 61 43%
(8%) (13%) (16%) (34%) (23%) (5%) 1
DRC 6 7 15 18 24 7 77 28%
(8%) (9%) (19%) (23%) (31%) (9%) 1
Rwanda 0 4 3 14 12 0 33 31%
(0%) (12%) (9%) (42%) (36%) (0%) 1
doi:10.1371/journal.pone.0169634.t005
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The second hypothesis regarding the effect of power and influence is not substantiated. In
Burundi we find a negative estimate (-0.23) that indicates that for each additional indegree an
organisation has in the influence network, it is 0.79 times less likely to form a collaborative tie.
For DRC we also find such a negative estimate, although here the effect is not significant. In
Burundi, influential organisations are either collaboration averse, or they are being ignored by
other organisations for collaboration. In Rwanda the estimate also fall outside the cut-off rate
for significance of p <0.05.
The strongest effects we find relate to the effect of the knowledge degree of organisations as
indicated in hypothesis 4. We find that knowledge degree is positively correlated with the
amount of ties an organisation has in the collaborative network in all three countries. This
effect is strongest in Burundi where an additional degree in the knowledge network corre-
sponds to 1.30 times the number of ties in the collaborative network. For Rwanda and DRC
this effect is also positive albeit smaller (with odds ratios of 1.23 and 1.06 respectively). This
confirms hypothesis 4 that knowledge exchange is significantly correlated with the amount of
ties in the collaborative network.
Table 6. Number and percentage of organisations per level in the knowledge networks.
District Provincial National Supranational Unknown Total
Burundi 1 5 25 26 4 61
(2%) (8%) (41%) (43%) (7%)
DRC 10 14 11 34 8 77
(13%) (18%) (14%) (44%) (10%)
Rwanda 1 0 10 22 0 33
(3%) (0%) (30%) (67%) (0%)
doi:10.1371/journal.pone.0169634.t006
Fig 2. Distribution of knowledge degrees among different types of organisations.
doi:10.1371/journal.pone.0169634.g002
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With regard to heterophily between administrative levels that was proposed in hypothesis 5
we found significant effects for the Burundi and Rwanda MSPs. Organisations working at the
same level have a smaller chance of forming a tie (indicating a higher chance for organisations
at different levels to form ties).
Hypothesis 3 states that denser networks perform better with regard to scaling. Results of
the ERGM helped us to calculate the densities and the mean degrees of the three networks.
Because of the unknown complete network size, they could not be derived directly from the
sampled ego network. However, the results of the ergm.ego were scaled to a network of a 1000
nodes for all the three countries which allowed us to compare the tendency of the organisa-
tions to form ties.
Fig 6 shows the three boxplots resulting from a simulation using the ergm.ego results to
draw 1000 networks for a network of 1000 nodes. It shows that densities and mean degrees for
Rwanda are the lowest and for DRC are the highest. Based on this result we conclude that
DRC has the highest propensity to form collaborative ties and Rwanda has the lowest propen-
sity to form a dense network. Even though it is not possible to compare these figures against
an objective benchmark, we can assume that scaling in DRC will likely have the best results,
compared to Burundi and Rwanda.
Discussion
Limitations of the study and sampling
We have conceptualized the collaborative ties between organizations as the connections through
which knowledge and influence are effectuated and consequently we did not ask respondents to
separately name the organisations which they collaborate with, exchange knowledge with and
find influential. Instead we asked to name their collaborative partners and then choose from
within this list to name the 5 organisation with whom the exchange the most knowledge, and the
5 organisations which they consider to be the most influential. The formulation of the question
might lead us to exclude important knowledge, or influential organisations within the broader
AIS. Furthermore, it might provide a bias towards highly connected organisations within the
Table 7. Composition of the influence networks and MSPs.
Business Farmer Govern-ment NGO Research and training unknown total
Burundi 2 (4%) 4 (9%) 16 (35%) 11 (24%) 13 (28%) 0 46
Platform members 2 0 2 4 4 0 12 (26%)
DRC 9 (11%) 12 (14%) 16 (19%) 24 (28%) 19 (22%) 5 (6%) 85
Platform members 1 3 3 5 2 1 15 (17%)
Rwanda 1 (3%) 2 (6%) 9 (26%) 13 (38%) 9 (26%) 0 34
Platform members 0 1 3 2 1 0 7 (21%)
doi:10.1371/journal.pone.0169634.t007
Table 8. Composition of the influence networks and MSPs.
