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Structural Conditions for Collaborationand Learning in Innovation Networks:Using an Innovation SystemPerformance Lens to AnalyseAgricultural Knowledge SystemsFrans Hermansab, Laurens Klerkxb & Dirk Roepc

a Structural Development of Farms and Rural Areas, LeibnizInstitute of Agricultural Development in Transition Economies,Theodor-Lieser Strasse 2, 06120 Halle (Saale), Germanyb Knowledge, Technology and Innovation Group, WageningenUniversity, PO Box 8130, EW 6700, Wageningen, The Netherlandsc Rural Sociology Group, Wageningen University, PO Box 8130, EW6700, Wageningen, The NetherlandsPublished online: 08 Jan 2015.

To cite this article: Frans Hermans, Laurens Klerkx & Dirk Roep (2015) Structural Conditions forCollaboration and Learning in Innovation Networks: Using an Innovation System Performance Lensto Analyse Agricultural Knowledge Systems, The Journal of Agricultural Education and Extension,21:1, 35-54, DOI: 10.1080/1389224X.2014.991113

To link to this article: http://dx.doi.org/10.1080/1389224X.2014.991113

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Structural Conditions for Collaboration andLearning in Innovation Networks: Using anInnovation System Performance Lens toAnalyse Agricultural Knowledge Systems

FRANS HERMANS*,†, LAURENS KLERKX† and DIRK ROEP‡

*Structural Development of Farms and Rural Areas, Leibniz Institute of Agricultural Development in TransitionEconomies, Theodor-Lieser Strasse 2, 06120 Halle (Saale), Germany; †Knowledge, Technology and InnovationGroup, Wageningen University, PO Box 8130, EW 6700, Wageningen, The Netherlands; ‡Rural SociologyGroup, Wageningen University, PO Box 8130, EW 6700, Wageningen, The Netherlands

ABSTRACT Purpose: We investigate how the structural conditions of eight differentEuropean agricultural innovation systems can facilitate or hinder collaboration andsocial learning in multidisciplinary innovation networks.Methodology: We have adapted the Innovation System Failure Matrix to investigate themain barriers and enablers eight countries (England, France, Germany, Hungary, Italy,Latvia, The Netherlands and Switzerland).Findings: Results show some of the recent trends the AKS actors in these countries haveexperienced and how these have affected their potential to act as collaborators inmultidisciplinary innovation networks. Lack of funds, combined with horizontal andvertical fragmentation and the lack of proper evaluation criteria for collaborativeinnovation networks are among the most important threats we found.Practical Implications: This study shows that each national AIS has some unique features.This means that the implementation of policies promoting collaboration and sociallearning (e.g. the European Innovation Partnerships and Operational Groups) shoulddepend on a critical reflection of the existing structural elements of the AIS in eachcountry and whether there is a need for inclusion of new actors, or whether certaininnovations for collective goods should be promoted.Originality: The paper contributes to the ongoing discussion in the scientific literature onthe advantages and disadvantages of privatization of extension and advisory services andthe shift from thinking in terms of the traditional Agricultural Knowledge System towardsa broader Agricultural Innovation System.

KEY WORDS: Agricultural innovation system, Agricultural knowledge system, Networks, Collab-oration, Social learning, LINSA

Correspondence address: Frans Hermans, Structural Development of Farms and Rural Areas, Leibniz Institute ofAgricultural Development in Transition Economies, Theodor-Lieser Strasse 2, 06120 Halle (Saale), Germany.Tel: +49 345 2928 134. Email: Hermans@iamo.de

The Journal of Agricultural Education and ExtensionVol. 21, No. 1, 35–54, February 2015

1389-224X Print/1750-8622 Online/15/010035-20 © 2014 Wageningen Universityhttp://dx.doi.org/10.1080/1389224X.2014.991113

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Introduction

It has become increasingly recognized that many of the complex problems theagricultural sector is currently facing, cannot be solved by a single actor alone. Insteadthey require the involvement of different kinds of stakeholders in innovation processesaimed at a transition towards a more sustainable agricultural sector (Knickel et al. 2009;Hermans et al. 2013; Poppe, Termeer, and Slingerland 2009). As a result a networkapproach is being promoted in which multidisciplinary and intersectoral innovationgroups are organized in which processes of knowledge co-creation and social learningtake place (Regeer 2009; Leeuwis and Pyburn 2002; Beers and Geerling-Eiff 2013).These groups go under different names in the literature, but in this paper we will focusspecifically on a learning innovation network for sustainable agriculture (LINSA). Aninnovation network can be classified as a LINSA when: a) it is explicitly aimed atsustainable agriculture, b) there is an open exchange of information between partners thatfacilitates social learning, and c) there is a collaboration between a wide range of types oforganizations (public, private, NGOs, etc.) (Brunori et al. 2013; Moschitz et al. 2014)

In this paper we will focus on the latter two elements of the LINSA: social learning andcollaboration within a heterogeneous group of partners. Creating and fostering effectivelearning and innovation processes among actors from different backgrounds is oftendifficult because of the technological, social, economic and cultural differences betweenthem (Pant and Hambly-Odame 2006; Klerkx et al. 2012). The origination of suchdivides, or conversely, conditions that optimize collaboration and social learning aredetermined by structural settings (e.g., the types of organizations involved and theirlinkages) and the institutional settings (e.g. the incentives for collaboration, intellectualproperty rights, the organization of research agenda setting mechanisms) of the nationalAgricultural Innovation System (AIS) in which a LINSA operates (Wieczorek andHekkert 2012; Klerkx, Van Mierlo, and Leeuwis 2012).

