# Fostering action perspectives to support crop diversification: Lessons from 25 change-oriented case studies across Europe

Margot Leclère, Lenn Gorissen, Yvonne Cuijpers, Luca Colombo, Mirjam Schoonhoven-Speijer, Walter a H Rossing

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## ► To cite this version:

Margot Leclère, Lenn Gorissen, Yvonne Cuijpers, Luca Colombo, Mirjam Schoonhoven-Speijer, et al.. Fostering action perspectives to support crop diversification: Lessons from 25 change-oriented case studies across Europe. Agricultural Systems, 2024, 218, pp.103985. 10.1016/j.agsy.2024.103985. hal-04724148

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Submitted on 7 Oct 2024

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1 Fostering action perspectives to support crop diversification: Lessons from 25 change-oriented case studies across Europe

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4 Margot Leclère<sup>a,b,*</sup>, Lenn Gorissen<sup>b,c</sup>, Yvonne Cuijpers<sup>b</sup>, Luca Colombo<sup>d</sup>, Mirjam Schoonhoven-

5 Speijer<sup>b</sup>, Walter A.H. Rossing<sup>b</sup>

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7 <sup>a</sup>Université Paris-Saclay, INRAE, AgroParisTech, UMR Agronomie, 91123 Palaiseau, France

8 <sup>b</sup>Farming Systems Ecology Group, Wageningen University and Research, PO Box 430, 6700AK

9 Wageningen, The Netherlands

10 <sup>c</sup>University of Twente, section Knowledge, Transformation & Society (KiTeS), Netherlands

11 <sup>d</sup> FIRAB, Fondazione Italiana per la Ricerca in Agricoltura Biologica e Biodinamica, Rome, Italy

12 * corresponding author: margot.leclere@inrae.fr

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14 Abstract

15 CONTEXT

16 There is growing evidence that diversified cropping systems can contribute to the fundamental reorientation of food production. However, actors involved in crop diversification (CD) initiatives often lack concrete action perspectives, i.e., ideas and expectations on what they can do to achieve their goals.

19 Indeed, various authors have pointed out the lack of operational guidance by high-level theories on innovation governance for actors in innovation niches that have to cope with complexity and unpredictability.

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23 OBJECTIVE

24 This paper aims to fill this gap by studying and cross-analysing the enactment of 25 Case Studies (CSs) consisting of multi-actor CD initiatives spread across 10 European countries.

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27 METHODS

28 We developed a heuristic framework that aimed to unpack the key components in the CSs' ways of working towards CD (motivations, participants, intervention levels, activities and learnings). Data collection was based on reflexive self-assessment documents produced by the CSs as part of the actor-oriented co-innovation approach. We cross-analysed the data to highlight patterns among the CSs in their ways of working towards CD using both qualitative and quantitative (Multiple Factor Analysis) approaches, in order to provide a diversity of insights to support action perspectives.

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35 RESULTS AND CONCLUSIONS
Seven groups of CSs were defined and qualified through the qualitative approach. Group 1 worked on fostering co-learning between farmers and developing participatory approaches. Group 2 specifically worked on enhancing cooperation between farmers. Group 3 promoted strip cropping through the development of a community of practice. Group 4 was concerned with gradually removing lock-ins to support the introduction of diversifying crops. Group 5 worked at aligning actors to create value chains to enhance grain legume production. Group 6 was about fostering locally integrated food systems for legumes and vegetables. And, Group 7 gathered CSs searching for ways out of wicked situations. The results from the MFA partially confirmed these similarities between the CSs but also revealed major differences between some CSs that were identified as similar with the qualitative approach.

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## SIGNIFICANCE

In this paper, we propose a framework to describe how actors within innovation niches organized themselves, worked and learned together to stimulate a transition towards more sustainability in food systems. We assume that this framework, is usable by other project leaders of innovation niches to monitor and analyze their change processes toward sustainability jointly with practitioners. Grouping the CSs is a way to scale out these learnings and contribute to the production of action perspectives and mobilizing impact that are useful for people both inside and outside our project.

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## Keywords

Co-innovation; innovation niches; complex adaptative systems; cross-analysis; multiple factor analysis

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## Highlights

* Actors involved in crop diversification initiatives often lack of action perspectives to manage complexity and uncertainty.

* The enactment of crop diversification in 25 innovations niches was studied using a qualitative comparative approach.

* Seven groups of case studies were defined through the qualitative approach and then tempered based on the Multi-Factor Analysis

* This analysis contributed to reduce task uncertainty within the case studies of the project by informing social learning.

* More widely, by making explicit the processes and by comparing them, we provided guidance to actors in coping with complex and unpredictable situations.
# How to support crop diversification in Europe ?

Fostering action perspectives by studying and comparing the enactment of 25 case studies working on crop diversification across Europe

**DATA COLLECTION** – Collecting insights on case studies's current state, goals, strategy and learnings through the analysis of self assessment documents

5 themes of the heuristic framework

**INDIVIDUAL CHARACTERISATION** –
Describing the individual ways of working towards crop diversification across the 25 case studies through the use of an heuristic framework

**CROSS-ANALYSIS AND CLUSTERING** –
Identifying commonalities and proximities between the case studies through a qualitative systematic and comparative approach

7 groups identified

**Participants** : diversity of actors and stakeholders involved in the CS

**Intervention levels** : levels at which the CS worked and aspired to induce change

**Motivations** : reasons for emergence of the CS

**Activities** : main types of activities of the CS and their sequence in time

**Learnings** : lessons learned in dealing with uncertainties

**Actionable knowledge** that provides guidance for actors in coping with complex and unpredictable situations
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# 1. Introduction

While mainstream agriculture in industrialized countries has succeeded in supplying large volumes of foods to global markets, its major contributions to exceedance of several planetary ‘boundaries’ (Campbell et al., 2017) have stimulated a search for alternative, more inclusive models of food production and consumption (Poux and Aubert, 2018). There is growing evidence that diversified cropping systems can contribute to fundamental reorientation of food production by improving multiple outcomes of agricultural production systems simultaneously, such as enhancing soil and water quality, biodiversity, and climate change mitigation and adaptation (Beillouin et al., 2021; Tamburini et al., 2020). Changes towards diversification will need to rely on the simultaneous and organized mobilization of many stakeholders as part of system innovation (Elzen et al. 2004) and transition (IPES-Food, 2016).

It has been proposed that sustainability transitions are stimulated by the formation of innovation niches (Elzen et al., 2012; Flood Chavez et al., 2023; Meynard et al., 2017; Smith and Raven, 2012). Innovation niches are physical, ecological, technological, and virtual spaces where a diversity of stakeholders come together to develop and experiment with new modes of production and institutional arrangements that enable interactions across boundaries (e.g., sectoral, organizational, professional, disciplinary, cultural) (Elzen et al., 2012; Schot and Geels, 2008). In this paper, we zoom in on the working of agricultural innovation niches in transitioning towards diversified cropping systems.

Agricultural innovation niches are nested within the overarching agricultural innovation systems, which are increasingly understood as complex adaptive systems (Klerkx et al. 2010; Kok et al. 2021). Complex adaptive systems (CAS) thinking deviates from the formerly accepted linear vision of innovation and its assumption that knowledge is passed on from researchers to farmers through advisors, and instead emphasizes processes of self-organization, coincidence, and non-linear dynamics (Leeuwis and Aarts 2011). In a CAS, a wide array of heterogeneous actors adapt their strategies and actions based on the actions of others and on changing system conditions, while contributing to these changing conditions through their evolving responses to them (Spielman et al. 2009; Douthwaite and Gummert 2010).