District Provincial National Supranational Unknown Total
Burundi 1 3 18 24 0 46 32.4%
2.2% 6.5% 39.1% 52.2% 0.0% 100.0%
DRC 14 21 10 34 6 85 30.4%
16.5% 24.7% 11.8% 40.0% 7.1% 100.0%
Rwanda 3 0 12 19 0 34 31.5%
8.8% 0.0% 35.3% 55.9% 0.0% 100.0%
doi:10.1371/journal.pone.0169634.t008
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collaborative network with regard to knowledge and influence degrees. However, the results of
the ERGM calculations (especially for the influential organisations) do not suggest this. On the
contrary, we conclude that more influential organisations are less likely to form collaborative
bonds in Burundi. The conclusion that organisations with a high knowledge degree are more
Fig 3. Distribution of influence indegrees among different types of organisations.
doi:10.1371/journal.pone.0169634.g003
Fig 4. Goodness of fit over the degree distribution for different model forms.
doi:10.1371/journal.pone.0169634.g004
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PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 13 / 21
likely to form collaborative bonds, might indeed suffer from this bias. This does provide a limita-
tion of this study, but given the well-established link in between knowledge creation and innova-
tion in the existing innovation literature, we think that this bias did not influence the
conclusions of our study.
Another limitation of the study has to do with our decision to model the networks as ego-
networks. The data sampling for the networks are based on a one-wave snowball sample and
this is not exactly the same as the ego networks described by Krivitsky and Morris (66). By
modelling our collaborative networks as ego networks we have essentially ignored some of the
additional information we have in our sample regarding alter-alter ties that could be used to
model ‘higher-order effects’ such as tendencies for triadic closure. By applying ergm.ego
modelling we have assumed that the overlap between egos and alters is small enough (for
Burundi 7.0%, for DRC 7.8% and for Rwanda 5.6%) to be able to ignore these triadic effects.
However, it would be good to check this assumption by gathering more information on the
networks of actors within AIS that are linked to the MSP but not a direct member of it in a
later stage.
Fig 5. Goodness of fit diagnostics over model (M2) parameters.
doi:10.1371/journal.pone.0169634.g005
Table 9. Exponential Random Graph Models for collaborative networks in Central Africa.
Burundi (M2) DRC (M2) Rwanda (M2)
Estimate Std. Error Odds ratio Sig. Estimate Std. Error Odds ratio Sig. Estimate Std. Error Odds ratio Sig.
Network size
adjustment
a)
-6.90 -7.02 *-6.88
Edges 0.87 0.32 2.39 *0.081 0.13 32.28 *1.57 0.56 4.82 *
Degree (1) 1.95 0.52 7.01 *2.94 0.4 18.82 *3.74 1.05 41.98 *
Knowledge degree 0.26 0.02 1.30 *0.06 0.04 1.06 *0.19 0.03 1.21 *
Influence indegree -0.23 0.08 0.79 *-0.03 0.03 0.98 0.08 0.08 1.08
Administrative level -0.54 0.18 0.59 *-0.04 0.12 0.96 -0.42 0.19 0.66 *
Organisational type 0.30 0.14 1.36 *0.45 0.14 1.56 *0.16 0.12 1.17
a
Network size adjustments are fixed by offset and are not estimated: pseudo-population = exp(-netsize adj.).
*Significant effect at p<0.05.
doi:10.1371/journal.pone.0169634.t009
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Capacity to innovate in the MSP networks
Regarding the capacity to innovate we find that the absence of businesses in the collaborative
network all three countries means that stakeholder representation in the networks is not pro-
portionally balanced, which might negatively affect the capacity to innovate. The MSP may
respond less to the needs of the private sectors and entrepreneurial activities which forms a core
function of technological innovation networks [45,71]. The knowledge networks are dominated
by NGOs (in terms of presence) and research and extension organisations (in terms of degree
centrality). The underrepresentation of farmer organisations and businesses in the knowledge
exchange networks of Burundi and DRC may further exacerbate this potential weakness of the
three MSPs in terms of their capacity to innovate. In other words, capacity for innovations that
require a high level of knowledge exchange (e.g. local adaptation of cropping practices) is rela-
tively weakly developed in these MSPs. A potential explanation is that the MSPs in this study
prioritised removing institutional rather than technical barriers to agricultural development [16].
The results of the ERGMs showed that the collaborative networks are important conduits
for knowledge exchange, as the organisations that possess complementary knowledge are
more likely to be collaborated with. In contrast, the effects of influence differ from country to
country.
When comparing the densities (and mean degree of collaboration) of the collaborative net-
works in the three countries, we observe that DRC is highest followed by Burundi and Rwanda.
This means that–on average–organisations in DRC are collaborating more with other organi-
sations than in Burundi and Rwanda. Based on our proposition, the capacity to innovate in
DRC will benefit from this dense network. A potential explanation for the high mean degree in
DRC is that general partnerships as well as social capital among organisations are relatively
more developed. This is out of necessity since state governance systems to support farmers and
other stakeholders are much weaker in DRC. Based on a study of social capital among farmers
Fig 6. Boxplots for density based on 1000 generated networkswith ERGMs for the three countries.
doi:10.1371/journal.pone.0169634.g006
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PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 15 / 21
in DRC, Lambrecht et al. [72] concluded that social capital indicators do not only affect aware-
ness, but also capacity to innovate (which they refer to as “try-out”). In Rwanda the state fulfils
a much stronger governance role in the network. Burundi is a mix of both the governance
models in Rwanda (with a centralised government) and DRC (where the government role is
taken over by NGOs), with a mix of government and NGO influence.