The question this paper poses is therefore how the characteristics of differentagricultural innovation systems can hinder or stimulate processes of social learning andcollaboration for innovation towards sustainable agriculture. We will answer this questionby comparing the arrangements of some of the most important players of the AIS: theresearch, education and advisory sectors in eight European countries: England, France,Germany, Hungary, Italy, Latvia, The Netherlands and Switzerland.

This question holds theoretical as well as a practical value for researchers, practitionersand policy makers. From a theoretical perspective this paper contributes to filling theexisting gap between the study of innovation systems at the macro level (e.g., innovationpolicies and support instruments, research infrastructure) and how this affects the microlevel of innovation networks (Klerkx, Aarts, and Leeuwis 2010). While research has beendone at the country level which map the AIS and look at its innovative capacity (Temel2004; Sorensen 2011; Gildemacher et al. 2009; Davis, Ekboir, and Spielman 2008; Hellin2012), there appear to be few studies from a comparative perspective. The fewcomparative studies have not taken a broad AIS perspective, but have looked at aspecific sub-sector of the innovation system: for instance how reforms in advisory andextension services have affected farm level performance (Garforth et al. 2003; Laurent,Cerf, and Labarthe 2006).

Networking, knowledge co-creation and collaboration between different partners arebecoming increasingly popular across the various European countries as means to

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stimulate innovation. At the European level, the European Innovation Partnership (EIP)has a become a focal point of EU innovation policy under the ‘Europe 2020’ strategy thatfocusses on partnerships and bottom up initiatives in ‘Operational Groups’ that will beconnected to it (EU-SCAR 2012). From a practical point of view, this paper contributes tocurrent discussions on how to best organize these types of networks and how currentknowledge infrastructures can be tailored towards better supporting them, making thechance of achieving a successful transition towards sustainable agriculture more likely.

Theoretical Framework: Structural Conditions for Learning and Collaboration

Innovation requires the generation of new ideas and there is a widespread consensus onthe importance of learning and collaboration as sources of new knowledge and practices.New ideas are not necessarily the work of one brilliant individual. Instead, many newideas come from applying existing ideas in a new social context, or by the recombinationof existing ideas (Burt 2005; Powell and Grodal 2005). Actors seek out other actors tocollaborate with, based on their compatibility in terms of resources, knowledge, power ornetwork position (Van der Valk 2007; Ahuja 2000). At the same time, however, creativityand innovation are stimulated by cooperation and active exchange of ideas and this formsthe basis for the concept of ‘social learning’. By bringing people together and providingthem with an opportunity to share their ideas and discuss them with other people, theyalign their personal mental models into a shared group model and as they learn from eachother and form new relationships they develop the capacity to take collective action (Reedet al. 2010; Wals 2007; Beers et al. 2014; Moschitz and Home 2014). Collaboration andlearning are therefore closely entwined in innovation processes.

Drawing on Leeuwis and Pyburn (2002), Van Mierlo et al. (2010) show that there are anumber of conditions and circumstances that influence the probability of achieving aneffective and productive learning process. Some of these conditions have to do withindividual and psychological aspects. However, in this paper we are interested in thestructural conditions: that is how the institutionalized norms and the shared cognitiveframeworks that make up a society can enable or hinder learning and collaboration. Thisis in line with the framework presented by Birner et al. (2009) and the results of Hermanset al. (2013) that show that the affiliation of an actor is more important than his or herindividual capabilities for the roles they perform in an innovation network.

To classify such structural conditions for learning and collaboration we will make useof the innovation system failure framework developed by Klein Woolthuis, Lankhuizen,and Gilsing (2005). This framework was derived from the thinking about the NationalSystems of Innovation developed (among others) by Lundvall (1992) and Edquist (1997).This innovation system perspective provides an analytical framework to study techno-logical change as a complex process of actions and interactions among a diverse set ofactors engaged in generating, exchanging, and using knowledge (Spielman et al. 2008;Markard and Truffer 2008).

