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How Dynamics of Learning are Linked
to Innovation Support Services: Insights
from a Smallholder Commercialization
Project in Kenya
a a a
Catherine W. Kilelu , Laurens Klerkx & Cees Leeuwis
a
Knowledge, Technology and Innovation Group, Wageningen
University, Hollandseweg 1, Wageningen, 6706 KN, Netherlands
Published online: 25 Sep 2013.
To cite this article: Catherine W. Kilelu, Laurens Klerkx & Cees Leeuwis , The Journal of
Agricultural Education and Extension (2013): How Dynamics of Learning are Linked to Innovation
Support Services: Insights from a Smallholder Commercialization Project in Kenya, The Journal of
Agricultural Education and Extension, DOI: 10.1080/1389224X.2013.823876
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Journal of Agricultural Education and Extension
2013, 120, iFirst
How Dynamics of Learning are Linked to
Innovation Support Services: Insights from
a Smallholder Commercialization Project
in Kenya
Downloaded by [Wageningen UR Library] at 07:15 26 September 2013
CATHERINE W. KILELU, LAURENS KLERKX and CEES LEEUWIS
Knowledge, Technology and Innovation Group, Wageningen University, Hollandseweg 1, Wageningen,
6706 KN, Netherlands
ABSTRACT Purpose: The important role of learning is noted in the literature on demanddriven
approaches to supporting agricultural innovation. Most of this literature has focused on macro-
level structural perspectives on the organization of pluralistic innovation support systems. This has
provided little insight at the micro-level on the dynamics of demand articulation, and the related
interplay of matching farmers’ demand with supply of innovation support services. This paper
contributes to understanding this interplay using the concept of the dynamic learning agenda.
Design/methodology/approach: We present a case study of a project supporting smallholder
commercialization of onions in Kenya. Data were collected in selected project sites over seven
months using key-informant interviews, focus group discussions, participant observation at various
meetings and project document reviews.
Findings: The results show that because learning in agricultural innovation processes is dynamic,
static notions of demand articulation and related support are inadequate. Supporting learning and
innovation requires an understanding of how farmers’ demand evolves, a flexible matching process
with various innovation support services to achieve ‘best-fit’, and an awareness of sometimes
competing interests of actors.
Practical implications: The findings are useful for enhancing support of innovation processes by
pointing to the need for paying attention to evolving demands and how these are matched with the
right type of services, guided by effective monitoring in order to adapt the dynamic learning
agenda accordingly.
Originality/value: We add to the debate on demand-driven approaches to innovation with a
dynamic analysis of pluralistic innovation support service provisioning, which has mainly been
analyzed statically.
KEY WORDS: Dynamic learning agenda, Demand articulation, Innovation support services,
innovation brokering, Learning-oriented monitoring, Reflexivity
Correspondence address: Catherine W. Kilelu, Knowledge, Technology and Innovation Group, Wageningen
University, Hollandseweg 1, Wageningen, 6706 KN Netherlands. Email: catherine.kilelu@wur.nl
1389-224X Print/1750-8622 Online/13/010001-20 # 2013 Wageningen University
http://dx.doi.org/10.1080/1389224X.2013.823876
2 C.W. Kilelu et al.
Introduction
In the changing agricultural development context in developing countries, learning in
innovation processes is important to address challenges and opportunities facing
smallholder farmers (World Bank 2006). The imperative for learning in innovation is
linked to recent insights on innovation processes as knowledge-intensive, non-linear,
interactive and inherently unpredictable, and accompanied by risk, conflict and
uncertainty (Hall and Clark 2010; Leeuwis and Aarts 2011; Smits 2002). Following
these insights on innovation, it has become recognized that if agricultural innovation
is to be adequately supported, it is necessary to re-conceptualize advisory services
as a broad range of innovation support services (Christoplos 2010; Leeuwis & van
den Ban 2004). These should be provided in response to growing demands from
farmers and other stakeholders (demand-driven) and cover a varied range of support
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services. These include articulating innovation needs, accessing knowledge and
technologies, enhancing entrepreneurial capacity, building multi-actor linkages
and networks, facilitating action learning and experiments (for example, Farmer
Field Schools), organizing farmers and mediating conflict (Christoplos 2010; Klerkx
and Leeuwis 2008; Rivera and Sulaiman 2009). Establishing an adequate match
between demand and supply of these various innovation support services is
important, especially in the context of smallholder agricultural development in
sub-Saharan Africa (SSA), where the sector is hampered by various socio-technical
and institutional challenges (Hounkonnou et al. 2012; Poulton et al. 2010; World
Bank 2007).
The literature on demand-driven approaches to supporting agricultural innovation
has so far mainly focused on analyzing, from a macro-level structural perspective, the
challenges of optimally matching the needs of farmers (demand side) to innovation
support services (supply side) in increasingly pluralistic innovation support service
systems (Birner et al. 2009; Christoplos 2010; Klerkx and Leeuwis 2008; Parkinson
2009; Swanson & Rajalahti 2010). These studies indicate that the systems consist of a
wide array of actors (for example, public extension, private advisors, agri-business
companies, researchers) that undertake a broad range of privately or publicly funded
innovation support functions. Thus, a ‘best-fit’ between demand and supply should
be sought by choosing services from a ‘menu of options’ from the supply side (cf.
Birner et al., 2009). They do not, however, investigate how choices from this menu
are made in a dynamic innovation process. Recent work has also pointed to the
important role of so-called innovation intermediaries that undertake a brokering
role to improve the match of demand and supply of innovation support services
and hence enhance innovation processes (Kilelu et al. 2011; Klerkx and Leeuwis
2008; Leeuwis and Aarts 2011). However, these studies have mainly focused on
characterizing types of innovation intermediaries and the functions they provide.