Influential organisations are less likely to be collaborated with in Burundi and capacity to
innovate could suffer as there may be insufficient actors who can create space to experiment
and create legitimacy of new innovations.
Scaling of innovation in the MSP networks
The structure of the collaboration networks, the knowledge networks and the influence networks
can tell us about the potential of the MSP to support scaling of innovation within levels (outscal-
ing) and across different levels (upscaling). The collaborative networks analysed in this study
were dominated by supranational and national organisations (associated with the National Agri-
cultural Research System—NARS), whereas local organisations were mostly absent. The central
position of the NARS in the knowledge networks provides both opportunities and constraints for
scaling of innovation. NARS and their extension systems form part of broader AIS that have the
ability and infrastructure to reach many farmers and other stakeholders [73]. However, incum-
bent research and training systems have path-dependencies, sunk investments and a certain insti-
tutional logic, which is not easy to change and whose efficiency and innovation capacity is often
low [74,75]. The question is therefore whether the prominent placement of these types of organi-
sations within the MSP networks will foster or hamper the removal of institutional barriers to
innovation and scaling.
In all three countries, the local and provincial levels are mostly absent in the influence
and knowledge networks, which might indicate poor connectivity between this level and the
national level of the MSP, and vice versa. Other studies confirm that MSPs that are imple-
mented in such linear systems will reinforce the top-down transfer of innovation paradigm,
rather than foster systems approaches where innovation emerges from interactions between
different types of stakeholder groups across different levels [16].
The results of the ERGMs indicate that, at least in Burundi, there is a clear tendency for
organisations that operate at different levels to form a link. This will contribute positively to
scaling. The results of the ERGM in Burundi show strong homophily between the same type
of organisations, and heterophily when it comes to the administrative level. This indicates a
scaling process in which organisations are sharing knowledge that is relevant for their type of
organisation. Scaling is thus done mostly within the same type of organisation because no
‘translation’ of knowledge is necessary between organisations that use the same type of ‘institu-
tional logic’ [76,77].
Recommendations for policy and further research
This paper provides a first analysis of the early stages of the MSP networks in the three coun-
tries. Continuous mapping of MSP networks over time will enable a longitudinal analysis of
network evolution and also link it to the actual performance of the MSP with regard to achiev-
ing development impacts.
Based on the results of this study we can make some recommendations for the MSPs in the
three countries based on their current structural characteristics and deficiencies, combined
with insights in the underlying processes that are likely to have influenced the networks forma-
tion. Such insights could be used proactively to think about innovation network ‘architecture’
or ‘building’ to achieve specific types of innovations, innovation processes or scaling pathways
Social network analysis of multi-stakeholder platforms in agricultural research for development
PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 16 / 21
[20,78,79]. In all countries this recommendation has to do with the inclusion of more farmer
and business representatives within the MSP, to ensure that innovation and scaling is more
end-user inclusive. For DRC and Burundi more attention to developing the knowledge
exchange network is necessary in order to connect the different parts of the network (across
hierarchies and spatial scales).
Conclusions
In this paper we have explored the potential for innovation and for scaling of innovations of
three MSPs for agricultural research and development in Rwanda, DRC and Burundi. A series
of propositions and hypotheses that are based on innovation and scaling literature have guided
us in comparing the collaborative, knowledge and influence networks and functions associated
with these MSPs in contrasting governance contexts.
With regard to the capacity to innovate we observed that all three MSP networks are domi-
nated by NGOs with an apparent lack of involvement of the business sector. The dominance
of development organisations and lack of entrepreneurial capacity in these networks may hin-
der social learning and the development of innovations that are commercial and respond to
end-user needs. Knowledge plays an important role in the innovation network and the amount
of knowledge exchange is positively correlated with the amount of collaborative ties an organi-
sation has within the innovation network. In DRC and Burundi the decentralised governance
structure seems to create a problem in that MSPs are not strongly linked to the most influential
agencies, which could negatively affect their legitimacy and create obstacles for achieving insti-
tutional (policy) innovations and upscaling for impact.
The MSP networks are dominated by supranational and national organisations, whereas
local organisations were mostly absent. Such networks are thus less geared towards the outscal-
ing of knowledge intensive innovation and their local adaptation to diverse end-users and
environments. The study illustrates that MSP networks are diverse and context-specific. We
propose that MSPs should not be used as blueprint vehicle for supporting innovation and scal-
ing, but that more research is required to understand how the institutional setting (e.g. gover-
nance) and under-representation of certain actors (e.g. private sector) affect the ability of
MSPs to stimulate capacity to innovate and achieving development impact at scale.