The thinking about agricultural innovation systems thus has a somewhat differentbackground than the thinking about Agricultural Knowledge Systems (AKS) andAgricultural Knowledge and Information Systems (AKIS). The AKS included thepublicly funded research, education and extension services involved in the productionand transfer of formal knowledge. The AKIS concept came up in the 1990s and was anextension of the older AKS concept. The AKIS concept emphasized the process of how

Structural Conditions for Collaboration and Learning in Innovation Networks 37

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information and knowledge are created, transformed, stored, integrated, transmitted and/or utilized and also included actors outside the research, education and advice institutes(Rivera, Qamar, and Mwandemere 2005; Röling 1990). In general, the AIS framework isbroader than the AKIS framework because it further broadens the scope of theinvestigations beyond the actors directly involved in the agricultural production chain(mainly focusing on farmers) and the agricultural research, extension and educationsystem (the formal Agricultural Knowledge System), to a multitude of actors that canplay at role within innovation processes. Secondly it pays particular attention to theinfluence of institutions (defined here as ‘the rules of the game’) and infrastructures. Inrecent years, the AKIS concept has been redefined as Agricultural Knowledge andInnovation System (our emphasis), and the two concepts have thus started to resembleeach other even more (Klerkx, Van Mierlo, and Leeuwis 2012; EU-SCAR 2012).

System imperfections, or ‘failures’, can form a blockade for innovation. Using aninnovation system failure matrix these failures can be identified, thus helping actors toovercome these problems. In this paper we have adapted this approach somewhat by notfocussing solely on the weaknesses of existing innovation systems, but also to focus ontheir particular strong points. For that reason we have renamed the matrix the InnovationSystem Performance (ISP) matrix. The ISP matrix shown in Table 1 makes a distinctionbetween ‘the players’ and ‘the rules’ of the innovation game. The columns of this matrixcontain some of the most important actors that interact in an agricultural innovationsystem. It is important to note that the columns in Table 1 function as an example of thepossible relevant actors within an AIS. For practical purposes we have limited ourcomparison to the analysis of the traditional actors that made up the formal AKS: the(mostly) public funded research, education and advisory institutes (depicted in the table ina grey scale).

The rows of the ISP matrix contain the different categories that may hinder or facilitatethe performance of the innovation system (Klein Woolthuis, Lankhuizen, and Gilsing2005; Klerkx, Van Mierlo, and Leeuwis 2012):

. (Knowledge) infrastructure concerns the physical infrastructure, such as roads,railroads and telecommunication. Infrastructure typically requires major investmentsthat cannot be made by the actors of the system independently. With regard to theAIS, the infrastructure also concerns investments in knowledge infrastructure (R&Dfacilities) the financial infrastructure and funding of public and private research.

. Laws and regulations are the formalized rules of the system. A lack of them mayhamper innovation. For example, lack of intellectual property regulation takes awayincentives from innovators as they cannot protect their innovation. However toomuch regulation and red tape can also be detrimental for the innovativeperformance.

. The unwritten rules are formed by the ‘norms, values and culture’, and they refer to‘the way business is done’ between the actors and what constitutes good businessand farming practices. They affect how actors interact with each other but also relateto their (in)ability to change their behaviour and operations to enable innovation totake place

. Interactions and network characteristics refer to the way actors are connected toeach other. Loosely coupled, ‘weak’ networks can suffer from fragmentation,resulting in missed opportunities for collaboration and a limited recombination of

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Table 1. Innovation system performance matrix (example)

ResearchInstitutes

andUniversities

Agriculturaleducation

Extension(public)

Advisoryservices/

consultancy(private)

Governmentagencies

Agriculturalchambers

Farmers’ unions,product boards,cooperatives, etc.

Agro-food

industry NGOs Consumers

InfrastructureLaws, rulesandregulations

Values,norms andculture

Interactionsandnetworks

CapabilitiesMarketstructure

Source: (Klein Woolthuis, Lankhuizen, and Gilsing 2005; Van Mierlo et al. 2010; Klerkx, Van Mierlo, and Leeuwis 2012).

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knowledge and resources. We make a distinction between vertical fragmentation(i.e. the lack of hierarchical communication and coordination from the governmentdown), and horizontal fragmentation: the lack of communication and coordinationbetween two or more organizations of the same type. Strong network failure, on theother hand, refers to a situation when a (small) number of actors are locked intotheir relationship with each other without links to outsiders, causing tunnel visionand blocking new information from entering.

. Capabilities points to the technical and organizational capacity of the actors in thesystem to adapt to and manage new technology and organizational innovations.Examples are a certain level of entrepreneurship, adequately educated persons, timeto dedicate to innovation and networking skills.

. Finally, market structure refers to the positions of and relations between marketparties. In this case we focus on the organization of the knowledge market and howthe supply and demand of information and knowledge is arranged. Well knownproblems are formed by monopolies, or the lack of transparency in complex foodsupply chains.

Summarising: in this paper we take a broad AIS perspective (that includes a look atinfrastructures and institutions) to investigate how different categories of failures/facilitators can promote or hinder learning and collaboration in innovation networks.However, for practical reasons we have limited our comparison of the eight Europeancountries to a narrow set of actors: the actors of the traditional Agricultural KnowledgeSystem: the research, education and advisory (or extension) services.

