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studies. The emergence of systems approaches in agricultural innovation stem from a recognition that problems in agriculture are increasingly

understood to be “... complex, uncertain and operate at multiple levels, from the field to the global supply chain, and involve social, economic,

institutional and technological change” (Turner, Klerkx, White, Payne, & Everett-Hincks, 2015b, p. 1). As Hall (A. Hall, 2007, p. 5) notes:

“In 1998 a small group of researchers in India started to experiment with the innovation system concept as an analytical aid to help understand why otherwise promising research and development projects failed to bring about

desired social and economic changes.”

Today, a family of related (but different) approaches, including AKIS, FSR and AIS, continue to make use of ‘systems thinking’. Such a view represents

“current” thinking on innovation in agricultural systems, indicating the profound challenges to, and replacement of, the research-extension-farmer

model of agricultural change that, like the agrarian sciences associated with it, can be considered positivist and reductionist in outlook6 _(Koutsouris, 2012).

Klerkx et al. (2012b) chart the development of these theories in agriculture, noting that although systems enquiry in an agricultural context has matured in relative isolation from other systems traditions, there has been a degree of cross-fertilisation. Several broad stages of thinking can be discerned:

1. Adoption and diffusion theories: these are characterised by the Technology Transfer (TT) paradigm initiated by Rogers (Rogers, 1983) and include a strong emphasis on social systems, but treat institutions and policy as external factors influencing rates of adoption.

2. Farm Systems Research (FSR): this perspective emerged in response to perceived inadequacies of the more linear approaches to agricultural innovation. FSR aims to take a holistic account of agricultural change by “... capturing the interactions between farms

and their natural, social and economic context” (Darnhofer et al., 2012, p. 3).

3. Agricultural Knowledge and Information Systems (AKIS): this tradition also grew out of dissatisfaction with the linear model of

6_{That being said, Klerkx et al. (2012b) position the Diffusion tradition within the} systems lineage given its focus on social networks and mass media (social systems).

Technology Transfer but traces its lineage to extension science. It focuses on the organisations interactions between different actors as they produce, adapt, transmit and store knowledge. In its earliest incarnations (see Stenvang et al., 2013) AKIS adopted a “hard systems” view; the system in question has a ‘solid’ boundary and common purpose, which exists independent of the observer and, by

extension, can be “engineered” towards an unambiguous goal. However, AKIS has come to be epitomised by “softer systems”

thinking in which it is accepted that the boundaries of a given system will be understood differently by different people within it (an issue returned to in 3.1.2). As framework for understanding, AKIS has been particularly influential; the European Commission, for example, has framed recent policy debates around a conceptualisation of the European agricultural innovation system as an AKIS (EU SCAR, 2012).

4. Agricultural Innovation Systems (AIS): growing in tandem but separate from AKIS, is AIS, which was instead influenced by National Systems of Innovation thinking. It differs from AKIS in its scope, broadening the horizon to include all relevant organisations in agricultural innovation (and not only research and extension systems) (Hermans et al., 2015, p. 38). Innovation in this context is given to be the result of multiple interactions between farming systems, supply chains, policy environments, and economic and social systems (Klerkx et al., 2012b). As Klerkx et al. (2012b) explain:

“... the main achievement of the AIS approach thus appears to be that

it has further broadened the scope of analysts and interventionists on the complex interactions between a multitude of players and sub-

systems that characterize innovation”. Not only is this the most

“recent” in the family of systems-orientated approaches to agricultural innovation (Klerkx et al., 2012b), it also represents the most pertinent approach to tackling the kinds of complex problems that are the concern of this project (Schut, Klerkx, Rodenburg, Kayeke, Hinnou, Raboanarielina, et al., 2014a, p. 1).