Supporting information
S1 File.
(ZIP)
S2 File.
(ZIP)
S1 R-scripts.
(R)
S1 Table.
(DOCX)
Acknowledgments
This work was carried out under the framework of the Consortium for Improving Agricultural
Livelihoods in Central Africa (CIALCA) that is funded by the Belgian Directorate General for
Development Cooperation and Humanitarian Aid (DGD). CIALCA forms part of the CGIAR
Research Program on Integrated Systems for the Humid Tropics (Humidtropics), and the
Social network analysis of multi-stakeholder platforms in agricultural research for development
PLOS ONE | DOI:10.1371/journal.pone.0169634 February 6, 2017 17 / 21
CGIAR Research Program on Roots, Tubers and Bananas (RTB). We would like to acknowl-
edge Humidtropics, RTB and the CGIAR Fund Donors (http://www.cgiar.org/about-us/
governing-2010-june-2016/cgiar-fund/fund-donors-2/) for their provision of core funding
without which this research could not have been possible. The authors highly appreciate all
multi-stakeholder platform facilitators and members who collaborated with us and provided
data and insights necessary for this study.
Author contributions
Conceptualization: M. Schut FH M. Sartas.
Data curation: M. Schut M. Sartas BvS.
Formal analysis: FH.
Funding acquisition: PvA BvS M. Schut.
Investigation: M. Schut FH M. Sartas PvA BvS.
Methodology: FH M. Sartas.
Project administration: M. Schut PvA.
Resources: PvA BvS M. Schut.
Software: FH.
Supervision: PvA M. Schut.
Validation: M. Schut M. Sartas PvA BvS FH.
Visualization: FH.
Writing – original draft: M. Schut FH M. Sartas.
Writing – review & editing: FH M. Schut M. Sartas PvA BvS.
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... The second way, termed 'scaling of innovations,' conceptualizes innovations as interdependent practices involving technological, organizational, and institutional change. This implies scaling in the form of systemic change toward a conducive institutional environment for innovation (up-scaling) (Hermans et al. 2013(Hermans et al. , 2017. We refer to these two interdependent types of scaling by using two complementary lenses for the analysis of innovation processes in agriculture and nutrition. ...
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Chapter
Overcoming challenges for crops, people and policies in Central Africa – the story of CIALCA stakeholder engagement The Great Lakes region is a beautiful area, abundant in hills, people and conflicts. Its high altitude and cooler climate make it ideal for crops. But soils have been exhausted, spare land is no longer available, and competition and struggle for resources has marked much of the region’s history of the past 50 years. Many farmers in parts of this region rank among the most food insecure and malnourished on earth. It is in this context that the Consortium for Improving Agriculture-based Livelihoods in Central Africa (CIALCA) was launched in 2006. A baseline survey revealed that more than 60% of the population in Central Burundi and South Kivu were food insecure and had very few opportunities to diversify income with off-farm activities. Farm sizes are too small, although DRC still has some spare land. Existing land tenure arrangements do not encourage farmers to invest in soil and water conservation. Different types of ‘innovation platforms’ emerged across different levels. “At that point in time we never heard of ‘innovation platforms’, says Bernard Vanlauwe, CIALCA Director. These platforms simply emerged out of need, which was key for their crucial role in fostering adaptive collaboration between different groups of stakeholders, and CIALCA’s impact and reputation in the region. After 6 years of collaborative work, farm productivity had been raised by 27% leading to household incomes to increase by 19%. Protein intake subsequently went up by 12% while demand for innovation technologies also increased by 77%. While progress was good, the consortium agreed that it should improve by taking a more holistic approach to its research for development: integrate livestock, gender and business planning. The systems learning and policy engagement could also be strengthened further to deal with issues like land tenure that requires much more system based interventions beyond land conservation trials. The fact that there was platform at different levels starting with population around the trials to field and action sites, allowed experience sharing and cross learning. Country to country learning played big roles like in the case of banana-coffee intercropping policy engagement in Rwanda that was facilitated by the three country research results from Rwanda, Uganda and Burundi. The dynamism that allowed focused collaborative generation and regional based exchange of information tailored to partner’s needs within and between countries, was key innovation strength in this regional initiative. Furthermore, CIALCA has trained over 20 PhDs and 50 Masters and hundreds of Bsc students who now occupy strategic jobs like Directors and Department Heads in national research institutes and beyond. These are now powerful ‘change agents’ who can collaborate with CIALCA as it evolves into the Humidtropics CGIAR Research Program. This case highlights the role of stakeholder engagements in generating and disseminating knowledge to address challenges between crops, people and polices in this beautiful and sometimes volatile area of Central Africa.