Methodology

Data Collection

The arrangements of the agricultural innovation systems within eight different Europeancountries (England, France, Germany, Hungary, Italy, Latvia, the Netherlands andSwitzerland) were analysed by eight different research teams, each located within thecountry and with close experience and knowledge of the national AIS.

A first desk research included a description of the general set up of the AIS in therespective countries with particular emphasis on the different actors, roles, governance,funding mechanisms and paradigms towards learning and innovation. The desk study wassupplemented with a number of interviews performed with some of the key actors withineach country. Interviewees were selected based on their expertise of a sector or centralplayer within the AIS: universities, government agencies, innovation subsidisingagencies, multinationals, farmers unions and advisor groups, see Table 2. An interactiveworkshop concluded the investigation in each country. During this workshop the resultsof the literature study and the interviews were discussed in a broader audience ofstakeholders and experts.

The information from the literature review, interviews and workshops was used tocompose a country report: one report for each country. Some of these countries reportswere later reworked and published as a peer reviewed publication (see for instance:Nemes and High 2013; Home, Jawtusch, and Moschitz 2013; Lamprinopoulou et al.

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Table 2. Overview of interviews

Totala Farmers UnionsAdvice andconsultancy Extension

Government andpolicy

Product chains/agro-industry Research Education

Civil societyand NGOs

Hungary 11 4 2 5Italy 12 3 1 2 1 1 3 1Latvia 11 3 1 2 1 4 2The Netherlands 11 1 4 2 1 3 1England 13 2 1 1 2 1 2 3Switzerland 12 2 5 2 2 2France 3 1 2Germany 7

aTotal number of interviews does not necessarily correspond to type of organizations because of some double affiliations of interviewees.

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2014). However for the present study we used the eight original country reports as abasis.

As a first step in establishing a comprehensive comparative analysis, the differentcountry reports were reworked into a single ISP matrix by the authors. The information inthe different country reports was summarized and subsequently labelled using a closedcoding format that followed the different categories of innovation failures (infrastructure,legislation and regulations, values norms and culture, interaction and networks,capabilities and market structure). In a second step these broad categories were refinedinto more detailed subcategories using an open coding format. The resulting ISP matrixwith the subcategories were checked and adapted where necessary by the differentnational research teams in order to make sure the summaries and labels properly reflectedthe existing situation. Finally, the different subcategories of failures and successes withinthe ISP matrix were systematically compared and evaluated.

Results

Before we turn to the institutional categories we will first discuss some of the trendsaffecting the actors mentioned in the columns of the ISP matrix. Over the last 10 to 15years the traditionally public funded AKS actors have been subject to changingconditions all over Europe. Table 3 details some of these changes and the effect theyhave had on the roles and functions these actors have in the broader AIS.

The role of research institutes and universities as the dominant sources of knowledgeand innovations is slowly diminishing. The economic crisis that started in 2008 hasresulted in a further reduction of research budgets for agricultural research in manycountries. In several countries universities and research institutes are given strongincentives to merge and this has resulted in a concentration process of research facilities.Traditional categories of fundamental versus applied research and public versus privateresearch funding are starting to disappear and universities have increasingly started tocooperate with (large) agri-businesses in research projects. An exception is the situationin Hungary. Large international agro-food industries prefer to let their R& D to be doneoutside of the country at institutes with a higher academic reputation (such as for instancethe ETH in Switzerland). Because of the growing internationalization of research funds ofsuch an institute, the links with the national Swiss field are in danger of disappearing.

The organization of extension services shows the highest diversity between countries.The Netherlands and England have completely privatized their extension services, whilein France and Hungary a publicly funded extension service still exists. In most countriesthe public extensions services are accompanied by a range of private advisors as well.However, the distinction between commercial and non-profit advisory systems is slowlydisappearing. Private consultancy firms can have difficulty establishing themselves inthose countries where public funded extension services still provide a cheaper alternativefor farmers.

From the traditional AKS-triangle of Research, Education and Extension, agriculturaleducation is in the most difficult structural position. With the exception of Switzerland,all other countries report either a lack of funds, a lack of interest from students, or acombination of the two. Universities and extension services have started to create linkswith private and commercial actors for the purpose of generating and distributingknowledge, either by linking up with large corporations (universities) or by creating more

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Table 3. Changing roles and functions of the traditional AKS actors per country

CountryPublic agricultural research:institutes and universities Agricultural education Public agricultural extension

Private agricultural advisoryservices

England Concentration of research institutesover the last 30 years, from 30 to 3.Increasing cooperation betweenuniversities and agro-food industry.

Education foragriculture has shrunkbecause of lack of fundsand declining interest.

Public extension completelyprivatized.

A diverse advisory community hasemerged after extension serviceswere privatized.

France Universities are given incentives tocollaborate or merge.

The number ofagricultural colleges hashalved due to mergers.

Strong public extensionsystem still present.

Many advisory organizations,somewhat in competition. Howevernot a lot of private advisorycompanies. Agro-Food industry hasa prominent place in advice tofarmers.

Germany Agricultural faculties havedifficulties. They struggle forsurvival. Shift in research fromuniversities to private companies ingene tech and agricultural chemistry.