Given its focus on issues similar to those framing this thesis, it is worth exploring the AIS approach in more detail; diagnostic AIS approaches have been used to identify the barriers to innovation in various settings (see Polzin, Flotow, & Klerkx, 2016; Turner, Klerkx, Rijswijk, Williams, & Barnard, 2015a) and even in parts of the UK (Lamprinopoulou et al., 2012). Taking an AIS perspective, Klerkx et al. (2012b) describe different views

regarding what can “enable” and “disable” innovation, though it should be acknowledged that the idea of optimising innovation systems hinges upon

one’s precise conceptualisation of the agricultural innovation system and its components. An infrastructuralist view of the AIS sees its purpose as being to support agricultural innovation, and its success is thus determined by the extent to which it enhances or hinders agricultural innovation in a rather static manner; a process view of AIS sees the innovation system as a self- organising system of networks, that come and go in the pursuit of novelty. An inherently dynamic process with clear influences from the Multi-level Perspective, the configuration of the value chain in such systems is seen as moving ever towards an alternative to the ‘incumbent regime’, perhaps even

threatening it entirely (A. Hall & Clark, 2009). As such, this view of the innovation system borders on the idea of system innovation – the

transformation of a system to a new, innovative configuration (see Elzen et al., 2004; Geels, 2005). In turn, a well-functioning AIS is, according to this perspective, determined by the extent to which niches can be supported and system transition encouraged (see Section 2.3.5.3); lastly, the functionalist

view of innovation like that described by Hekkert (2007) sees the innovation system as providing several important functions. Klerkx et al. (2012b) note the similarities between the evolutionary perspective on evolution here: a biological system cannot function without each of its components – organs – fulfilling some function or other.

By mapping such functions and the interactions between them, one can

pinpoint areas of ‘systemic failure’ to inform policy (see Turner, Klerkx, Rijswijk, Williams, & Barnard, 2015a for example). As such, it is possible to

assess the functioning of this system against its supposed ‘functions’ in a

systematic way (see Hekkert et al., 2007; Kebebe et al., 2015; Suurs, Hekkert, Kieboom, & Smits, 2009; Turner, Klerkx, Rijswijk, Williams, & Barnard, 2015a). The seven functions of innovation systems are as follows:

1. Entrepreneurial activities use the potential of new knowledge, networks and markets to create value (see Klerkx & Leeuwis, 2008b).

Such activities can also include lobbying and attempts to ‘restructure’

institutional environments.

2. Knowledge development is considered a fundamental prerequisite to innovation (Kebebe et al., 2015, p. 70) and the ability to create new knowledge a vital component of effective AIS. Creation of new knowledge is not restricted to formal research establishment; farmers and agro-businesses can also be a source of new knowledge.

3. Knowledge diffusion through networks is particularly important to

further develop and adapt knowledge and innovations, and to “scale them up” (by garnering support in terms of favourable policy and

market conditions) and “out” (by increasing the number of users). In

other words, to enhance the “co-evolution of social, technological,

institutional and market changes” (Turner, Klerkx, Rijswijk, Williams, & Barnard, 2015a, p. 3). An emphasis on the importance of

innovation ‘platforms’ and networks where this interactive learning

can occur is common in AIS literature (Kilelu, Klerkx, & Leeuwis, 2013; Klerkx et al., 2013; Schut, Klerkx, & Sartas, 2015).

4. Guidance of the search concerns the creation of a “vision” for the

innovation system with which to orientate other system functions (such as entrepreneurial activity and knowledge development). Turner et al. (2015a) stress that ‘innovation agendas’ can be used to

this effect. Mylan et al. (Mylan, Geels, Gee, & McMeekin, 2014, p. 22) also note that shared meanings, expectations and a clear vision for the future can stimulate innovation by reducing uncertainty and providing a sense of direction to innovation processes.

5. Market formation concerns the development of markets for novel products or existing products made in new ways, which can prove

challenging due to resistance from the consumer and/or ‘incumbent players’.

6. Resource mobilisation involves the management of the human and financial resources to undertake activities within the AIS; this includes funding for research and subsidies for certain technologies

for example, as well as to attract appropriate expertise in “innovation trajectories”.