De Wildt-Liesveld et al. (2015) describe *task uncertainty* as the difference between the information required to perform a specific task and the amount of information possessed by the decision maker. Both the ambiguity of cause-effect relations inherent to complex adaptive systems and the uncertainty about the goal to be achieved that typically characterize sustainability transitions in agriculture result in high levels of task uncertainty for actors involved in agricultural innovation niches (Caron et al., 2014; Vermunt et al., 2022). As a result, researchers and other societal actors involved in crop diversification initiatives often lack concrete *action perspectives* (Vermunt et al., 2022), referring to ideas and expectations on what they can do to achieve their goals (Roorda, 2020).

Some authors have pointed out the lack of operational guidance on innovation governance for actors in innovation niches that have to cope with CAS complexity and unpredictability and have proposed to better “operationalize” project theories of change by elaborating middle-range theory (Douthwaite and Hoffecker, 2017). Concomitantly, complexity-aware governance approaches, such as co-innovation
(Ingram et al., 2020; Rossing et al., 2021) and arenas, such as sustainability labs (Bergmann et al., 2021; Kok et al., 2023), have emerged with the underlying ambition to reduce task uncertainty within agricultural innovation niches through a better connection between science and society actors and the stimulation of social learning. Both categories of governance approaches have been shown to have positive impact on supporting sustainability transitions, especially by fostering action perspectives within projects (Kok et al., 2023; Small et al., 2021). The issue of the lack of action perspectives for sustainability transition has been also tackled by several authors at another level, beyond individual project, by performing cross-case analysis and typology building (Matt et al., 2017; McCrory et al., 2022; Mignon and Kanda, 2018), as « typologies can serve as highly useful tools for organizing existing knowledge and guiding further empirical observations» (Kanger, 2024). Such approaches are in line with insights from design sciences that assume that creativity can be fostered by sharing inspiring examples (Agogué et al., 2014; Fink et al., 2010).

In this paper, we propose to add to this field of research by (i) specifically addressing the question of fostering action perspectives for crop diversification and (ii) hybridizing these approaches by studying and comparing its enactment in 25 agricultural innovation niches using a quali-quantitative systematic and comparative typology approach to support actions perspectives in itinere during the project execution. More precisely, this study was guided by two research questions:

(i) How did actors in 25 crop diversification niches organise themselves halfway through a large European participatory research project?

(ii) Which differences and similarities in the ways of working towards crop diversification can be identify from this sample in order to inform action perspectives of innovation niches' actors within and outside the project?

In the following sections we first describe the DiverIMPACTS project on crop diversification that provided the empirical material for this study, and the qualitative and quantitative methods deployed. Then, the resulting innovation niche typologies are exposed. Finally, we discuss the merits and limitations of the approach and the results for reducing task uncertainty and providing action perspectives to actors in agricultural innovation niches, and the contribution to theory development.

## 2. Materials and methods

### 2.1. The 25 change-oriented case studies of DiverIMPACTS

#### 2.1.1. Characteristics of the case studies

DiverIMPACTS (Diversification through rotation, Intercropping, Multiple Cropping, Promoted with Actors and value-Chains Towards Sustainability) was a five-year (2017-2022), 11 M€, European project funded as a Horizon 2020 Research and Innovation Action that aimed 'to achieve the full potential of diversification of cropping systems for improved productivity, delivery of ecosystem services and resource-efficient and sustainable value chains' (https://www.diverimpacts.net). At the beginning of the
project, a network of 25 change-oriented Case Studies (CSs) was set up, consisting of existing and newly elaborated initiatives on crop diversification spread across 10 European countries (Table 1). In each CS, a project leader supported by a process monitor (sensu van Mierlo et al. 2010), created a facilitating environment for engaging relevant participants to develop and implement various activities and thus foster the adoption of crop diversification as an alternative to dominant production and consumption systems in place in their countries.

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<table><caption>**Table 1: Description of the 25 change-oriented Case Studies (CSs) of DiverIMPACTS spread across 10 European countries.** At the beginning of the project, CSs were grouped into five clusters spearheaded by a cluster leader to ease knowledge exchanges between CSs as part of the co-innovation approach.</caption><thead><tr><th>CS#</th><th>CS title</th><th>Country</th><th>Cluster</th></tr></thead><tbody><tr><td>1</td><td>Breaking maize monocultures</td><td>Netherlands</td><td></td></tr><tr><td>2</td><td>Herbal or diverse leys use as service crops and valorised through grazing</td><td>UK</td><td>Crop diversification with special attention for service crops</td></tr><tr><td>3</td><td>Crop diversification to improve water quality stability</td><td>Germany</td><td></td></tr><tr><td>4</td><td>Service crops valorised through grazing</td><td>Belgium</td><td></td></tr><tr><td>5</td><td>Introducing crop diversification in maize monoculture</td><td>France</td><td></td></tr><tr><td>6</td><td>Developing organic oilseed production with oilseed rape and hemp</td><td>Switzerland</td><td></td></tr><tr><td>7</td><td>Diversification of cereal-based rotations with soybean</td><td>Hungary</td><td></td></tr><tr><td>8</td><td>Diversification of cereal-based rotations with pulse crops or multispecies service crops under adverse conditions</td><td>Romania</td><td>Crop diversification under adverse conditions</td></tr><tr><td>9</td><td>Diversification of durum wheat cropping systems in a semi-arid environment with sulla clover, hemp and chickpea</td><td>Italy</td><td></td></tr><tr><td>10</td><td>A successful diversification initiative based on the production of soft fruits, vegetables and oil crops</td><td>Poland</td><td></td></tr><tr><td>11</td><td>Co-design of diversified systems in Pays de Loire</td><td>France</td><td></td></tr><tr><td>12</td><td>Sustainable diversification of organic stockless cropping systems: interconnecting organic arable cropping systems and no-till practices</td><td>Belgium</td><td></td></tr><tr><td>13</td><td>Introduction of innovative crops and of legumes to foster the sustainability of arable systems under oceanic climate</td><td>France</td><td>Crop diversification in systems from Western Europe</td></tr><tr><td>14</td><td>Diversification of arable crop rotations, specialised in winter crops, under shallow and stony clay-limestone soils</td><td>France</td><td></td></tr><tr><td>15</td><td>Growing pulses and innovative crops for a less resource-intensive diet</td><td>UK</td><td></td></tr><tr><td>16</td><td>Spatial, temporal and genetic diversification of intensive systems</td><td>Netherlands</td><td></td></tr><tr><td>17</td><td>Grain legumes intercropping with cereals</td><td>Belgium</td><td></td></tr><tr><td>18</td><td>Diversification of organic arable rotations through the production of minor grain crops as mono- or intercrop for local processing toward local human consumption</td><td>Belgium</td><td>Diversification through intercropping with specific focus on grain legumes</td></tr><tr><td>19</td><td>Local legumes – Collaboration within the whole value chain</td><td>Sweden</td><td></td></tr><tr><td>20</td><td>Intercropping for species other than peas and faba beans in association with cereal</td><td>Switzerland</td><td></td></tr><tr><td>21</td><td>Crop diversification through inter-farms co-operation</td><td>Belgium</td><td></td></tr><tr><td>22</td><td>Cereal-vegetable systems for food quality</td><td>Italy</td><td></td></tr><tr><td>23</td><td>Multi-cropping for vegetables in the short supply chain</td><td>Netherlands</td><td>Diversification of vegetable cropping systems</td></tr><tr><td>24</td><td>Crop diversification in protected vegetable systems</td><td>UK</td><td></td></tr><tr><td>25</td><td>Enhancing crop diversification of vegetable food chains in the Roussillon area through joined actions from farms to market</td><td>France</td><td></td></tr></tbody></table>
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2.1.2. Case study monitoring and data collection during the project