Mass education andbudget cuts weakeneducation capacity.

No public extension ineastern BundesländerTechnical- and economicadvice of public extensionservice often insufficient.

Great organizational diversity,growing number of private advisors.

Hungary Universities and research institutesstruggle for budget. Universitycontracts with industry decreasing:international firms go outside ofHungary for knowledge.

Agricultural education isin a bad shape: notenough students andageing faculty members.

Village extension servicesgive advice on legislation andsubsidies. Public servantswith a controlling task.

Commercial advisory services aresmall because subsidized options arecheaper.

Italy Reduction of public funds. Shifttowards European funds. Agro foodindustry is starting up joint researchwith universities and is growing inimportance.

Attention for linkagesbetween education andresearch is weak butgrowing.

Public extension is present insome regions of the South.

Private sector advice is mainlyconnected to large agro-foodcorporations.

StructuralConditions

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Table 3. (Continued)

CountryPublic agricultural research:institutes and universities Agricultural education Public agricultural extension

Private agricultural advisoryservices

Latvia Research institutes focus on practicalresearch in seeds and plants. Therole of private industry is growingslowly. R&D funding and sciencelinks to industry remain largelyneglected.

Decline in studentnumbers; decliningprestige and ageing ofteaching staff diminishesquality.

Extension gets a lot of policyattention; rest of AKSfunctions get less attention.

Largest consultancy firm isprivatized, but still retains closerelations to the Ministry ofAgriculture.

The Netherlands Mergers between universities,universities and vocational schools(HBOs) are increasing.Public privatepartnerships between agro-industryand universities is more and morecommon.

Links betweeneducation andagricultural businessesare sparse. Greeneducation lacks studentinterest.

Public extension completelyprivatized.

A wide array of brokers andintermediaries has become availableon all levels of the AIS.

Switzerland Main agricultural university (ETH)is focussing on internationalresearch. Links with the field inSwitzerland may decrease.

Vocational schools havea good infrastructure,and staff with a highlevel of training.

Public extension mainly dealswith the conformation withrules and regulations (directpayments).

Shift from public to private.

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commercial alternatives (advice). However such links have not yet been established ineducation. The links of agricultural education and other sectors are often not welldeveloped and businesses and schools have difficulty finding each other.

All in all, the competition for scarce resources has forced many organizations to startlooking into alternative financing models with either public or private funds as thecircumstances may require. The distinctions between different types of actors arebecoming more diffuse as all actors are moving into each other’s traditional corebusinesses: universities start do to applied research and R& D, (higher) education alsoperforms some consultancy and advisory roles and large agro-firms are carrying out theirown research, especially on genomics, feeds and fertilizers. When actors are not capableof doing these new activities themselves, they are looking for strategic partners tocollaborate or even merge with. Strategic cost-benefit calculations of the potentialrevenues of collaborations are becoming more important and there is less room forexperimentation and innovations that involve long-term issues where the pay-off are notdirectly clear.

Analysis of Trends and Conditions and their Effects on Social Learningand Collaboration

There is a large difference in reported characteristics not only between different countries,but also within some countries themselves (especially the ones with a federal or strongregional governance structure, like Germany, Switzerland and Italy). We will limit ourdiscussion of the results to some of the most common trends we have observed and wediscuss these in relation to their potential impact on collaboration and social learning inmultisectoral innovation networks (see Table 4).

Substantial Reduction of Public Research Funds Resulting in Increased Competition andInsecurity. The economic crisis has resulted in reduced research budgets and agriculturalresearch institutes are reported to be struggling to deal with these budget cuts. Thedecrease in funding creates a fierce competition at all levels of the AIS, including theoverarching European level. The increasing competition for contracts and financialresources leads to higher levels of insecurity and to less sharing of information which canhave detrimental effects on collaborative innovation projects and social learning.

Overregulation of Innovation Policies with Missing or Unsuitable Criteria forEvaluation. Regulations regarding the innovation policies are often considered to be asource of hindrance and not of support. The bureaucracy of many innovation programmesis a shared complaint between farmers, researchers and companies. Innovation policy isoften characterized by an overabundance of ‘red tape’ and overregulation. This complaintis made in both the liberalized Dutch AIS and the more traditional and centralized Franceand Hungarian AIS. In order to be eligible for funding, an innovation project is requiredto provide detailed information on the expected results, focusing often on hardmeasurable criteria and ignoring the softer outcomes of a collaborative innovationprocess like improved stakeholder relations and trust. This means that monitoring andevaluation of collaborative innovation projects is difficult because good and measurableeffect indicators of successful collaborative innovation projects are missing.

Structural Conditions for Collaboration and Learning in Innovation Networks 45

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Table 4. Structural analysis of trends and conditions per country and their potential effects on collaboration and social learning

CH DE EN FR HU IT LT NL Potential effects on collaboration and social learning

Infrastructure, investments and fundingLack of funds / decreasing funds. X X X X X X X More competition and more insecurity are not conducive for

collaboration, sharing of resources and learning.Legislation, rules and regulationsOverregulation, bureaucracy and volatility oftopics and criteria.

X X X X X Collaboration for collective goods are difficult to set up;Continuity/ stability of collaborative networks is threatened;long term effects are not invested in.

Monitoring, assessment and evaluation ofprojects and programmes is not consistentand systemically done.

X X X X X X Learning experiences not fully incorporated; Criteria not suitedfor ‘soft’ outcomes of social learning.

Norms, values and cultureSocial capital and trust: low or decreasing. X X X X X X X First steps towards collaboration is difficult.Contested vision of the future leads tocompetition between different innovationcoalitions.

X X X X X X X Can be a strong motivator: ‘us against them’, but can alsoeasily lead to wasted time, energy and resources on politicalstruggles.

Interactions and networksVertical and horizontal fragmentation andlack of coordination.

X X X X X X X Overview is missing of who does what; potentialcollaborations are difficult to establish if organizations are notaware of each other.

CapabilitiesEducation and specific information skills areoften missing (confusion of knowledgeconsumers).

X X X X X Difficulty in formulating knowledge questions and informationneeds hampers learning.

Barriers for interaction in different types oforganizational logic and incentives (scienceand farmers especially).

X X X X X X X Individual goals and incentives of people with a differentaffiliations can be difficult to overcome.

Market structureLack of demand of information services. X X X Dependence on embedded advice in commercial agro-

industrial products discourages participation in innovativeprojects that might threaten these old products.

Increasing competition between knowledgeproviders.

X X X X X X X X Too many competing advisory service providers can createconfusion, add to the bureaucratic burden and do notstreamline the collaborative process anymore.

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The second constraint has to do with the lack of stability in funding criteria andinnovation tenders. Shifts in political coalitions will result in political attention for certainareas to suddenly come up or disappear. There is a trend of increased accountability ofpolitics and public policy that increases the pressure on politicians and civil servants toshow ‘results’ of public investments made in the innovation system. This trend, combinedwith a shift towards more attention to short term thinking in general, often results inincoherent innovation policies that focus on short term results. Collaborative partnerstherefore have to invest a lot of time and effort in organising follow-up collaborations toachieve long term project goals as well, at the cost of the time that can be spend on thecurrent collaborative projects.

Perspectives on (Sustainable) Agricultural and Rural Development have becomeFragmented. Issues like multifunctional production, conservation of biodiversity, climatechange and rural development have led to increasing demands and requirements set onthe agricultural sector by different actors. As a result the vision on agriculture has becomefragmented in a lot of countries. Different innovative groups are competing with eachother, often with completely opposing ideas of sustainable agriculture. Although this cangive these groups a strong motivation to continue with their own projects (‘us againstthem’), at the national level there is a threat that a lot of time, energy and resources maybe wasted on unproductive political struggles.

An important cultural difference can be seen between countries like Switzerland andthe Netherlands, that generally love collaboration and consensus and a country likeHungary, where many farmers do not like anything ‘collective’ as a result of the years offorced collectivism in agriculture under communist rule. The downside of the Swiss andDutch preference for consensus is however, that risk taking is not well establishedculturally.

Social Capital and Trust are Low or Decreasing. It is also important that the fragmenta-tion of visions leads to conflicts between various actors within the AIS. Farmers feelundervalued and misunderstood by the general public and politicians: they have to dealwith what they feel are unrealistic demands of society regarding their ways of production.Trust and social capital are reported to be decreasing among several of the most importantpartners in the AIS. Trust and social capital are both important prerequisites for sociallearning and a lack of social capital and trust can prevent innovative collaborations totake off. The trust in the role of government is especially important and the trust offarmers in government is largely absent in some countries and under pressure in others.

Vertical and Horizontal Fragmentation Make it Difficult to Gain an Overview of the AIS.With the exception of Switzerland, all countries report fragmentation of their nationalAIS. However, the reasons for this fragmentation differ from country to country. In theNetherlands and England, government intentionally gave away an important instrumentto influence developments of the AIS when it decided to liberalize the extension services.In these countries, government and the reported vertical fragmentation is therefore anexpression of the lack of hierarchical steering mechanisms. Collaborative arrangementsand public-private partnerships have become popular instruments for the government tobe able to influence some of the developments within the sector, without prescribingsolutions.

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However vertical fragmentation can also be found in countries like Hungary and Latviawhere the organization of the AKS is aimed at improving the productivity of the smallholder farms. Publicly funded extension services hold an important position to performthis task. The reported vertical fragmentation in these countries is not so much the lack ofsteering mechanisms, but it is more the result of a lack of political interest combined withlimited funds.