These studies thus still provide little insight at the micro-level of innovation projects,
on the interplay between articulating demands and matching these demands with
supply of appropriate innovation support services, and the related dynamics of
learning that accompany such innovation processes. While some work has indicated
that specifying farmers’ needs and demands most probably require continuous re-
articulation (Chowa et al. 2013; Kibwika et al. 2009; Klerkx and Leeuwis 2009a), it
has not explored this process in detail. Also, recent studies on innovation platforms
How Dynamics of Learning are Linked to Innovation Support Services 3
that highlight learning processes in multi-actor networks (Kilelu et al. 2013; Nederlof
et al. 2011) fall short of analyzing this evolving process in relation to matching
demand for innovation support services to their supply.
This paper seeks to contribute to addressing these gaps in the literature by
deepening insights on understanding learning processes in agricultural innovation in
connection to the role of innovation support services, using a case study of an
agricultural development project on smallholder commercialization of bulb onions
in Kenya. The main research question the paper addresses is: how did the project
support the matching of farmers’ innovation support demands to innovation support
service provisioning within an evolving learning process? In section 2 we briefly review
literature and build a conceptual framework for the study. We then present the case
study design and the findings in the subsequent sections, and end with a discussion on
the theoretical and policy implications of our findings in connection to the debate on
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demand-driven advisory services and their role in enhancing innovation processes.
Conceptual Framework: Dynamic Learning Agenda and the Matching of Demand and
Supply of Innovation Support Services
There are diverse theories for understanding learning processes. Given the purpose of
this paper, our goal is not to look in depth at these different theories that provide a
broad conceptual understanding of learning, intersecting between individual and
collective processes, as these have been described elsewhere (see Blackmore 2007; and
Loeber et al. 2007 for a detailed review of key conceptual issues in learning such as
single or double loop learning and learning as a cognitive or a social process).
Instead, we study learning in relation to supporting agricultural innovation, by
looking at processes of formulating a learning agenda triggered by questions or
analysis of problems and opportunities which continually emerge in unfolding
innovation processes (following Regeer 2009; van Mierlo et al. 2010). Such analysis
usually leads to the identification of needs for knowledge and other resources
necessary for innovation (for example, technologies, research, advisory services,
funding etc.), which in turn triggers demand for various innovation support services
(Klerkx and Leeuwis 2008; Smits 2002; Sumberg and Reece 2004). The conceptua-
lization of a learning agenda is hence connected to the notion of demand articulation
in innovation processes. Some scholars have stated that when seeing innovation as a
complex process involving interactive creation of knowledge, the ‘market metaphor
of demand and supply’ paradoxically suggests adherence to a linear perspective on
innovation (Hall and Clark 2010; Klerkx 2008; Leeuwis 2000). However, since
innovation support is embedded in services, and the demand of these services is
usually not completely determined ex-ante then matching demand and supply leaves
space for co-creation (see also Klerkx 2008; Sarewitz and Pielke 2007).
In the literature on agricultural innovation support and advisory services, the
concept of demand articulation has often implied a notion of demand that is tied to
economic elements such as willingness and ability to pay and has been related mainly
to financial mechanisms (for example, voucher schemes, competitive bids for
extension services, privatization) for optimizing demand and supply of services or
inputs in pluralistic advisory systems (Birner et al. 2009; Christoplos 2010; Klerkx
et al. 2006; Parkinson 2009). However, in line with ideas of a learning agenda, the
4 C.W. Kilelu et al.
notion of substantive demand noted in innovation studies is more relevant here.
Substantive demand articulation is about concretizing unspecified, sometimes latent
needs into clear demands through dialogue between the ‘demand’ and ‘supply’ sides
of innovation support services to effectively guide the formulation and provision of
relevant innovation support services (Boon et al. 2011; Klerkx et al. 2006; Leeuwis
and van den Ban 2004).
In the changing agricultural context in developing countries, with a renewed focus
on increased market orientation of smallholder farmers, there is recognition that
innovation goes beyond technology development and use. It is seen to include building
capacities for producers to be more strategic about their enterprises, strengthening
farmer organizations and more broadly streamlining actor linkages in agricultural
value chains (Chowa et al. 2013; Christoplos 2010; Swanson and Rajalahti 2010). Thus,
supporting innovation entails providing both technical and generic business (entrepre-
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neurial) support services, which has been recognized already in the context of developed
countries (Nieuwenhuis 2002; Phillipson et al. 2004). Furthermore, innovation support
services are not always tied to support of private demands of specific actors but also
to demands related to public or societal interests such as those related to sustainability
issues. These demands are often conflictive and are negotiated in innovation processes
(Klerkx and Jansen 2010; Leeuwis 2000).
Generally, the articulation of demands in innovation processes has been looked at
as a rather static process, with demand articulation taking place at the start of an
innovation process through exercises such as diagnostic studies or needs assessments
(Hall et al. 2006; Parkinson 2009; Ro¨ling et al. 2004). However, understanding that
innovation is a continuous process of planning, acting, reflecting and readjustment
implies that the learning agenda should be dynamic and needs to continuously adjust
in response to opportunities and problems that emerge over time and are context
specific (Regeer 2009; van Mierlo et al. 2010). As studies have shown, this process
is often facilitated by various types of intermediary actors (Boon et al. 2011; Kilelu
et al. 2011; Klerkx and Leeuwis 2008).
As Figure 1 conceptually outlines, the dynamic learning agenda entails con-
tinuously (re-) articulating needs and demands and consequently matching them to
action, often supported by various innovation support services. This requires that the
intermediary actors facilitate reflexive monitoring and capture feedback, to identify
emerging demands and match these demands with innovation support services. This
learning process is to guide the continuous adaptation of goals and plans in order to
ensure the support is not mismatched thus enhancing the interventions (Leeuwis and
van den Ban 2004; Regeer 2009; van Mierlo et al. 2010).
Case Description
We apply the conceptual framework outlined in the previous section to analyze
an on-going project implemented by Farm Concern International (FCI), a non-
governmental organization that is supporting the commercialization of onions by
smallholders in Kieni east and west districts, in central Kenya (Farm Concern
International 2010). Despite favourable conditions for bulb onion farming in various
regions in Kenya, a deficit in supply of locally produced onions has necessitated the
importation of the produce, mainly from Tanzania. Studies have shown that onion
How Dynamics of Learning are Linked to Innovation Support Services 5
Needs diagnosis/ Needs diagnosis/ Needs diagnosis/
demand articulation demand articulation demand articulation
(Mis)match
(Mis)match
(Mis)match
reflection
Feedback
reflection
Feedback
Actions supported by Actions supported by
Actions supported by
and
various innovation
and
various innovation various innovation
support services support services
support services
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Monitoring and Monitoring and Monitoring and
evaluation evaluation evaluation
(a continuous process) (a continuous process) (a continuous process)
Dynamic learning process facilitated by an innovation intermediary actor
Figure 1. Conceptualization of a dynamic learning agenda.
Sources: Authors; Regeer, 2009; Klerkx and Leeuwis 2009.
yields in Kenya are considerably low and of lower market quality (for example,
storability and visual appearance) than those from Tanzania. This poor performance
has been linked to the predominant use of low yielding open pollinated varieties
(OPV) coupled with challenges in weed and pest management, poor post-harvest
practices and marketing (Koenig et al. 2008; Muendo and Tschirley 2004; Waiganjo
et al. 2009). These challenges and the identified market opportunity provided the
impetus for supporting the onion commercialization project.
This was a scaling-up project that started in 2010 following an initial pilot
implemented in 2005 in the same region. The project areas (Kieni districts) are
located in the drier part of the central region in Kenya, but are noted to have
potential for intensive onion production with high market returns. The farmers in
Kieni operate in diverse, complex, agro-ecological and socio-economic conditions
and grow varied staple and horticultural crops. The project goal was to facilitate
improved production and post-harvest management practices and to strengthen
linkages to credit and output market channels, all aimed at boosting productivity and
profitability of onion farming for the smallholder households. The project uses the
Commercial Village (CV) model developed by FCI to support farmers to organize as
enterprises at a village level focusing on enhancing commercialization of onions
(Farm Concern International 2010, 2011; Roothaert and Muhanji 2009).
Research Methods
We chose a single case study design because we were studying a process that required
in-depth investigation to unravel the dynamics of learning in relation to the matching
of demand and supply of innovation support services (Flyvbjerg 2006; Yin 2003). The
6 C.W. Kilelu et al.
case was identified from an exploratory study that mapped various multi-stakeholder
agricultural development projects in Kenya (see Kilelu et al. 2011). The project was
selected for further in-depth research as it had a clear goal for facilitating innovation
processes through matching demand with supply of different types of innovation
support services. It thus fitted our research objective and moreover, because it was
on-going, it allowed us to follow the process in real time.
Data were gathered between August 2011 and February 2012 to coincide with the
main onion production season in the project areas. This enabled us to follow the
interventions of the project and gather data at various points in order to observe and
understand how the process evolved over time. We used various data collection
methods and sources to enable triangulation and enhance the validity of the study
(Yin 2003). Data from farmers were collected from four CV sites to enable us to get a
broader view of this process. Two sites were part of the pilot project (Embaringo and
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Kinyaite CVs) and two were new areas (Kiaragana and Tanyai CVs). Table 1 below
provides a summary of the methods and data collected.
The interviews and focus group discussions were tape-recorded and fully transcribed.
The analytical focus was on the processes by which innovation needs and demands were
articulated and how these were matched to a supply of innovation support services. We
also studied the dynamics of how this process evolved over the production season. To
organize and code our data, we built on Leeuwis and van den Ban (2004), and
distinguished two main ‘learning domains’; the technical and socioinstitutional. We
first categorized the various technical and socio-institutional demands identified at the
outset of the project. Over the production season we examined how farmers’ demands
evolved and the extent to which they were captured through monitoring and feedback
and were then matched to various innovation support services.
Findings
In this section we describe and analyze how the innovation process evolved, how this
translated into a dynamic learning agenda and how it guided the articulation of
demands for support, and how these were matched, or not, with adequate innovation
support services.
Setting the Agenda: Identifying Innovation Needs and Demands
The project’s goal to enhance onion commercialization in Kieni district was guided
by a diagnostic and market opportunity analysis conducted by FCI prior to the pilot
project. According to the project field manager, the current project aimed to scale up
onion commercialization and targeted to reach 10,000 farmers in Kieni east and west
districts. Below is a list of the innovation needs identified at the outset of the project
that relate to challenges in the technical and socio-institutional domains (Farm
Concern International 2010; Roothaert and Muhanji 2009).
1. Technical domain:
(1) Improved production of quality bulb onions;
(2) Improved agronomic practices and use of other production technologies; and
(3) Improved post-harvest handling and storage of onions.
How Dynamics of Learning are Linked to Innovation Support Services 7
Table 1. Summary of methods and data collected.
Data collection Overview of area of focus
methods Sources of information collected
Key informant 2 seed companies Views on challenges faced by onion farmers.
interviews representatives
3 agrochemical companies’ The nature of support they provide to
agents farmers.
3 Kieni District Ministry Their engagement with the project.
of Agriculture officers
2 Microfinance institution
(MFI) officers
4 farmer training meetings The concerns related to onion farming were
and farm visits expressed during the various meetings.
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Types of support that is provided to the
farmers by different actors.
How project captures feedback.
What follow up action was taken on farmer
demands raised these meetings.
2 Farmers-traders forums What issues were raised during interactions
between farmers and traders.
Focus group 4 CVs Types of onion varieties grown.
discussions (FGD) The production challenges faced over the
(about 15 participants in
each CV) season and the support provided through
the project.
The challenges faced in relation to CV
operations and the support provided.
1FGD with onion traders The sources of onions, types of market
(25 participants) segments, challenges faced by onion traders.
Semi-structured 2 model/demonstration Their views on challenges faced by onion
interviews farmers farmers and their role in supporting farmers.
2 farmer-trainers and
3 CV facilitators How did the project facilitate support and
Project field manager monitor this process.
Short questionnaire 43 farmers Estimates of yield (kg), challenges faced
(at end of growing season) during production and views on the areas or
gaps in support from the project.
Review of project Project reports The challenges(demands) identified at the
documents onset of the project.
Monitoring reports Types of activities undertaken in the project.
Project monitoring and feedback processes.
Source: Authors’ data.
2. Social-institutional domain:
(1) Collective action through the commercial village;
(2) Conducting farming as a business;
(3) Improving farmer savings and credit access; and
(4) Streamlining the value chain and distribution system (linking farmers, input
suppliers, extension and traders).
8 C.W. Kilelu et al.
These needs translated into demands for various innovation support services and
informed the project interventions. Below we further describe how the demands
(clustered into the two learning domains) were linked to various innovation support
services and how the learning agenda evolved.
Matching Demand and Supply of Innovation Support Services in an Evolving Learning
Agenda in the Technical Domain
The main technical issues pertained to improving yield and quality of onions grown in
the project area. According to the field manager, farmers used cheap OPVs before the
project interventions and had an average yield of between 0.5 to 1 tonne per
acre (the project used acre as unit for measuring farm size (1 acre0.4 hectare)),
whereas the expected yield from hybrid varieties in optimal local conditions was
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estimated to be about 1014 tonnes per acre. On average farmers in the project sites
grew onions on about 0.4 acres. The project interventions started with organizing
farmer mobilization meetings to promote hybrid onion seed varieties (for example,
Tropicana F1, Red Pinoy F1, Red Passion F1, and Rouge FI) just before the beginning
of the growing season (in August). We participated in two of these meetings where seed
companies and agri-input suppliers were invited to promote their hybrid onion seed
varieties and the related agro-chemicals. During these sessions, the input suppliers also
provided information on improved onion production practices. The data we collected
from individual farmers in the discussion groups indicated that in the older sites*
Embaringo and Kinyaite CVs*about 80% (n31) of the farmers had planted hybrid
varieties and a minority still grew OPVs. In the two newer CVs*Kiaragana and
Tanyai*only 28% (n28) of farmers grew hybrid varieties while about 62% indicated
growing OPVs while another 12% mixed both hybrid and OPVs. Thus in the older sites
there was a higher adoption of hybrid varieties.
During one of the mobilization meetings, some farmers noted that while such forums
were a useful source of knowledge on onion production, they felt that they still did not
have adequate information to enable them make decisions on which varieties to grow.
As one of the farmers explained: ‘We have tried onion farming but were not happy with
the productivity. An experiment should be conducted to understand if the seeds
promoted are suitable in our area’ (Farmer meeting, Endarasha, September 2011).
Thus, the concern about suitability of onion varieties triggered a demand for
different innovation support. In response, the project field manager liaised with
two seed companies to set up demonstration plots of their seeds in collaboration
with selected lead farmers. The seed companies were to provide seeds, the various
agro-chemicals and technical support to the farmers. But as one of the CV facilitators
noted in discussions, only one of the companies followed up on the progress of
their demonstrations. The representatives of the seed company visited the farmers
weekly to monitor and discuss progress and to provide further instructions on how to
proceed, including sometimes changing the types of agro-chemicals. While this
demonstration plot provided an opportunity for collaborative learning, many farmers
from around the area noted that the seed company did not systematically engage
them in a joint learning process. This finding shows that while the articulated demand
was matched to a support service, the service was not optimally utilized and hence
this can be viewed as a mismatch.
How Dynamics of Learning are Linked to Innovation Support Services 9
Farmers were linked to other various support services for improving crop manage-
ment practices to coincide with the peak onion growing season (October to January).
First, the project facilitated farmer-to-farmer visits, where lead farmers (identified
mainly in the older CVs) would share their experiences with the ‘new’ farmers on various
technical issues. During discussions farmers indicated that these visits were important
avenues for acquiring information on improved production practice. Second, the project
organized crop management training forums in various locations. We attended some of
these forums where various agro-chemical company agents were again invited to
disseminate information on standard procedures on applying fertilizer, pesticides and
herbicides at different stages of onion production. While farmers were able to ask
questions during these sessions about specific issues they faced, their feedback after
these sessions indicated the need for more practical training on application of agro-
chemicals but also concerns with the effects of using them. These forums were also meant
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to create direct links between farmers and the agro-chemical suppliers as a way of
stimulating demand for the agro-chemical products but to also ensure farmers accessed
quality products. But as farmers indicated, the investment costs also remained a
constraint to the adequate use of quality inputs as illustrated by a comment of one of the
farmers: ‘We have so many chemicals available so when you use chemical X for thrips, it
doesn’t work although it is cheap and everyone can afford it, but when you tell someone
to buy another chemical Y, that costs 600 shillings (about 6.9 USD [1 USD is equivalent
to 87 Kenya shillings]), while X goes for 150 shillings (about 1.7 USD). But some of these
chemicals are not working’ (Farmers group discussion, Tanyai, December 2011).