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The CSs participated in co-innovation, an actor-oriented approach that uses elements from niche governance, Participatory Impact Pathway Analysis (PIPA; Douthwaite et al. 2007), and outcome mapping (Earl et al. 2001), and builds on complex adaptive systems theory, social learning, and dynamic monitoring and evaluation (Rossing et al. 2021). The approach was introduced during the project's first two years through three rounds of co-innovation workshops. In the first workshop, the facilitation team (including three co-authors of this paper) introduced the systemic PIPA framework. CS leaders and monitors then used its components, including vision and mission formulation, socio-technical systems analysis, problem trees, and action plan elaboration, to prepare for further work with CS participants. More components were added during the second and third co-innovation workshops, including a more profound system and actor analysis based on Reflexive Interactive Design (RIO; Bos et al. 2009), training in interviewing and facilitating meetings, and peer-to-peer learning among participants based on insights from CS analyses.

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2.2. Cross-analysis of the 25 CSs

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2.2.1. General approach

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To foster action perspectives through typology building, the first step of our approach consisted in developing a heuristic framework to look at all CSs with a common lens. Indeed, the self-assessment documents previously described were designed to fuel learning and reflexivity rather than describe the transition pathways towards crop diversification. The design process of the framework – especially the theoretical background - and its application to the CSs is presented in section 2.2.2.

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The second step of the approach consisted in applying successively a qualitative and a quantitative approach to build typologies. Participatory and statistical approaches for making typologies have been well-documented in the agricultural sciences (e.g. Kuivanen et al. (2016)) and combining both approaches has been recommended to mitigate drawbacks associated with each (Alvarez et al., 2018). In our case, we assumed that combining approaches was relevant to address the objective of “fostering action perspectives”. The qualitative approach was designed to provide “types” that were easy to identify

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with, thus allowing CS actors to find inspiring examples. The quantitative approach, on the other hand, was used as a more systematic approach to call into question the first typology, find new proximities among CSs, and further open up the scope for action perspectives. The methodologies used for the qualitative and quantitative approaches are presented in sections 2.2.3 and 2.2.4, respectively.

### 2.2.2. Individual CS analysis using a heuristic framework

Our heuristic framework was developed based on an abductive reasoning, i.e., where an ‘original framework is successively modified, partly as a result of unanticipated empirical findings but also of theoretical insights gained during the process’ (Dubois and Gadde, 2002), by combining inputs from literature and from our own experiences in innovation and/or crop diversification projects. The design of the framework relied on the premise that change towards crop diversification is a dynamic process during which a diversity of actors (farmers, consumers, advisors, researchers, policymakers, etc.), in response to an unsatisfactory situation, collectively and collaboratively explore, design, implement, and assess various options to work towards a shared overarching objective (Blesh et al., 2023; Revoyron et al., 2022; Schiere et al., 2012; Schiller et al., 2015). We also assumed that, as part of this process, learning based on reflexivity allows for changing and adapting processes to generate desired results (Bos et al., 2009; Falconnier et al., 2017; Rossing et al., 2021; van Mierlo et al., 2010). Based on these two assumptions and drawing on insights from transition and innovation theory scholars (Hoffecker, 2021; Lacombe et al., 2018; Luederitz et al., 2017; McCrory et al., 2022), we defined a set of five themes to scrutinize each CS, each theme being related to a key component of the change towards crop diversification (Table 2). The first theme dealt with the main *motivations* underlying the emergence of the CS that prompted the actors to work towards more diversified cropping and farming systems. The second theme focused on the *participants* in order to provide information either on their role within the food system (e.g., advisory) or on their place within the food value chain relative to the farm level (e.g., upstream and downstream actors), and on their degree of involvement in the CS. For the latter we distinguish between actors, individuals or organizations actively involved in CS activities, and stakeholders, individuals or organizations that had an interest in or an impact on the CS activities without being actively involved. The third theme was about *intervention levels* and aimed at providing information about the level(s) at which the CSs worked and aspired to induce changes. Crop diversification is a generic term that encompasses a wide range of agricultural practices that can be conceptualized and assessed at different levels, from field to landscape (Beillouin et al., 2019; Hufnagel et al., 2020). The fourth theme concerned the CS *activities* and their purposes. Finally, the fifth theme was about *learnings*, and aimed at summarizing the topics on which the CS participants stated to have learned while working towards crop diversification.

After the themes were defined using the various self-assessment documents described in section 2.1.2, we collected and formalised information [i.e., text excerpts] related to these five themes for each CS. To be able to compare CSs, we defined categories within each theme. For example, within the theme
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'motivations' the reasons for the emergence of a CS were classified into seven categories, including
pedo-climatic, environmental, political, agronomic, societal, organisational, and economic (Table 2).

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For the first three themes, the categories were based on well-established and pre-existing classifications.

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For classifying the CS activities, we adapted an existing framework, proposing types of ‘scaling strategies’ to support companies in organising strategic thinking about achieving desirable societal impact (Kalafatas, 2006), drawing on Hoffecker’s (2021) definitions of activity types in inclusive innovation-oriented projects. By analogy, the CS activities were interpreted as the CS participants’ efforts to achieve a desired impact, i.e., stimulating the implementation and adoption of diversified cropping systems in line with the DiverIMPACTS objectives and the categories were thus adapted accordingly. Finally, seven categories of learning were built inductively based on the reported learnings in the CSs’ Learning Histories.

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### 2.2.3. *Cross-analysis of the 25 CSs using a qualitative approach*

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To establish a first typology, we used the outputs from the application of the heuristic framework to the CSs (Table 1) and our own knowledge of the CSs (not formalized) that resulted from our regular interactions with the actors, to group the CSs inductively. We did not search for a full match in the individual profiles that emerged from the application of the framework, but rather for a common ground in the ways of working. The methodology was inspired by studies in ethnography (Hummel and van der Duim, 2016; Jørgensen and Schou, 2020) that have worked on making emerge “modes of ordering” that are “recurring patterns embodied within, witnessed by, generated in and reproduced as part of the ordering of human and non-human relations” (Law, 1994).

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Each group was then characterized in terms of their prominent themes and the categories within them that together describe their way of working towards crop diversification halfway through the DiverIMPACTS project. In addition, an analysis of the learnings reported within each group was performed to qualify the nature of their potential contribution to action perspectives for crop diversification outside the project through knowledge production.

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### 2.2.4. *Cross-analysis of the 25 CSs using Multiple Factor Analysis*

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To complement the inductive grouping and enhance learning by comparison, a statistical approach for typology building was applied. We used Multiple Factor Analysis (MFA) to reduce variability in the data and identify statistical similarity among the CSs. MFA is a multivariate data analysis method used to define the distance between individuals described by groups of variables in a dataset, and visualize the results (Pagès, 2002). MFA is typically used to analyse data from surveys in which the questions are organized by themes or dates.