Examples of horizontal fragmentation can be found in Germany and Italy where thereported fragmentation is the direct result of the organization of the state. Because of theirfederal and regional forms of government, there is also a wide variety of rules, regulationsand institutional interactions from region to region. Organizations can have difficulty toreach over regional boundaries. As a result the national AIS has a very high horizontalfragmentation which may be accompanied by a vertical type of fragmentation (dependingon the specific region). However, the reported success in Switzerland making anationwide transition to integrated pest management within a couple of years, showsthat this does not necessarily has to be the case. A federal system can still be effectivelymanaged, even at the national level if the country is small enough and actors can stillcommunicate with each other on a regular basis, led by a common goal.

Social learning depends on the exchange of information from a different backgroundand therefore the combination of different types of organizations within an innovationnetwork is important. The effect of the fragmentation of an innovation system, bothhorizontal and vertical, is that organizations will have difficulty finding each other andcoordinating effectively between different geographical regions, or hierarchical adminis-trative levels.

Capabilities are Missing for Formulating Information Needs and Collaboration.Differences in capabilities within the countries are mainly related to the differences inof their respective farming communities and particularly their level of education. Smallsubsistence farmers in Latvia and Hungary often hardly have any formal agriculturaltraining, while farmers in Switzerland and The Netherlands are among the highesteducated of Europe. However, this doesn’t mean that farmers in the Netherlands andSwitzerland have no difficulties in making changes. The shift to more entrepreneurialtypes of farming styles in Switzerland is for many farmers difficult. Similarly, in theNetherlands (and England) not all farmers possess the necessary skills to formulate theirspecific knowledge demands. This not only makes it difficult for them to successfullynavigate the market with commercial knowledge suppliers, but also search for partners tocollaborate with.

Institutional Logics and Incentives do Not Promote Collaboration. Almost in all countriesthe link between researchers and farmers is seen as the most problematic one. One of theexplanations is the fact that researchers nowadays are almost exclusively evaluated ontheir (peer reviewed) publication record and not so much on their societal impact. Thiscan make their participation in collaborative innovation networks problematic becausethey don’t always allow for perfect laboratory conditions and research questions are oftenthe subject of negotiations where scientific relevance is just one of several criteria.

Market Structure / Information Market is Not Conducive for Formation of InnovationNetworks. There is an increasing competition among different types of knowledge

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providers (i.e. research organizations and advisers), especially in countries where theextension services have been privatized (Netherlands and England). In other countries,public extension services often still dominate the information market, although thecompetition between them and other (private) advisers is also increasing. One of the risksis that when there are too many advisory service providers present, they create confusion,add to the bureaucratic burden and do not streamline the collaborative process anymore.At the same time there is among farmers not a lot of willingness to pay for advisoryservices, even in a country like the Netherlands. Many farmers therefore depend on the‘free’ advice they get when purchasing agro-chemicals, feeds or machinery. Thesecompanies often have a vested interest and only limited interest in collaborativeinnovation networks. They often discourage their clients to participate in innovativeprojects that might threaten their own business model.

Discussion

The question this paper started with was how the characteristics of different agriculturalinnovation systems can hinder or facilitate opportunities for social learning andcollaboration for innovations towards sustainable agriculture. Using the InnovationSystem Performance Matrix we have compared eight different European agriculturalinnovation systems with each other with a focus on the position of the actors of theagricultural knowledge system.

Limitations in the Application of the Innovation System Performance Matrix

The application of the Innovation System Performance Matrix has two limitations. Theframework was originally developed to aid in identifying the potential failures of aninnovation system. We have tried to adapt this approach in order to help us to think aboutboth the potential failures but also the positive sides, such as the potential opportunitiescertain institutional conditions may have for social learning and collaboration. However,we ended up identifying far more failures than opportunities and this might be a result ofthe particular focus of the original framework. However we think that it still correctlyreflects the most important issues. Collaboration, especially between groups withdifferent backgrounds, is hard and many attempts actually fail. The original researchreports of each country also show this, and the negative results have therefore not somuch to do with the translation of the reports into a single ISP matrix, but more with thefact that conditions in most countries are not yet very LINSA friendly.

The second disadvantage applies to the thinking in agricultural innovation systems ingeneral. This strain of literature often suggests that the actors in the system share acommon goal of generating new innovations. In reality different organizations pursuetheir own agenda, which may involve making sure that a particular threatening innovationwill fail. An AIS analysis is thus likely to overstate the problematic nature of politicalstruggles in innovation, which are in fact an indispensable part of many innovationprocesses (Swan and Scarbrough 2005). Despite these caveats, this paper shows thatinsights derived from an AIS perspective help to identify local problems and a number ofpromising measures to improve their innovative performance through collaboration andsocial learning.

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A Great Variety in Structural Conditions for Learning and Innovation Networks

Based on the results we can show that there are a number of interrelated structuralconditions that work for or against collaboration and social learning for sustainableagriculture. The reported fragmentation (both vertical and horizontal) in most countries ofthe AIS are a threat because different organizations are likely to have difficulty in meetingother organizations, which is the first step towards any collaborative endeavour. Theinformal rules values and culture, but also the capabilities of the individual actors help toexplain the motivation (or lack thereof) to participate in innovative networks, especiallyin countries like Hungary and Latvia. Finally, it has been shown that the formal rules andregulations regarding innovation, but also the amount of funds available and theconditions set on such funding, are often not conducive to cater to collaboration andsocial learning, especially when ‘common goods’ like sustainable agriculture areconcerned.

Our study shows that in all investigated countries new actors have become involved inredefining the functions of the countryside and agricultural production therein, or thattheir role has changed drastically as a result of the pressure to privatize extensionservices. This is not unlike earlier work done in this area (see for instance: Frouws 1998;Hermans et al. 2009; Cristóvão, Koutsouris, and Kügler 2012) but in this paper theemergence of the new roles of these actors and their accompanying discourses and visionfor the agricultural sector has been linked to the idea of a broader agricultural innovationsystem where these actors operate in. However, the increasingly broad range of actorsinvolved in each of the eight different countries, has made it difficult to make a propercomparison. We have therefore chosen to focus our comparison to the traditional AKSactors of research, education and advisory and extension services. This is a limitation ofthe present study, but since these traditional actors still hold a significant position in allthe investigated countries, the resulting error is considered to be relatively small.

We did not find a set of correlating conditions that would make it possible to categorizethe different countries into a neat AIS typology. For instance, countries where theextension and advisory services are more or less privatized and where the private sectorthus plays a dominant role are more susceptible for vertical fragmentation but less tohorizontal fragmentation of the AIS. Countries that still have a publicly funded extensionservice should suffer less from vertical fragmentation, at least in theory. However ourresults show that in reality a lack of funds and political interest still leads to verticalfragmentation in these cases as well. Horizontal fragmentation of the AIS is associatedwith countries with federalized or regionalized form of governance like in Germany andItaly. However, a federal governance system doesn’t necessarily have to lead to horizontalfragmentation of the AIS, as is shown by the example of Switzerland.

The Need for a Tailor made Application of Innovation Policies, with Some CommonDemeanours

The fact that we were unable to come to an AIS typology once again emphasizes the needto formulate innovation policies in support of LINSA which is relevant in light of theoperationalization of EU wide policies such as the European Innovation Partnership.Following earlier observations (for instance of: Klerkx and Nettle 2013; Tödtling and

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Trippl 2005) this requires locally adapted policies, that should be based on the particularhistorical, geographical, economic and social conditions that typify a national AIS.

Based on our analysis we can suggest two courses of action to better supportcollaborations and social learning for innovation. An AIS where the traditional AKSactors are still in charge and determine the agenda for research and extension (for instanceItaly), typically has difficulty in dealing with the acceptance of (radical) new ideas andthe organizations espousing those ideas. Policies that promote collaboration and sociallearning in innovation networks thus provide an opportunity to incorporate some of these‘outsiders’ into the existing networks, thereby broadening the perspectives on agriculturalchange. However, two remarks must be made here. Firstly, as Ingram et al. (2014)showed, not all of these ‘alternative’ innovation networks would actually want to link upwith the traditional AKS actors and some care must be taken in selecting the right personswithin an innovation network. Secondly, the lack of formal education, especially ofsmallholder farmers in countries like Latvia and Hungary, hampers their ability toorganize themselves in these collaborative innovation networks and they have difficultyto be viewed as valuable partners within such networks and to have their interests heard.In addition, therefore, particular attention needs to be paid to the development of suchskills.

Liberalized agricultural innovation systems with a diverse privatized advisory andextension service, (as well as a range of agricultural research institutes involved ininnovation and knowledge development) typically have problems in coordinatinginnovations for long-term environmental issues and other typical public goods (resem-bling earlier noted concerns by Laurent, Cerf, and Labarthe 2006). Supporting learningand collaboration in innovation networks is a promising approach for solving some of thelong term public policy goals. However, our analysis showed that the difficulty inproperly monitoring, assessing and evaluating the outcomes of collaborative innovationnetworks forms an important problem that should be dealt with.

Conclusions

Results of this study show that the different categories of the innovation systemperformance matrix all have different results in terms of how they promote or hinderlearning and collaboration in innovation networks in the eight European countriesassessed. Reduction of public research funds is reported to be one of the main problemsin all countries and it results in increased competition and insecurity which form badconditions for collaboration and social learning. Another important result of the study isthat many innovation policies are rather complex and not particularly well suited toevaluate the results of collaborations and social learning, especially for common goodslike sustainable agriculture.

The practical relevance of this study is that is shows that each national AIS is more orless unique. In order to promote collaboration and social learning it is therefore necessaryto see whether the particular AIS is suffering from vertical or horizontal fragmentationand whether there is a need for inclusion of new actors, or whether certain innovations forcollective goods should be promoted. We found the innovation system performancematrix to be a helpful tool for this purpose.

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Acknowledgement

We acknowledge the contribution of our SOLINSA colleagues whose country reportshave been used in the preparation of this paper.

Funding

This work was supported by the European Commission under the 7th Research FrameworkProgramme [grant agreement number. 266306]. The views presented here are the views of theauthors and do not necessarily represent the views of the Commission.

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