7. Creation of legitimacy is necessary to counteract resistance to change inherent in existing systems of production, trade and consumption.

Structures are the actors, institutions, interactions and infrastructure that determines the operation of these seven functions:

1. Actors are individuals, firms and other organisations, that tend to be delineated by their economic activities (i.e. NGO, business, government).

2. Institutions range from ‘soft’ (habits and routines) to ‘hard’ (rules,

norms and strategies) factors shaping the activity of actors. 3. Interactions are the dynamic relations between different actors. 4. Infrastructure refers to either 1) physical infrastructure (such as

roads or existing technology, 2) knowledge infrastructure, such as research and extension facilities or 3) financial infrastructure such as grants, subsidies and financial programmes.

A plethora of ‘types’ of system failure exist between these functions and structures (see Klein Woolthuis, Lankhuizen, & Gilsing, 2005). Weber & Rohracher (2012) provide an outline of these failures, which fall into the broader categories of market, structural and transformational failure. However, as Woolthius et al. (2005, p. 614) suggest:

“Most problems in the innovation system will not be uni-dimensional but will consist of a complex mixture of causes and effects, and involve several

Although there are differences between these conceptualisations of the

AIS, several common “enabling factors” and disabling factors” or “barriers”

to AIS performance can be discerned (see Table 1).

Table 1 Enabling and disabling factors for AIS performance (adapted from Klerkx et al., 2012b)

Enabling factors Disabling factors

• Learning within and between firms and organisations in order to innovate

• Strengthening individual and collective capabilities to innovate • Demand and supply-driven science

and technology

• Innovation agents focusing on complex and dynamic interactions • Network-based knowledge

dissemination

• Both embedded and dis-embedded knowledge dissemination: in both tacit and codified forms

• Decentralized management of innovation processes

• Infrastructural:

o Lack of investment in both physical structures (roads, telecoms) but also research and development facilities and financial infrastructure • Institutional:

o Ranging from “hard”,

formalised laws or regulations or lack of IP

protection, to “soft” norms,

values and culture that hamper innovation • Capabilities:

o Lack of technical or organisational capacity to adapt to or manage new innovation

• Market:

o Relations between market participants, lack of trust/transparency or monopoly

Because a systems perspective places emphasis on interaction between the

component parts of a given system, “... creating and fostering effective

linkages amongst heterogeneous sets of actors” is the primary goal of AIS enquiry; however, this can also be hampered by social, economic, technical and cultural divides (Klerkx et al., 2012b, p. 468; see also Oreszczyn, Lane, & Carr, 2010). These can happen due to the use of different incentive structures for private and public actors, or between formal (scientific) and informal (indigenous) knowledge (see Section 4.7.1.2). Going further, Hall (2007) outlines the types of organisational attitudes and practices that

“restrict” or “support” innovation (some of which emerge in this research) (see Table 2).

Table 2 List of restrictive and supportive attitudes and practices for agricultural innovation (adapted from A. Hall, 2007)

Restrictive attitudes and practices Supportive attitudes and practices • Mistrust of other organizations

• Closed to other’s ideas • Secretiveness

• Lack of confidence

• Professional hierarchies between organizations and disciples • Internal hierarchies • Top-down cultures and

approaches

• Failures are covered up • Limited scope and intensity of

interaction in sector networks

• Trust • Openness • Transparency • Confidence • Mutual respect

• Flat management structure • Reflection and learning from

successes and failures • Proactive networking

Systems-orientated approaches to agricultural innovation are now established in European (EU SCAR, 2012), African (Kebebe et al., 2015; Kilelu et al., 2013; Sumberg, 2005) and Australasian research (Turner, Klerkx, Rijswijk, Williams, & Barnard, 2015a), as well as being promoted by the World Bank (World Bank, 2012). A key strength of these approaches, at least from the perspective of this project, is the comprehensiveness with which they treat the possible sources of innovation – the final topic of this literature review.