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To apply MFA to our dataset, we likened the outputs of the individual CS analysis (Table 1) to the results of a survey in which the 25 CSs would have answered yes/no questions for each of the categories (variables) of the five themes of the framework (groups of variables). After preliminary analysis, we

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excluded the theme 'learnings' from the analysis to simplify interpretation as it had a low contribution
on the four first projection dimensions. To compare the typologies obtained with the two approaches,
we included the groups identified in the qualitative analysis as a supplementary explanatory variable in
the model, represented by their number. All analyses were conducted using Rstudio (version 4.2.1) and
the FactoMineR package.
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<table><caption>Table 2: Overview of the heuristic framework developed to unpack the ways of working towards crop diversification in the 25 Case Studies (CSs) of DiverIMPACTS.</caption><thead><tr><th>THEMES</th><th>CATEGORIES</th><th></th></tr></thead><tbody><tr><td>Motivations (reasons for emergence of the CS)</td><td>Pedoclimatic; Environmental; Political; Agronomic; Societal; Organizational; Economic</td><td></td></tr><tr><td>Participants (actors and stakeholders involved in the CS)</td><td>Farmer; Farmers' association; Rural development association; Technical institute; Advisory structure; Upstream actor; Downstream actor; Consumer; Research institute; Policymaker; NGO or foundation; Other</td><td></td></tr><tr><td rowspan="5">Intervention levels (levels at which the CS worked and/or aspired to induce change)</td><td>Cropping system</td><td>When activities were assumed to impact the field in terms of crop management or crop sequence.</td></tr><tr><td>Farm system</td><td>When addressing topics outside the field's perimeter but inside the farm's perimeter.</td></tr><tr><td>Agricultural system</td><td>When the aim was to change how agricultural production systems are organised, for example, in different specialised farming systems, such as animal husbandry, arable farming, dairy farming.</td></tr><tr><td>Geographic region</td><td>When focussing on a specific geographic area (local to national).</td></tr><tr><td>Value-chain</td><td>When working on connecting different parts of the value chain and the actors involved, ranging from raw material production and processing to a final product or service to be sold to the consumer.</td></tr><tr><td rowspan="8">Activities (main types of activities of the CS)</td><td>Food system</td><td>When working on the value chain and the action of "eating".</td></tr><tr><td>Action plan development</td><td>Setting up a strategy or course of action to realize the CS goals.</td></tr><tr><td>Partnerships/alliances</td><td>Organizing organizations or individuals into a network or association mobilized to advocate for shared goals and policies. Collaborating with other organizations to deliver services or address needs in new locations.</td></tr><tr><td>Generating knowledge</td><td>CS-internal knowledge building by accumulating information within the CS network through farm trials, workshops, visits, etc.</td></tr><tr><td>Promoting certain models</td><td>Increasing impact by encouraging the replication of the CS model by promoting the CS and its activities to people outside the CS network.</td></tr><tr><td>Technical assistance</td><td>External knowledge building by providing technical assistance, training, or consulting to people interested in offering similar programs or activities outside the CS network.</td></tr><tr><td>Knowledge dissemination</td><td>External knowledge building by sharing information with people outside the CS network through publications, the internet, or presentations.</td></tr><tr><td>Direct advocacy and lobby</td><td>Engaging public policymakers, legislators, and other government officials to influence the legislative or resource environment for the CS goals.</td></tr><tr><td></td><td>Expand geographically</td><td>Increase impact by offering existing CS activities or models in new locations.</td></tr></tbody></table>

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<table><tr><td></td><td>Influence public policy (environment)<br/>Packaging/licencing<br/>Increase quality or quantity of impact</td><td>Increase impact by changing public policy.<br/>Packaging a successful product/program and licencing it to existing organizations in other locations.<br/>Increase impact by providing existing CS activities or models more frequently or in greater quantity in current locations or by improving the quality of existing CS activities or models.</td></tr><tr><td><strong>Learnings</strong> (lessons learned in dealing with uncertainties)</td><td>Specific learning about CS topic<br/>Lock-ins hindering diversification in the CS<br/>External factors influencing the CS dynamic<br/>Internal factors influencing the CS dynamic<br/>Enablers to the CS dynamic<br/>CS mission and role<br/>Looking forward</td><td>Learning resulting directly from the activities realised in the CS (e.g., experiments, workshops, consumers study, etc.).<br/>Learning about the barriers to diversification.<br/><br/>Factors not under the control of the CS, belonging to the context in which the CS is embedded. The influence reported can be positive or negative.<br/><br/>Factors inherent to the CS that influenced the general dynamic of the innovation process.<br/><br/>Factors fostering the dynamic between actors of the CS team and enabling progress towards CS goals. The CS presents these enablers as mandatory or necessary for a successful diversification project.<br/><br/>Learning about the specific mission and role of the CS core team (i.e., leader and monitor).<br/>Statements about future activities and the future evolution of the CS dynamic.</td></tr></table>
## 3. Results

### 3.1. Diversity of ways of working towards crop diversification across the 25 CSs

The analysis at the individual level revealed that each CS was driven by a distinct blend of pedoclimatic, agronomic, environmental, economic, organizational, political, and/or societal factors. This, in turn, entailed the engagement of a distinct group of actors and intervention levels, as well as the enactment of a unique set of activities. Each CS thus adopted its own distinct approach towards crop diversification (Figure 1).

<caption>Figure 1: Overview of the diversity in the ways of working towards crop diversification across the 25 CSs of DiverIMPACTS based on the results from the application of the heuristic framework.</caption>

The CSs were grouped based on the qualitative typology.

<figure><img src="image_3.png" /></figure>
3.2. Qualitative identification of patterns in the ways of working towards crop diversification

Communalities among CSs emerged by considering the emphasis each CS put on the various themes and the categories within them. In this way, we identified seven groups of CSs, that covered a range of scales at which the CSs worked or desired to achieve impact, and the level of diversity in the participant types involved (Figure 2).

**Figure 2: Overview of the seven groups of case studies (CSs) of DiverIMPACTS based on similarities in the way or working towards crop diversification.** Groups are represented as a function of the level of diversity of the types of participants (x-axis) and the scale of work or impact (y-axis).

<figure><img src="image_4.png" /></figure>

3.2.1. *Group 1: Fostering co-learning between farmers (CS5, CS12, CS14, CS23)*

This group gathered four CSs engaged in developing sustainable and viable farms based on diversified cropping systems, combining agronomic, environmental and economic performance. The initial motivations were often agronomic (e.g., difficulties with weed control), pedoclimatic (e.g., poor soil fertility) and/or environmental (e.g., water pollution) (Figure 1). The CSs in this group adopted the same strategy: fostering co-learning between farmers by organizing a knowledge sharing network. The primary actors involved were farmers (Figure 2), who often initiated the project and actively participated in the CS's activities, such as conducting experiments on their farms or hosting events. While other actors like research institutes, advisory structures, and downstream actors could be involved, their key role was to bring knowledge to the group of farmers. The core teams, mainly composed of individuals from advisory structures or technical institutes, facilitated the farmers' networks and contributed to knowledge production for sharing within the networks. The activities within this group of CSs revolved around generating knowledge, primarily through on-farm trials and field visits, and disseminating
knowledge through various written documents, such as scientific papers, farmers magazines, and technical guides (Figure 1). Reported learnings in this group were mainly about agronomic (e.g., weed management techniques in CS23) and methodological (e.g., participatory approaches by and for farmers to promote change at cropping system level) knowledge. Actions perspectives inside and outside the project thus benefitted from knowledge for farmers’ empowerment or new methodologies and tools to support participatory approaches towards crop diversification, as reflected in a recent paper based on experiences of a CS of this group (Longis et al., 2024).