Thus the issue of weed and pest management (especially thrips) remained a persistent
challenge. Other feedback also pointed to other issues including the constraints of high
labour costs and poor germination of some seeds. Furthermore, we noted some marked
gender differences in explanations about the challenges; more women than men farmers
attributed their production problems to a lack of proper knowledge, including on
application of agro-chemicals. While we did not pursue this issue in greater depth for
this study, it indicates that efforts to match demands for innovation support with supply
should necessarily integrate a gender analysis, and respond accordingly.
Table 2 provides a summary of the needs and demands in the technical domain and
how these were supported and monitored based on a review of the monitoring process.
We collected estimates of yield data from a small sample of farmers (n43), in three CVs
in February (Embaringo, Tanyai and Kinyaite) and found that the average production
was about 3.4 tonnes per acre, with some variation in the different sites. While a more
detailed study with a larger sample size would be needed to get more conclusive results,
our findings indicate that there was improved production in the project areas, although
the volumes are still below the expected yield of between 10-14 tonnes. Furthermore,
from observations at harvest time, we noted that some of the onions were small in size
and not properly cured indicating problems of quality. Thus, the main technical
challenges were not resolved, pointing to the need for continuous support to farmers.
Matching Demand and Supply of Innovation Support Services in an Evolving Learning
Agenda in the Socio-institutional Domain
Following the diagnosis assessment at the onset, support for innovation in the socio-
institutional domain focused on two broad areas: (1) enhancing collective action
10 C.W. Kilelu et al.
Table 2. Summary of demands in the technical domain identified at the onset of the project
and the matched innovation support services.
Demands in the How the support
technical domain Matched innovation support services was monitored
Production of quality Organize farmer mobilization forums involving Types of varieties and
onions seed and agri-input companies’ representatives quantities grown by
to promote and market hybrid seeds. the farmers in the
project
Production volumes
(yields estimated in
kilos)
Improved agronomic Facilitated training forums that brought The number of
practices including various representatives of agro-chemical farmers that used
proper nursery suppliers to train farmers on various onion agro-chemical inputs
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management and crop production aspects including proper nursery (fertilizer including
management management and crop management (fertilizer organic, pesticides)
application and pest and weed control using The number of
various agro-chemicals and bio-fertilizer); farmers that attended
ministry of agriculture (MOA) extension staff the training
were also involved in some of the training.
Organized new farmer groups to visit lead / The number of
farmer trainers) to learn from their experiences farmers that attended
of onion production; one of the lead farmers the training
participated in a weekly radio programme
where he discussed various topics related to
onion production.
Post-harvest Facilitated construction of a storage unit in The number of stores
management one of the CV by providing part of the built in the CVs
financing.
Organize farmer-trader forums where traders Number of
discuss quality issues that affect onion participants in the
marketing. forums
Dissemination of flyers on pre-harvest Number of flyers
management procedures (curing) to enhance distributed.
quality.
Source: Authors’ data.
of farmers in the value chain; and (2) strengthening entrepreneurial capacity of
individual farmers. Table 3 provides a summary of how the innovation demands in in
this domain were matched to innovation support services.
Enhancing collective action was anchored on FCI’s commercial village (CV) model
that brings together many farmers within an administrative village to engage in
commercialized production of identified crops. The CV model is operationalized first
through the formation of commercial producer groups (CPGs) made up of about
2030 households. The CPGs within a village are then clustered to form the larger
commercial village (Farm Concern International 2011 provides details of the model).
According to the project manager, getting the CVs as new institutions operational was
hinged on establishing elaborate structures, comprising several committees at the CPG
and CV level. All CPG members were expected to be actively involved in at least one of
How Dynamics of Learning are Linked to Innovation Support Services 11
Table 3. Summary of demands in the socio-institutional domain identified at the onset of the
project and the matched innovation support services.
Demands in the socio- How the support was
institutional domain Matched innovation support services monitored
Organizing farmers as Project field manager and CV facilitators Number of CPGs and
collectives using the provided guidance on the establishment and CV established
commercial village structuring of commercial villages (CV).
model
Increasing farmer Project field manager coached the groups Total amount of
savings through group on setting up and management of group savings per CV; the
and personal saving savings schemes. total amount of credit
schemes and enhancing Facilitated linkages between the groups and accessed by farmers
credit access a local MFI to enhance access to credit and (through internal
improve on savings. savings and external
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loans through MFI)
Improving business General training and awareness creation Number of trainings
skills of farmers records and financial management provided organized and number
by partnering organizations i.e. MFI agents of participants
and MOA extension officers.
Streamlining value The project organized exposure visits to Number of market
chain by improving markets for farmers to understand the visited and number of
access to quality and dynamics of onion trade through farmers that
affordable agro-inputs, discussions with traders (e.g. market quality participated.
advisory services and demands, sourcing for onions, pricing etc.).
output markets
Linking the CVs directly to various agro- Total value of
input suppliers (seed, fertilizers, pesticides) collective inputs
through various forums to facilitate purchased
collective and bulk discounted purchasing.
Facilitate farmer- trader forums towards the The number of forums
harvest period to initiate marketing organized and markets
transactions (negotiations on expected visited
volumes and prices) and link farmers
directly to different markets.
Field manager visited different markets in Volumes of onions sold
different cities to scout for potential market and selling price
opportunities.
Source: Authors’ data.
the committees. It is through these structures that farmers would be able to engage in
collective action through aggregating their demands for various innovation support
services such as bulk purchase of inputs, advisory and extension support, financial credit,
and would also enable them to leverage better prices through collective marketing.
To support the establishment of CVs, the field manager periodically consulted with
the CV leaders and provided them with guidance as needed. In addition, a number of
individuals from the different projects sites were trained as community level CV
facilitators and were expected to offer further support in operationalizing the CV as
this was considered a continuous learning process. But from the interviews we
gathered that these CV facilitators provided little support in strengthening the CVs
because in practice, they had to spend most of their time collecting various
12 C.W. Kilelu et al.
monitoring data for the project. Furthermore, from discussions with farmers we
established that the older CVs had set up most committees while the new CVs only
had a few committees set up (production and marketing). However, many farmers
indicated that they were not actively involved in the committees as envisaged. Others
mentioned the issue of conflict within groups and a lack of collaboration between
different CPGs, which affected the operation of the CVs. The field manager
considered such conflict as part of internal dynamics of CVs, which the project
avoided being drawn into. These findings suggest that there are some gaps with the
support needed for strengthening farmer organizations where the demands for such
institutional support are not well articulated.
The demand for streamlining farmers’ participation in the onion value chain was
supported by linking farmers directly to the market (traders) and other innovation
support services that were referred to as business development services (BDS). On
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marketing, the project organized a number of farmer-trader forums in order to
facilitate direct market links so as to by-pass the middlemen who many farmers
considered exploiters. In addition, the field manager visited various markets in
different parts of the country to scout potential untapped market opportunities.
Farmers noted that the direct linkages resulted in substantive increase in prices from
approximately 10Ksh (0.1USD) before the project to about 50Ksh (0.57 USD per
kilogram) during the season when the study was conducted. For the traders, the
sourcing became better coordinated as they could order large volumes through
the CVs. Thus brokering such linkages as an innovation support service enhanced the
farmers’ position in the high value market.
Farmers were also linked to various input suppliers and advisory services, as noted
earlier. In addition, farmers were linked to a local micro-finance institution (MFI),
which developed a credit product specifically for onion farmers (for purchasing of
inputs) that had a flexible payment plan designed to coincide with the four-month
onion growing cycle. Many farmers, particularly in the older CV had obtained credit,
but as some farmers explained, the application sometimes took too long to be
approved which affected timely purchase of inputs; while for others the amount
approved was significantly less than what they had applied for. This shows the need
to recognize differences between farmers, which would then have a bearing on how
support services are organized and how these are made available to make them
suitable for the different types of farmers.
Support related to enhancing individual entrepreneurship aimed to change
farmers’ attitude and practices of farming as a business. According to comments
from the Ministry of Agriculture (MOA) officers and MFI representatives, this need
for entrepreneurial capacity of smallholder farmers seemed to be a latent demand
that needed to be stimulated. To address this demand, the project facilitated forums
where representatives of the MFI and the MOA agri-business officers trained farmers
on basic farm records and financial management, calculating profitability combined
with general discussions on what it means to do farming as a business. However, the
project did not follow up to see if the farmers had incorporated some of these ideas
and skills into their practices. Interestingly, the discussions with farmers showed that
they associated entrepreneurial support more with facilitating access to credit and
markets rather than displaying a demand for specific skills, competences and
attitudes. Thus, we see that in the case of such latent demands related to
How Dynamics of Learning are Linked to Innovation Support Services 13
entrepreneurship there was an apparent mismatch with the support provided. This
highlights the importance of having a better understanding of such latent demands,
and detecting these demands and supporting them requires adequate monitoring and
feedback. In the following section we analyze how the monitoring and feedback
process contributed to a dynamic learning agenda.
The Role of Monitoring and Feedback Processes in a Dynamic Learning Agenda
As indicated in the conceptual framework, monitoring and feedback are important
components for guiding the matching of demand for and supply of innovation
support as part of dynamic learning processes. From the interviews with the field
manager and a review of monitoring reports, we noted that the information gathered
through the formal monitoring system was mainly geared toward reporting on
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project progress. The project monitoring system comprised mainly a series of forms
that were used to periodically collect data for tracking project progress. As shown in
Tables 2 and 3, this formal monitoring system was used to capture pre-defined
outcomes of the project (for example, using indicators such as number of farmers that
were growing hybrid varieties, yields attained, amount of inputs purchased
collectively etc.). These indicators were linked to the demands identified at the onset
of the project through the diagnostic study. However, the data was not systematically
analyzed and reflected upon, particularly not in relation to whether the innovation
support provided adequately met farmers’ demands. Thus, the formal monitoring
system did not adequately guide learning and the re-orienting of innovation support
based on (re)emerging demands. In addition, we observed some informal feedback
processes within the project, as shown in Table 4. Farmers mainly expressed this
feedback during various meetings. For example, the demonstration plots were set up
in response to farmers’ demand for further guidance on seed variety selection. Such
informal feedback provided avenues for demand (re)articulation. While in some
instances the feedback was used to re-orient activities to match the demands, most of
the emerging demands were not addressed (see Table 4). For example, during a
meeting farmers indicated some concerns with the effect of intensive use of agro-
chemicals on soils and indicated that they wanted research to look into this matter
but there was no follow-up on this issue. Thus, the emerging needs from such
informal feedback and the responses to the demands for support were somewhat
arbitrary. These findings indicate a gap with the intermediary role of the project in
terms of being a broker between demand and supply of services and the extent to
which it organized to support a dynamic learning agenda.
Discussion
Matching Demand and Supply of Innovation Support Services is Part of a Continuous
Learning and Negotiated Process
Our results show that supporting learning in agricultural innovation processes is tied
to linking the needs of actors, particularly farmers, to various resources and services
that contribute to dynamic innovation processes. Importantly, the study showed that
in the context of demand-driven pluralistic innovation support, the requisite for
learning that underlies innovation processes trigger the mobilization of a network of
14 C.W. Kilelu et al.
Table 4. Summary of the emerging demands in the two domains and how these were matched
to innovation support services.
Emerging needs/demands from
farmers feedback Matched innovation support services
Technical More guidance in selecting suitable Project liaised with some seed
domain seeds for specific agro-ecological companies in collaboration with
areas. selected lead farmers to establish
demonstration plots to test several
varieties.
Poor seed germination of some of the X
varieties; general challenge of
drought
Poor efficacy of some of the agro- Farmers linked directly to selected
chemicals (pesticides and herbicides) agro-chemical suppliers, but many
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purchased farmers bought from local agri-input
dealers
The need for more on farm Facilitated more farmer to farmer
experiments on the constraints visits to some of the lead farmers.
related to pests and weeds
management
Request for on-farm research to X
understand the effects of intensive
agro-chemical application in onion
production on the soils.
Concerns with effects of applying Awareness raising by agro-chemicals
agro-chemicals on human health company representatives during
training sessions on the use of
protective gear.
Increasing labour costs X
Socio- Some organizational limitations of Some support from CV facilitators.
institutional the CVs including low involvement
domain of members in committees in some
CVs and CPGs
Limited cooperation and conflict X
within some CVs
Inconsistency with farmers keeping X
records related to their onion
enterprise (e.g. inputs, labour costs,
farm management tasks such as
fertilizer application etc.)
High cost and shortage of some seeds The project signed partnerships with
in the market one seed companies to make seeds
readily available and at a discount in
subsequent seasons.
Some farmers had difficulties with X
accessing timely credit through the
MFI due to procedural issues
Note: *x- Indicates no action was undertaken to address the emerging demand.
Source: Authors’ data.
different innovation support service providers who bring in different complementary
knowledge, skills and resources necessary for innovation. This confirms recent
findings of Chowa et al. (2013) that pluralistic advisory support systems are better
How Dynamics of Learning are Linked to Innovation Support Services 15
tailored to support learning, and using the words of Birner et al. (2009) they hence do
provide a menu of options. Our findings also support other studies which have shown
that brokering roles (in this case fulfilled by FCI) are important in facilitating
linkages among various actors, as they try to optimize a demand and supply match
for innovation support services (Crawford et al. 2007; Klerkx & Leeuwis 2008).
What our study adds to earlier work on demand-driven innovation support
services (Birner et al. 2009; Christoplos 2010; Klerkx et al. 2006; Parkinson 2009) is
to show that there are continually emerging demands in innovation processes,
triggered by new problems, uncertainties and challenges or new opportunities.
Because of the many interacting socio-technical factors that determine the outcome
of agricultural innovation processes (cf. Hall and Clark 2010), these emerging
problems, uncertainties, challenges and opportunities are not fully predictable.
Therefore supporting learning requires a fine-grained understanding of the various
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demands for services that emerge in the process and which require to be matched to a
combined supply of innovation support services (Crawford et al. 2007; Klerkx and
Proctor 2013). It also requires an adequate monitoring system for capturing these
demands, as shown in the conceptual framework (Figure 1). This is where the
challenge lies with regard to supporting a dynamic learning agenda. While our results
show that the FCI project mobilized different innovation support services, the
process was not always effective in addressing emerging issues and adapting the
agenda accordingly (a demand for research to understand the effect of intensive input
use in onion production on soils was not incorporated into the agenda as no research
partners were mobilized as collaborators in the project). This ties to arguments
against generic knowledge transfer models in innovation support interventions,
which are not geared towards addressing everyday farmers’ concerns and practices
which are diverse and evolve over time (Hall and Clark 2010; Parkinson 2009).
Furthermore, our study indicates that matching demand and supply of innovation
support services in pluralistic and privatized systems is a complex process, given that
there are competing interests. While input suppliers played an important role in
training farmers, but in line with other findings, these service providers typically gear
their advice to support sales of their products (Glover 2007; Poulton et al. 2010), but
did not fully engage in learning processes in which also the potential negative
consequences of their products are discussed. There is also an interplay of power
relations in such support systems, which has been noted to disadvantage smallholder
farmers (Parkinson 2009; Poulton et al. 2010). Therefore intermediaries sometimes
need to take an advocacy role to empower certain groups such as farmers. Taking
such an advocacy role however requires careful balancing (cf. Klerkx et al. 2009), in
order to remain legitimate to be able to engage all relevant actors including input
suppliers in the evolving learning process.
Monitoring and Feedback Processes and the Learning Agenda
As the findings indicate, the project continually gathered data in order to monitor
progress of the interventions in relation to the pre-defined project goal, such as
tracking the adoption of hybrid seed varieties by farmers and the linked yield
outcomes. However, the inadequate match with appropriate support for most of the
emerging demands shows the limitations of this monitoring approach. Considering
16 C.W. Kilelu et al.
that the monitoring system had a focus on tracking pre-set goals, it was not able to
adequately capture useful feedback on emerging demands of farmers as the process
unfolded, and hence it reproduced a linear view of innovation processes. Our findings
thus confirm that an indicator driven monitoring system is limited in its ability to
systematically capture feedback and enable evolving demand (re)articulation, and
hence improve the efficacy of action by linking to appropriate innovation support
services. This builds on the argument that a dynamic learning agenda should be
linked to reflexive learning-oriented monitoring systems (Regeer 2009; Ringsing and
Leeuwis 2008; van Mierlo et al. 2010; Woodhill 2007).
Related to the issue of emerging demands not being adequately tracked, is the
issue that feedback on some demands was easier to pick-up and match to particular
innovation support service than other feedback. For example, linking farmers to agri-
input providers was easily achieved compared to translating the demand for problem-
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oriented participatory research related to pest management and pesticide application
into a concrete on-farm experiment. This confirms what other scholars have found
(Labarthe 2009; Parkinson 2009; Van Mele 2008), that some demands are not general
and require sustained support over time, which poses challenges in operationalizing
demand driven innovation processes, due to the investment required of time and
money.
Furthermore, the results also show that demands emerging from feedback in the
socio-institutional domain (building entrepreneurship capacity) were more latent
than the technical demands (access to hybrid seeds) and thus were largely not
addressed (see Table 4). The limitation of supporting farmers to incorporate generic
business skills and entrepreneurial attitudes points to a mismatch as regards the
appropriateness of the support provided to agricultural enterprises. As some scholars
have noted (Klerkx and Leeuwis 2009b; Phillipson et al. 2004), part of the difficulty
in providing support related to enhancing business skills in agriculture has been a
lack of familiarity of non-agricultural innovation support service providers with
farmers (and vice versa), but also a limited understanding by ‘traditional’ agricultural
innovation support providers of entrepreneurial learning processes that are more
tacit and contextual (Cope 2005). While most of the studies on support of
entrepreneurship of farmers have been undertaken in the context of developed
countries, our findings indicate this is also a concern in developing countries. Studies
emerging from other developing and emerging countries indicate that dedicated
entrepreneurship support programs are highly relevant to stimulate smallholders to
become more entrepreneurial and market-oriented (Berdegue´ 2001; Kaganzi et al.
2009; Namdar-Irani and Sotomayor 2011).
Given the above problems related to demand articulation, our article re-
emphasizes the message from earlier work (Klerkx and Leeuwis 2009b; Parkinson
2009) that adequate effort should be put in optimizing the quality of demand
articulation processes, including capturing the latent needs. When not putting
sufficient attention to the quality of demand articulation, interventions may miss
out on the broad range of farmers’ needs and demands. This means that monitoring
the process through continuous capture of information from both formal and
informal feedback process is needed (Ringsing and Leeuwis 2008). This is a key task
of the intermediary actors involved in these interventions as brokers, which in this
case was the role of the project staff. In order to enhance a dynamic learning agenda,
How Dynamics of Learning are Linked to Innovation Support Services 17
the emphasis of such intermediaries should not be on controlling the process and
monitoring predefined outcomes. Such a focus reduces the learning potential, as it
tends to overlook feedback. Rather, emphasis should be on steering the process to
enable optimal interactions between the demand and supply sides of the innovation
processes, guided by a learning agenda. This indicates that the three principal
functions of such intermediaries (demand articulation, network formation, and
innovation process monitoring; see Klerkx and Leeuwis 2009b) should be performed
in tandem. As has become clear from the previous section, while executing these
functions, power dynamics between actors at the demand side (farmers) and the
supply side (input suppliers) need to receive sufficient attention.
Conclusion
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By applying the concept of a dynamic learning agenda we bring in a new perspective
to understanding how to enhance demand-driven innovation support service delivery.
Our findings have shown that there is a need for a more nuanced understanding of
the concept of ‘best fit’ in increasingly pluralistic agricultural innovation support
service systems (Birner et al. 2009). As the findings show, it is crucial that farmers are
assisted to navigate these systems to enable better targeted and context-specific
support, especially in a context in which there are contrasting private and public
interests, and power differences between farmers and innovation support service
providers. As our analysis reveals, in fact several ‘best-fits’ should emerge through a
continuous process of articulating of demands that are then linked to an adequate
network of service providers with attention to the appropriateness of service
modalities. Sufficient attention needs to be paid to evolving demands, and the
quality of demand articulation needs to be high to be able to inform the choice for
appropriate type of innovation support. Also, there may be a need to build capacity
to be able to provide certain types of innovation support services when these are not
available (for example, entrepreneurship support). Hence, following Regeer (2009),
intermediaries that act as brokers between demand for and supply of innovation
support services within such innovation processes should put more attention to
‘making the invisible visible’. This means incorporating learning oriented monitoring
systems that integrate a learning agenda that enables optimally matching demand
and supply of innovation support services.
From the foregoing, two policy implications can be derived: (1) more attention
needs to be given to building adequate brokering capacities and embed the brokering
role more centrally in agricultural development projects (see also Klerkx et al. 2009);
and (2) as demand for and supply of innovation support cannot be fully determined
ex-ante, policy-makers and funders of agricultural development projects should
incorporate a degree of flexibility in project funding, design and implementation
supported by learning oriented monitoring, to stay in tune with the dynamics of
demand-driven innovation processes that also considers the heterogeneity of farmers.
In terms of future research, looking at the development of dynamic learning
agendas over a longer timeframe is needed, as our study was only able to capture
some of the dynamism. Following Klerkx and Proctor’s (2013) recent findings on
how ‘alliances of advisors’ form to provide an integrated palette of innovation
support services, more research on how technical and socioinstitutional advice
18 C.W. Kilelu et al.
(entrepreneurship support) can be optimally combined is needed. This is especially
relevant in the context of complex systems of public and private pluralistic
innovation support services which have emerged in many developing countries.
Acknowledgements
We would like to acknowledge FCI for their support of this research and specially thank Mr Gerard
Watoro for his invaluable assistance in the field. We extend our gratitude to the many farmers and other
collaborating actors for their time and co-operation during the research. The constructive comments of
anonymous reviewers were very helpful in improving the article. We gratefully acknowledge the financial
support of Wageningen Graduate School of Social Sciences, which enabled this study. The usual
disclaimers apply.
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