### 3.2.2. Group 2: Enhancing arable and livestock farmer cooperation (CS 2, CS4, CS11, CS21)

This group of four CSs aimed to enhance cooperation between arable and livestock farmers to contribute to both diversified cropping systems and geographical regions (Figure 2). The motivation behind the CS was twofold: firstly, to address agronomic and environmental issues related to the simplification of cropping systems, and secondly, to support livestock farmers in developing healthy, sustainable, and viable feed supplies (economic and societal motivations) (Figure 1). The shared goal of the CSs was to develop sustainable and viable partnerships between the two groups of farmers, which they achieved by documenting and evaluating ongoing cooperation among a small group of pioneers. The results of their efforts were then communicated to a wider group of farmers, with advisory structures and research institutes playing a key role in facilitating collaborations and studying the benefits, costs, and barriers to the practices. The CSs' activities included generating agronomic knowledge through farm trials, farm visits, and study trips, as well as understanding motivations and barriers for establishing win-win partnerships through interviews and surveys. They also disseminated knowledge and promoted success stories through demonstration days, regional meetings, technical guides, publishing articles, videos, etc. Additionally, they engaged in lobby activities, such as meetings with authorities, to remove barriers linked to regulations. Accordingly, action perspectives for crop diversification from this group for use inside and outside the project were through providing innovative practices for agricultural management and animal feeding (e.g., grazing of service crops) or through guidelines for building new contracts, necessary to scale-out the arable-livestock farmers’ cooperation to a greater number of farms (Cabeza-Orcel et al. 2021).

### 3.2.3. Group 3: Developing a national or local community of practice around strip cropping (CS16, CS 22)

Similar to Group 2, this group consisted of CSs that aimed to diversify cropping systems and expand to different geographic regions (Figure 2). However, instead of farmer cooperation, this group focused on promoting the practice of strip cropping among farmers. Despite having different motivations (agronomic and societal for CS16, and economic for CS22), both CSs employed a common strategy of developing a community of practice around strip cropping, which is defined as "a group of people who share a concern or passion for something they do and learn how to do it better as they regularly interact"
(Wenger, 2011). The group comprised a diverse range of participants, all of whom were interested in promoting strip cropping in their professional capacities. Another point of convergence was the research-driven approach of the CSs, with researchers playing a central role in guiding and organizing the CSs' dynamics. The group invested significantly into promoting the strip cropping model and disseminating knowledge through various communication media such as posters, brochures, and pictures. The CSs also worked towards expanding their actor network through meetings and awareness campaigns. In pursuit of their scaling ambitions, the CS teams learned about the challenges of on-farm implementation, including mechanisation and human resource requirements, as well as the management and organization of the community of practice. Beyond providing knowledge on their specific diversification practice, this group fostered action perspectives inside and outside the project by sharing experiences on how to scale-out and facilitate farmer adoption of agroecological practices that were proven at the experimental level to have multiple positive effects (Juventia et al., 2022).

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### 3.2.4. Group 4: Creating niches and removing barriers to support the introduction of novel diversifying crops (CS6, CS7, CS8, CS9, CS10)

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This group gathered CSs that aimed to develop resilient and productive diversified cropping systems as an answer to the issue of variability in yield and farmer income they were facing. The reasons underlying yield variability were diverse (pedoclimatic, agronomic, economic, organizational) but in all CSs diversification was perceived as the most promising solution to allow stable yields and incomes. The common strategy embraced by the CSs was to support the introduction of new crops by concomitantly addressing agronomic issues, developing niches for the commercialization of these new crops or their derived products, and removing technological and regulatory barriers at the value-chain level. This group was unique in that it involved upstream and downstream actors, policymakers, and farmers. The core teams of the CSs, primarily composed of people from research institutes, played a crucial role in facilitating knowledge production and exchanges between farmers, as well as negotiating with actors from the value chain to remove technical and legal barriers. Activities of this group included on-farm and on-station experiments, knowledge sharing during field days and through technical guides, organizing professional days and multi-actor meetings to facilitate discussions among all the actors of the value chain, performing value-chain and market analyses, and developing and testing new crop varieties, processes, and products. Therefore, in terms of action perspectives inside and outside the project, the CSs of this group provided technological innovations (as new varieties or processes) but they also constituted an interesting panel of success stories about how researchers-driven projects can contribute to diversification (Bencze et al., 2020; Colombo et al., 2020).

### 3.2.5. Group 5: Aligning actors in value-chains to enhance grain legume production (CS 17, C20)
This group gathered CSs that were formed to increase the production of various legumes for human and animal consumption, specifically through low-input or organic legume-cereal intercropping. This focus stemmed from the need in several countries for environmentally friendly cropping systems, as well as changes in legislation regarding protein imports. The CSs adopted a strategy of building new value chains based on the positive attributes of legume-cereal intercropping. Like groups 3 and 4, this group consisted of a diverse range of participants, with institutional entrepreneurs (such as a regional collector and distributor for CS17) leading the way by negotiating with upstream and downstream actors to structure value chains and establish fair prices. Research institutes were involved as members of the CS core teams, addressing agronomic issues related to intercropping implementation by farmers. The group engaged in various activities, such as experimenting with innovative crop management and processing techniques, promoting intercropping, forming new partnerships and alliances, organising and facilitating multi-stakeholder events, and lobbying. Such type of CSs contribution to action perspectives inside and outside the project provided learnings on skills or organisational changes (such as contracts) needed to allow concrete development of fair and viable value-chain within the dominant regime (Mamine et al., 2020).

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3.2.6. Group 6: Fostering locally integrated food systems for legumes and vegetables (CS 15, CS18, CS19, CS24)

This group united CSs that aimed to foster sustainable, fair, and local food systems in response to a growing interest from consumers in local, organic and/or vegetarian food. These CSs aimed to meet the current societal demand for more diversified agriculture and diets, which emerged from a collective awareness of the benefits of diversification for both human nutrition and the environment. To achieve this goal, the CSs sought to better connect people within the food system and create food networks (Figure 2). This involved bringing together diverse actors - often non-mainstream - who were linked not only by practical or economic ties but also by shared values and visions. This group involved various stakeholders, including farmers, farmers' associations, small processors, retailers, research institutes, and advisory structures. Consumer participation was central to the overall dynamics of the CSs, as they aimed to change both farmers' and consumers' behaviour simultaneously. To structure the developing food networks, the CSs often relied on established and strong local actors, such as farmers (e.g., CS24) or retailers (e.g., CS18 or CS15). The CSs of this group engaged in various activities to connect with consumers, such as food jams, food tasting, consumer preference surveys, and awareness-raising campaigns through storytelling, TV reports and shows. Stakeholders' events, such as meetings, field days, and study trips, were essential to foster learning, reflection, and stakeholder trust-building. Contributions to fostering action perspectives inside and outside the project could include the sharing of innovative products or industrial processes on one hand and on new methods and tools to build, structure, manage and assess such "food network" on the other.

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### 3.2.7. Group 7: Searching a way out of a wicked situation (CS 1, CS3, CS13, CS25)

This group gathered CSs that shared the objective of creating sustainable, future-proof farming systems as solutions to regional 'wicked' situations characterized by multiple intertwined problems with no clear-cut and sometimes antagonistic solutions. For instance, CS1's ambition was to "break maize monocultures" to jointly address (1) pedoclimatic (e.g., drought due to climate change and soil quality problems), (2) agronomic (e.g., pest and weed problems), (3) organisational (e.g., dependence of dairy farms on imported feed), (4) environmental (e.g., high level of nitrate leaching), (5) economic (e.g., high dependence on imports) issues and answer to the societal demand for more sustainable farming systems as well as the political restrictions around pesticide and nitrogen use. Compared to Groups 2, 3 or 5 that were promoting or supporting specific solutions, the CSs of this group did not have a clear solution in sight to deal with their problematic situation. As a consequence, the shared strategy was to empower and support the stakeholders by exploration of ideas and re-invigorating ways of working to find ways out. The participants were engaged on ad-hoc basis rather than being part of the CS core teams, which were often composed of research or technical institutes or advisory structures. In terms of activities, a lot of time was invested in action plan development with thinking through the problem analysis, reflection, searching for entry points, and finding stakeholders willing to collaborate during collective meetings or design workshops. Concomitantly, there was an ambition of building a knowledge base, for instance through experiments or field visits, for substantiation of different possible directions for change. For these CSs, most of the learnings were about the difficulties they faced to solve their problem and to identify innovative ways of thinking, outside the business of usual. Accordingly, actions perspectives inside and outside the project relied on the identification and the reporting of the lock-ins and the explored solutions (Lefèvre et al., 2020).

## 3.3. Exploring the robustness of the qualitative grouping based on the MFA results

### 3.3.1. Interpretation of the dimensions

Of the total variability 41% was explained with the first four dimensions, with dimensions (Dim) 1 to 4 accounting for 13%, 12%, 8% and 8%, respectively. Groups of variables that contributed to each dimension by more than 30% were: *Participants* for Dim 1, *Participants* and *Levels of Interventions* for Dim 2, *Motivations* for Dim 3 and *Activities* for Dim 4 (data not shown). Considering the contributions of all qualitative variables to the dimensions we concluded that:

- Dim 1 distinguished CSs with societal motivations, working at the food system level and involving consumers and/or downstream actors in the CS team (Figure 3a; on the right) from CSs with pedoclimatic motivations that were not working with research but with technical institutes or farmers associations and involved downstream actors as stakeholders (Figure 3a; on the left);

- Dim 2 distinguished CSs working at hierarchical levels lower than the food system and the value-chain and with political motivations, thus involving public policy makers (Figure 3a; at
455 the top) from the CSs working at the food system or the value chain level (Figure 3a; at the bottom);

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457 - Dim 3 distinguished CSs with political and environmental motivations (Figure 3b; on the right) from CSs without agronomical or environmental motivations and involving rural development associations;

460 - Dim 4 distinguished CSs that dedicated activities to expand geographically and to increase their impact (Figure 3b; at the top) from the CSs motivated by organizational reasons, working at the geographical level, involving upstream actors from the value-chain as stakeholders, that did not dedicate activities to build or develop partnerships or alliances.

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3.3.2. *Comparison of quantitative and qualitative grouping*

466 For some groups (e.g., Groups 4, 5 and 6), the results from the MFA confirmed the similarity of the CSs found in the qualitative analysis, especially when considering the first two dimensions, i.e., when emphasizing the participants and the levels of intervention (Figure 3a). For instance, the MFA results identified CS15, CS18, CS19 and CS24 from Group 6 as close because of their societal motivations, the involvement of consumers, and the interventions at the food system level. Similarly, the MFA confirmed the proximity between CS17 and CS20 (Group 5) and their contribution to Dim 2, thus highlighting their intervention at the value-chain level. For other groups (e.g., Group 1, 2, 7), the MFA partially confirmed the qualitative results. For instance, CS1 and CS3 (from Group 7) were identified as close because they were both sharing political and environmental motivations (Figure 3b). The MFA also revealed major differences between some CSs (e.g., CS 16 and CS 22 from Group 4) that were identified as close with the qualitative approach (Figure 3a, 3b).

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The MFA also demonstrated the proximity between some groups (e.g., Group 1 and 7, Figure 3a) and highlighted formerly unidentified proximities between CSs from various groups. For instance, when considering Dim 1 and 2, CS5, CS8 and CS13 were identified as close because of their pedoclimatic motivations and the fact that they were not research-driven or research-supported (Figure 3a), while when considering Dim 3 and 4, CS7, CS14 and CS17 showed similarities in their willingness to work on scaling-up their results (Figure 3b). Finally, the uniqueness of some of the CSs regarding specific variables was brought out, such as for CS22, which was the only CS working with rural development associations or CS7, which was the only CS working with upstream actors as main actors rather than as stakeholders (Figure 3b).

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<figure><img src="image_5.png" /><figcaption>Figure 3: Projection of individual case studies (CSs) on the first four dimensions of the MFA. (a): Dim 1 and Dim 2; (b): DIM 3 and DIM 4. The CSs are colour-coded based on their group identified in the qualitative analysis: Group 1 = red; Group 2 = blue; Group 3 = green; Group 4 = purple; Group 5 = orange; Group 6 = brown; Group 7 = pink.</figcaption></figure>
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# 4. Discussion and practical implications

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Crop diversification as an innovation niche comprises a diversity of agricultural practices, outcomes, actors, and transition trajectories (Lin, 2011; Revoyron et al., 2022). Drawing on a heuristic framework applied to a panel of 25 CSs, we identified inductively seven different ways of working towards crop diversification that we specified in terms of motivations, participants, levels of interventions, types of activities and learnings. These patterns were consistent with previous studies whether they focussed especially on crop diversification trajectories (Aare et al., 2021; Leclère et al., 2023) or more generally on agroecological transition (Lacombe et al., 2018; Polita and Madureira, 2021). By combining the inductive approach with a statistical approach (MFA), we nuanced the results of the first typology by highlighting other proximities between CSs based on specific characteristics rather than on a strategy. In the following sections we discuss the contribution of the results for reducing task uncertainty and providing action perspectives to actors in agricultural innovation niches, the merits and limitations of the approach, and its contribution to theory development

## 4.1. Informing social learning cycles and reducing task uncertainty linked to crop diversification

### 4.1.1. Within the DiverIMPACTS project

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While most studies have worked on building ex-post typologies to foster action perspectives (McCrory et al., 2022; Mignon and Kanda, 2018), our cross-analysis had the specificity to be performed *in itinere*, halfway through the project, thus allowing to identify how, in practice, it contributed to fostering action perspectives of actors within the project. Crop diversification was the overarching topic within the DiverIMPACTS project, but approaches in the 25 innovation cases were highly distinct. To enable social learning among the CSs as part of the co-innovation approach, identification of common themes, approaches, and experiences was needed. The mid-way self-reflection documents and their analysis (i.e., this study) became an important tool for social learning and for reducing task uncertainty. Individual and collective feedback meetings were organized with the CSs based on the results, complemented by peer-to-peer knowledge exchange sessions within specific groups. The testimony of the CS23 leader after individual feedback highlights the effect on task uncertainty for her: *"As you guided me through the reflection document, the words and graphs came to life. You explained why our case study was ranked or characterized in a certain way and made comparisons with other case studies you scrutinized. The conversation gave me more insight in the different trajectories that we followed for the different problems we set ourselves to work on in the case study. And maybe most important, I got an outlook and inspiration for several options to explore in the last part of this project"* (boldface print by the authors). Combined with the quantitative analysis that contributed to questioning and nuancing the qualitative grouping, we argue to have produced actionable knowledge, i.e., *"knowledge to strengthen the actors in the conduct of their activities"* (Avenier and Schmitt, 2007) and to have fostered action perspectives of the actors in the CSs. Indeed by making explicit the processes, through the heuristic framework, and by comparing and contrasting them, through the cross-analysis, we provided guidance
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to actors in coping with complex and unpredictable situations (Agogué et al., 2014; Fink et al., 2010; Toffolini et al., 2017).

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### 4.1.2. Outside the DiverIMPACTS project

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Like McCrory et al. (2022), our empirically grounded typologies aimed to be comparative and heuristic rather than provide “evaluative judgements regarding the transformative potential or impact”. While the identified groups were contextual and specific for the DiverIMPACTS project, the themes that distinguished the groups inform ultimate comparison across studies of similar typologies to reveal patterns that are relevant to inform practice and middle range theory building. Such middle range theory would provide on the one hand generalizable results across innovation niches, while on the other hand being detailed enough to be actionable. In our case, each group contributed to produce both situated technical knowledge but also more generic knowledge on enablers, constraints, methods and tools to support crop diversification transition pathways. As part of a European research project, CSs were asked to capitalise on this knowledge, thus leading to the production of many open-access resources (such as practical abstracts) that could foster action perspectives and reduce task uncertainties for similar cases. Additionally, this study utilized results of self-reflection material of innovation niches half-way through a 5-year project. More detailed analyses showed that activities of all CSs reflected the stimuli by the project to consider the CS’s vision and mission and to engage in network formation around a common theme. Evaluation of CS learning histories at the end of the project should reveal the extent to which the CSs had extended their activity patterns and broadened their networks and knowledge base to advance to their goals and create a legacy (Bergmann et al., 2021).

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## 4.2. Added-value and limits of the proposed approach to cross-analyse real-world experiences

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### 4.2.1. Combining qualitative and quantitative methods to build typologies

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By deploying both qualitative and quantitative approaches, our ambition was to provide different lenses to question the same dataset and thus contribute to fostering action perspectives (i.e., supporting exploration of new idea by actors). As expected, while the heuristic framework enabled bringing out similarities that constituted productive entry points for social learning in the project, the statistical approach brought to light differences that had been considered less important in the qualitative analysis, as shown by the highly different projections of the two case studies dealing with strip cropping (CS16 and CS22). The qualitative results provided insights on how to scale-out and facilitate the adoption by farmers of the agroecological practices that had been proven at the experimental level to have multiple positive effects. Indeed, by learning from CS16, CS22 managed to overcome regulatory hurdles around CAP subsidies for strip cropping. The statistical analysis emphasized the difference in the involvement of policy makers and the emphasis on scaling out in CS16 compared to CS22, thus prompting reflection on networking strategies by the CSs.
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Based on our experience, a major limitation to this combined approach resides in the collection of good quality data. As part of a self-reporting process, there is a risk to have missing data, which is not so problematic in an inductive approach, but which can make statistical analysis more complex and difficult to interpret.

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### 4.2.2. *Boundaries of the framework*

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The framework we developed aimed at unpacking, within each CS, the way of working towards crop diversification, that was understood as a complex, dynamic, adaptative and multi-actor process. However, questions remain on the role of the embedding project environment (e.g., the co-innovation approach) on the unfolding of the strategies in the innovation niches (Klerkx et al. 2017), especially in order to foster action perspectives of researchers working on crop diversification transition pathways.

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Like has been identified by sustainability scientists for Sustainability Labs, e.g., Bergmann et al. (2021); Kok et al. (2023) creating a space that is at the same time protected from negative influences of the environment and has semi-permeable boundaries enabling instigating transformation, is still to be operationalized. For instance, based on an analysis of the learning histories of three complex case studies in international agricultural research, Hoffecker (2021) proposed a middle range theory based on realist evaluation, comprising a context - mechanism - outcome framework where social learning and social capital contributed to collective cognition and consensus formation, which acted as mechanisms to bring about capacity to innovate and foster inclusive innovation.

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## 5. Conclusion

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To decrease task uncertainty and foster action perspectives within and outside a large European crop diversification project, we grouped innovation cases combining a heuristic, inductive approach and a statistical approach. Both provided information that enabled case study leaders to reflect on their CS's themes and strategies in comparison with those of the other CSs and may be expected to produce actionable knowledge for actors outside the project. For next projects, the *in itinere* approach constitutes a useful instrument to foster social learning. Accumulative evidence of this type may in due course lead to practical abstraction in middle range theories that can support project leaders in guiding transformative change.

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**Acknowledgment** We thank the 25 Case Study teams' dedication to the co-innovation approach in DiverIMPACTS. Many thanks are due to the project coordinators Dr Antoine Messéan of INRAE and Dr Didier Stilmant of CRA-W, and to the workpackage leaders for their continued support of the approach to foster crop diversification.

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**Funding** This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreements No 727482 (DiverIMPACTS).

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<table><caption>Figure 1</caption><thead><tr><th></th><th></th><th>GROUP1</th><th>GROUP2</th><th>GROUP3</th><th>GROUP4</th><th>GROUP5</th><th>GROUP6</th><th>GROUP7</th></tr><tr><th></th><th></th><th>CS5</th><th>CS12</th><th>CS14</th><th>CS23</th><th>CS6</th><th>CS18</th><th>CS1</th></tr></thead><tbody><tr><td rowspan="8">Motivations</td><td>Pedoclimatic</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Agronomical</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Organizational</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Environmental</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Economical</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Societal</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Political</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Farmers</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="7">Participants</td><td>Farmers association</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Rural development association</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Technical institute</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Advisory</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Upstream</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dowstream</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Consumers</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="6">Intervention level</td><td>Research</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Public makers</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>NGO</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Other</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cropping system</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Geographical</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="10">Activities</td><td>Farm</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Value Chain</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Agricultural system</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Food system</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Action plan development</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Generating knowledge</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Partnership/alliances</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Promoting certain model</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Expand geographically</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Direct advocacy/lobby</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Knowledge dissemination</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="7">Learning</td><td>Technical assistance</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Influence public policy</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Packaging/licencing</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Increase quantity and quality of impact</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Specific learning</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lock-in hindering crop diversification</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>External factors</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Internal factors</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Enablers</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CS mission and roles</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Looking forward</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>Scale at which the CS worked
and/or aspired to induce change
(Level of intervention)</figcaption></figure>

Level of diversity in
participant types
involved in the CS
<figure><img src="image_7.png" /><figcaption>Figure 3a</figcaption></figure>
<figure><img src="image_8.png" /></figure>
<table><caption>**Table 1: Description of the 25 change-oriented Case Studies (CSs) of DiverIMPACTS spread across 10 European countries.** At the beginning of the project, CSs were grouped into five clusters spearheaded by a cluster leader to ease knowledge exchanges between CSs as part of the co-innovation approach.</caption><thead><tr><th>CS#</th><th>CS title</th><th>Country</th><th>Cluster</th></tr></thead><tbody><tr><td>1</td><td>Breaking maize monocultures</td><td>Netherlands</td><td></td></tr><tr><td>2</td><td>Herbal or diverse leys use as service crops and valorised through grazing</td><td>UK</td><td>Crop diversification with special attention for service crops</td></tr><tr><td>3</td><td>Crop diversification to improve water quality stability</td><td>Germany</td><td></td></tr><tr><td>4</td><td>Service crops valorised through grazing</td><td>Belgium</td><td></td></tr><tr><td>5</td><td>Introducing crop diversification in maize monoculture</td><td>France</td><td></td></tr><tr><td>6</td><td>Developing organic oilseed production with oilseed rape and hemp</td><td>Switzerland</td><td></td></tr><tr><td>7</td><td>Diversification of cereal-based rotations with soybean</td><td>Hungary</td><td></td></tr><tr><td>8</td><td>Diversification of cereal-based rotations with pulse crops or multispecies service crops under adverse conditions</td><td>Romania</td><td>Crop diversification under adverse conditions</td></tr><tr><td>9</td><td>Diversification of durum wheat cropping systems in a semi-arid environment with sulla clover, hemp and chickpea</td><td>Italy</td><td></td></tr><tr><td>10</td><td>A successful diversification initiative based on the production of soft fruits, vegetables and oil crops</td><td>Poland</td><td></td></tr><tr><td>11</td><td>Co-design of diversified systems in Pays de Loire</td><td>France</td><td></td></tr><tr><td>12</td><td>Sustainable diversification of organic stockless cropping systems: interconnecting organic arable cropping systems and no-till practices</td><td>Belgium</td><td></td></tr><tr><td>13</td><td>Introduction of innovative crops and of legumes to foster the sustainability of arable systems under oceanic climate</td><td>France</td><td>Crop diversification in systems from Western Europe</td></tr><tr><td>14</td><td>Diversification of arable crop rotations, specialised in winter crops, under shallow and stony clay-limestone soils</td><td>France</td><td></td></tr><tr><td>15</td><td>Growing pulses and innovative crops for a less resource-intensive diet</td><td>UK</td><td></td></tr><tr><td>16</td><td>Spatial, temporal and genetic diversification of intensive systems</td><td>Netherlands</td><td></td></tr><tr><td>17</td><td>Grain legumes intercropping with cereals</td><td>Belgium</td><td></td></tr><tr><td>18</td><td>Diversification of organic arable rotations through the production of minor grain crops as mono- or intercrop for local processing toward local human consumption</td><td>Belgium</td><td>Diversification through intercropping with specific focus on grain legumes</td></tr><tr><td>19</td><td>Local legumes – Collaboration within the whole value chain</td><td>Sweden</td><td></td></tr><tr><td>20</td><td>Intercropping for species other than peas and faba beans in association with cereal</td><td>Switzerland</td><td></td></tr><tr><td>21</td><td>Crop diversification through inter-farms co-operation</td><td>Belgium</td><td></td></tr><tr><td>22</td><td>Cereal-vegetable systems for food quality</td><td>Italy</td><td></td></tr><tr><td>23</td><td>Multi-cropping for vegetables in the short supply chain</td><td>Netherlands</td><td>Diversification of vegetable cropping systems</td></tr><tr><td>24</td><td>Crop diversification in protected vegetable systems</td><td>UK</td><td></td></tr><tr><td>25</td><td>Enhancing crop diversification of vegetable food chains in the Roussillon area through joined actions from farms to market</td><td>France</td><td></td></tr></tbody></table>
<table><caption>Table 2: Overview of the heuristic framework developed to unpack the ways of working towards crop diversification in the 25 Case Studies (CSs) of DiverIMPACTS.</caption><thead><tr><th>THEMES</th><th>CATEGORIES</th><th></th></tr></thead><tbody><tr><td>Motivations (reasons for emergence of the CS)</td><td>Pedoclimatic; Environmental; Political; Agronomic; Societal; Organizational; Economic</td><td></td></tr><tr><td>Participants (actors and stakeholders involved in the CS)</td><td>Farmer; Farmers' association; Rural development association; Technical institute; Advisory structure; Upstream actor; Downstream actor; Consumer; Research institute; Policymaker; NGO or foundation; Other</td><td></td></tr><tr><td rowspan="5">Intervention levels (levels at which the CS worked and/or aspired to induce change)</td><td>Cropping system</td><td>When activities were assumed to impact the field in terms of crop management or crop sequence.</td></tr><tr><td>Farm system</td><td>When addressing topics outside the field's perimeter but inside the farm's perimeter.</td></tr><tr><td>Agricultural system</td><td>When the aim was to change how agricultural production systems are organised, for example, in different specialised farming systems, such as animal husbandry, arable farming, dairy farming.</td></tr><tr><td>Geographic region</td><td>When focussing on a specific geographic area (local to national).</td></tr><tr><td>Value-chain</td><td>When working on connecting different parts of the value chain and the actors involved, ranging from raw material production and processing to a final product or service to be sold to the consumer.</td></tr><tr><td rowspan="9">Activities (main types of activities of the CS)</td><td>Food system</td><td>When working on the value chain and the action of "eating".</td></tr><tr><td>Action plan development</td><td>Setting up a strategy or course of action to realize the CS goals.</td></tr><tr><td>Partnerships/alliances</td><td>Organizing organizations or individuals into a network or association mobilized to advocate for shared goals and policies. Collaborating with other organizations to deliver services or address needs in new locations.</td></tr><tr><td>Generating knowledge</td><td>CS-internal knowledge building by accumulating information within the CS network through farm trials, workshops, visits, etc.</td></tr><tr><td>Promoting certain models</td><td>Increasing impact by encouraging the replication of the CS model by promoting the CS and its activities to people outside the CS network.</td></tr><tr><td>Technical assistance</td><td>External knowledge building by providing technical assistance, training, or consulting to people interested in offering similar programs or activities outside the CS network.</td></tr><tr><td>Knowledge dissemination</td><td>External knowledge building by sharing information with people outside the CS network through publications, the internet, or presentations.</td></tr><tr><td>Direct advocacy and lobby</td><td>Engaging public policymakers, legislators, and other government officials to influence the legislative or resource environment for the CS goals.</td></tr><tr><td>Expand geographically</td><td>Increase impact by offering existing CS activities or models in new locations.</td></tr></tbody></table>
<table><tr><td></td><td>Influence public policy (environment)<br/>Packaging/licencing<br/>Increase quality or quantity of impact</td><td>Increase impact by changing public policy.<br/>Packaging a successful product/program and licencing it to existing organizations in other locations.<br/>Increase impact by providing existing CS activities or models more frequently or in greater quantity in current locations or by improving the quality of existing CS activities or models.</td></tr><tr><td><strong>Learnings</strong> (lessons learned in dealing with uncertainties)</td><td>Specific learning about CS topic<br/>Lock-ins hindering diversification in the CS<br/>External factors influencing the CS dynamic<br/>Internal factors influencing the CS dynamic<br/>Enablers to the CS dynamic<br/>CS mission and role<br/>Looking forward</td><td>Learning resulting directly from the activities realised in the CS (e.g., experiments, workshops, consumers study, etc.).<br/>Learning about the barriers to diversification.<br/>Factors not under the control of the CS, belonging to the context in which the CS is embedded. The influence reported can be positive or negative.<br/>Factors inherent to the CS that influenced the general dynamic of the innovation process.<br/>Factors fostering the dynamic between actors of the CS team and enabling progress towards CS goals. The CS presents these enablers as mandatory or necessary for a successful diversification project.<br/>Learning about the specific mission and role of the CS core team (i.e., leader and monitor).<br/>Statements about future activities and the future evolution of the CS dynamic.</td></tr></table>
**Declaration of interests**

[x] The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

[ ] The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: