# On-farm water quality: Co-design of result-based indicators

Perrine Branchet, Olivier Godinot, Nouraya Akkal-Corfini, Matthieu Carof, Christophe Jaeger, Bénédicte Roche, Francoise Vertès

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## To cite this version:

Perrine Branchet, Olivier Godinot, Nouraya Akkal-Corfini, Matthieu Carof, Christophe Jaeger, et al.. On-farm water quality: Co-design of result-based indicators. Agricultural Systems, 2025, 225, pp.104275. 10.1016/j.agsy.2025.104275. hal-04969121

HAL Id: hal-04969121

https://hal.inrae.fr/hal-04969121v1

Submitted on 27 Feb 2025

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# On-farm water quality: Co-design of result-based indicators

Perrine Branchet<sup>a</sup>, Olivier Godinot<sup>a</sup>, Nouraya Akkal-Corfini<sup>a</sup>, Matthieu Carof<sup>a</sup>, Christophe Jaeger<sup>a</sup>, Bénédicte Roche<sup>b</sup>, Françoise Vertès<sup>a</sup>, Virginie Parnaureau<sup>a,*</sup>

## HIGHLIGHTS

* A collective design method was implemented with farmers, technical advisors and researchers to consider on-farm water quality.

* Design outputs are result-based indicators and conceptualization of on-farm water-quality monitoring strategy.

* Despite fixation effects (e.g., focus on means-based indicators and regulation), actors were actively involved.

* This study is an original approach to help mitigate agricultural impacts by acting on the sources of emissions.

## GRAPHICAL ABSTRACT

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

Editor: Paul Crosson

### Keywords:

Participatory research

KCP method

Water quality

Farm scale indicators

## ABSTRACT

**CONTEXT:** Pollution of water resources by substances emitted by agriculture, such as nitrate, pesticides, pharmaceutical residues, fecal microorganisms and microplastics, remains a crucial issue. To assess the effectiveness of pollution-mitigation projects, water quality is usually monitored at the watershed scale. In parallel, farmers and agricultural advisors use mainly means-based indicators to assess farm sustainability. In Brittany, France, the *Terres de Sources* project addresses the following issues: (i) individual farmers cannot assess effects of changes in their practices using result-based water-quality indicators at the watershed outlet and (ii) means-based indicators provide little information about local water quality.

**OBJECTIVE:** The aim of this project was to gather together researchers, farmers and advisors to build operational result-based indicators that would allow farmers to estimate on-farm emissions of pollutants to water. This article highlights the implementation and outputs of a collective design process to create such indicators.

**METHODS:** The Knowledge-Concepts-Proposals design method was implemented to explore ideas around the initial concept of "result-based water-quality indicators at the farm scale". The method's design process has four steps, from initiation to outputs. Emerging ideas of indicators were classified in four categories and we finally selected scientifically relevant and achievable indicators. The methods for measuring these indicators were worked during the final phase of the design process.

**RESULTS AND CONCLUSIONS:** The main results of the design process were (i) a set of result-based indicators focused on nitrate and pesticides and related to chemical measurements and bioindicators, (ii) the development

<footnote><sup>a</sup> INRAE, Institut Agro, SAS, 35000 Rennes, France</footnote>

<footnote><sup>b</sup> INRAE, DSLP, 17450 Saint Laurent de la Prée, France</footnote>

<footnote>* Corresponding author.</footnote>
of phases of “farm characterization” and “on-farm monitoring strategy” to understand water circulation, the relevant “types of water” to sample and suitable on-farm monitoring locations. In addition, breakthrough ideas have emerged but not exploited in this project; they were related to indicators based on senses and on exposure of livestock to pollutants. Despite fixation effects, the group was actively involved in the design process and in the proposal of subsequent prototype testing on farms.

**SIGNIFICANCE:** Most of the indicators selected had already been developed at the watershed scale, but attempting to adapt them to the farm scale was an originality. Farm-scale studies help understand sources of pollutant emissions that decrease water quality. Farmers' use of comprehensive assessment tools would help encourage them to pursue their efforts in agroecological transition.

# 1. Introduction

Contamination of water resources by agricultural emissions remains a crucial issue worldwide and includes substances such as excess inputs of nitrogen and phosphorus (Steffen et al., 2015), pesticides (Sharma et al., 2019), pharmaceutical residues from veterinary substances (Delgado et al., 2023; Wilkinson et al., 2022) and microplastics (Briassoulis, 2023; Persson et al., 2022). Widespread water contamination by these substances raises questions about their impacts on ecosystems and human health (Tong et al., 2022; Silva et al., 2023; Boelee et al., 2019).

To address agricultural water pollution, the United Nations recommends integrated water-resource management, and legal frameworks have been established, such as the European Union (EU) Water Framework Directive. Watershed management plans and mitigation programs have been developed to help waterbodies maintain or reach a good status (e.g., meet environmental regulations at the watershed scale). Regulations impact farmers' practices through action plans, such as the EU's Nitrate Directive, to prevent water pollution from agricultural sources of nitrate, and Pesticide Package, to reach "pesticide use consistent with sustainable development". In addition to regulations, agri-environmental measures of the EU's common agricultural policy are "management agreements that provide compensation payments to farmers for the temporary adoption of input-reducing practices" (Uthes and Matzdorf, 2013). Ravier et al. (2015) described agri-environmental measures as "action-oriented measures" because they implement farm practices without assessing their effects on water quality. Moreover, their effectiveness is debated among actors partly because of issues related to their multiple and vague objectives, spatial scale, performance indicators, and the lag time between their implementation and beneficial effects (Ackermann et al., 2016; Uthes and Matzdorf, 2013). They have been called "a complex planning and management problem" (Uthes and Matzdorf, 2013). In comparison, "result-oriented measures" (Ravier et al., 2015) based on the requirement to provide good water-quality status would be more effective. However, this would change the scale of the approach, since effects of changes in farm practices on water quality would be assessed at the watershed outlet, which makes it more difficult to relate farm practices to water quality. Such water-quality projects represent complex local challenges that concern multiple actors and assume a long-term engagement, as discussed by Nesheim et al. (2021).

In parallel, a wide panel of indicators intended for various end-users exists to assess water quality (Latruffe et al., 2016; de Olde et al., 2017). An indicator is a qualitative or quantitative variable that summarizes reality, which is complex and difficult to measure (Bockstaller and Girardin, 2003). Several types of indicators have been developed (Latruffe et al., 2016), from relatively simple cause indicators to more complex indicators based on operational models that consider environmental factors, with varying accuracy (Pierlot et al., 2017; Buczko and Kuchenbuch, 2010). At the farm scale, indicators can be placed on a causal chain that relates agricultural practices to impacts, which helps determine what kinds of indicators are appropriate to address water quality (van der Werf and Petit, 2002). van der Werf and Petit (2002) distinguish "means-based" indicators, which refer to farmers'

production practices, from "effect-based" indicators, which assess the effects of farm practices on environmental emissions of pollutants and impacts on ecosystems. Many indicators of on-farm water quality of various degrees of complexity have been developed. Most of them are means-based indicators used to characterize farmers' practices (Schröder et al., 2003; CORPEN, 2003, 2006; Girardin et al., 2000; Zahm et al., 2019). Other means-based indicators describe the environmental fate of pollutants from farming systems to water (Reus et al., 2002; Le Bellec et al., 2015; Lindahl and Bockstaller, 2012; Pierlot et al., 2023; Servien et al., 2014; Heckrath et al., 2008; Trodahl et al., 2017; Buczko and Kuchenbuch, 2010). These indicators are commonly used to evaluate the effectiveness of policies, due to their relatively simple access on a large number of farms (Thorburn et al., 2022). However, they are less relevant to estimate environmental impacts than effect-based indicators, which are more difficult to implement (Lebacq et al., 2012). Some effect-based indicators address impacts on on-farm biodiversity (Williams et al., 2004; Jabiol et al., 2021), such as macroinvertebrate communities (Verdonschot et al., 2012; Hill et al., 2016) and pesticide ecotoxicity (Padovani et al., 2004).

According to the literature, except for measurements of residual soil nitrogen before winter that indicate potential nitrate leaching (Vandenberghe and Delesalle, 2022; Rasouli et al., 2014; CORPEN, 2006), no other effect-based indicator seems to be directly related to water quality at the farm scale (hereafter, "result-based water-quality indicators"). While impacts are likely to be observed at the watershed scale, it is interesting to know how much a given farm contributes to the total pollutant load at the outlet. Moreover, there is a lag time between management practices and measurable improvement in water quality (Juan et al., 2018; Meals et al., 2010; Dupas et al., 2020). Thus, on-farm result-based water-quality indicators would allow farmers to assess impacts of their production system by assessing the short-term quality of the water that leaves their farm. These local and short-term indicators should be more reactive to practice changes than indicators at the watershed scale, and could therefore help address the change in spatio-temporal scale between changes in farm practices and water quality. They might thus be more likely to modify farmers' behavior.

The design of indicators is recognized as being more effective if it is performed collectively and with active participation of the different stakeholders and end-users (Cerf et al., 2012; Prost et al., 2012). Co-design is even more important when it involves actors from different professions and fields of expertise, especially for agricultural and environmental issues (Lacombe et al., 2018; Jeuffroy et al., 2022; Le Gal et al., 2011). Innovative design methods that require mobilizing a group have been developed to redesign farming systems and support agro-ecological transition (Meynard et al., 2012; Jeuffroy et al., 2022; Meynard et al., 2023). In such studies, the involvement of actors from different professions and fields of expertise, in particular farmers, can improve creativity during the design process, benefit from general scientific and local knowledge about farming systems and farmers' situations, and enable a sustainable redesign of farming systems (Lacombe et al., 2018; Jeuffroy et al., 2022; Le Gal et al., 2011). Among co-design methods, the Knowledge-Concepts-Proposals (KCP©) method, developed in industrial design and based on design theories (Hatchuel and Weil, 2009), has been applied in agricultural and environmental
contexts for a variety of purposes. Briefly, the KCP method defines a framework in which participants can “think outside the box” to imagine new objects with new properties. It was developed for the first time in the agricultural sector by Berthet (2013) and then for different purposes, such as to redesign the management and breeding of a local dairy sheep breed (Labatut and Hooge, 2016), develop an innovative method based on a different paradigm to renew nitrogen fertilization (Ravier et al., 2018), regenerate cultivated biodiversity (Berthet et al., 2020), identify combinations of agricultural practices that reduce water pollution by herbicides (Della Rossa et al., 2022) or promote local cattle production that maintains or improves the functioning of wet grasslands (Berthet et al., 2022). Other design methods have been mobilised in agriculture. Among them, the 4-step DEED approach (Giller et al., 2011), for example applied to the re-designing of organic grain legume cropping systems (Reckling et al., 2020) is interesting to gather different types of knowledge but they seem to be more adapted when the object to design is not so far from the existing system. Participatory design method based on serious games already crystallises a vision of the object to be designed in their model (e.g. Martin et al., 2011, Berthet et al., 2015), while the KCP method focuses more intrinsically and methodically on creativity and is built to generate new concepts. Finally, on-farm result-based water-quality indicators represent new objects to imagine and are thus suited to this approach.

We hypothesized that co-designing result-based water-quality in-
dicators at the farm scale using the KCP method to gather together re-
searchers, technical advisors, water managers and farmers could create
new sets of operational indicators to be measured on the farms. This
research was conducted in the context of Terres de Sources, a project led
by Eau du Bassin Rennais, a local authority for water production and
supply (Brittany, France). The Terres de Sources project aims to
economically support local farmers involved in agricultural production
methods that are defined as "more respectful of water resources". Here,
we describe how we co-designed new indicators, what kind of result-

based indicators were produced and the other outputs of this partici-
patory research for certain watersheds in Brittany.

## 2. Materials and methods

2.1. KCP design method

The KCP method aims for disruptive innovations and provides tools to stimulate creativity and to encourage collective action (Elmquist and Segrestin, 2009). To do so it consists in gathering a group, in which members have skills on fields of knowledge identified and related to the new objects to design. It incites this group to overcome (i) “fixation effects” (cognitive biases that limit the design of new objects and cause individuals to focus on obvious or pre-existing solutions (Agogué et al., 2014)) that can hamper creativity and (ii) the fact that radical changes may threaten its cohesion.

The KCP method is implemented throughout three types of work-
shops (detailed below). Participants in these workshops share a common
issue, which is formulated as a proposal, partly unknown but imaginable
(i.e., the “initial concept”) (Berthet et al., 2020). Three iterative phases
(K, C and P) of workshops are necessary and are conducted by facilita-
tors using appropriate methods and tools. To connect concepts and
knowledge, a C—K diagram is used to follow and formalize C and K
spaces and to identify relevant spotlight concepts (Fig. S1).

We adjusted the KCP method according to our initial concept,
involved participants and the available time period to lead the design
process. Since the project had constraints related to its duration, the
availability of actors involved in the workshops and the desire to have
operational results by the end of the workshops, we held the workshops
over a short period of six months. Thereafter we detail the steps of our
adapted method in chronological order (Fig. 1). The four steps of the
design process we implemented range from initiation to outputs which
consist in: (i) Step 1 consists of creating the scientific coordination team,

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Diagram of the Knowledge-Concepts-Proposals (KCP) design process implemented during the participatory research project. The four steps (bold black font) are (i) initiation of the design process, (ii) preparation of the K, C and P workshops and the initial C—K diagram, (iii) holding of the K, C and P workshops and management of the C—K diagram and (iv) management of design process output. The main objectives of each step are summarized in rectangles and numbered. Bold blue font indicates the adjustments we made to the steps of the reference KCP method described by Jeuffroy et al. (2022), or Della Rossa et al. (2022). Connections with the result sections are provided to improve the understanding of the paper. Italics indicate the actors involved in each step. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>
defining the initial concept and inviting actors to join the research project. (ii) Step 2 consists of preparing the workshops and drawing the initial exploratory C—K diagram. (iii) Step 3 consists of holding the workshops, modifying the C—K diagram and exploring concepts. (iv) Finally, step 4 consists of summarizing indicator ideas and making proposals for the future. The following sections describe specific points of the design process.

## 2.2. Steps of the design process

### 2.2.1. Design process initiation

The coordination team consisted of three researchers in environmental science (specifically, agronomy and environmental chemistry) whose role was to implement, organize and lead the entire design process. The coordination team was involved in all choices and discussion of results during the entire process, and one member of the team also served as facilitator of all of the workshops. The coordination team first defined the initial concept, on the base that in the fields of agriculture and water quality, the “dominant indicators” consists of either (i) result-based water quality indicators that are not designed with or for farmers, that assess the watershed scale or waterways and that focus on ecosystem services or (ii) means-based indicators based on agricultural practices and devoted to farmers. Thus, the initial concept submitted to the participants was expressed as “result-based water-quality indicators at the farm scale”. “Result-based” refers to direct or indirect impacts on water quality due to agricultural emissions to the environment, unlike means-based indicators. Combining “result-based indicators” with “at the farm scale” was an attempt to go beyond the “dominant indicators” by proposing to farmers that they could monitor the effects of their own practices on water quality.

Then, three groups of actors with different roles were invited (Fig. 1, d) and involved in the design process: local farmers, technical advisors and scientific experts (engineers and researchers from a research organisation), hereafter named as co-designers. Each actor was invited because of its skills and interests in water quality resource stakes. A large panel of local farmers was invited with a diversity of farming systems and environmental considerations, but only a small group of them actually joined the design process. Targeted end-users in this study are mainly farmers and eventually their technical advisers. Table S1 gives detail about the participants and their role.

Co-designers intervened during steps 3 and 4 of the design process

(Fig. 1, 3a-c and step 4). Farmers actively participated in the workshops and outputs of the design process. Special attention was paid to their comments to better understand their viewpoints and constraints, and how they perceive water quality and environmental issues, in order to keep them involved in the project. They were recruited either by telephone (more than 50 calls or text messages), based on lists provided by agricultural advisors involved in water-quality projects, or by public meetings organized by the coordination team (15 participants in total). Ultimately, 11 farmers from four sub-watersheds of the Rance and Couesnon Rivers participated in the workshops. They were already aware of water-quality issues, and some were involved in local water-quality projects to reduce pesticide use or measures to promote biodiversity. One farmer was already involved in the *Terres de Sources* project. Five agricultural advisors and water managers (hereafter, “technical advisors”) also participated in the design workshops. All were involved in local water-quality projects with farmers and had skills in agricultural landscape management, crop management and environmental chemistry. Finally, 26 scientific experts and technical advisors were involved as co-designers, more or less punctually, from the agronomic fields of crop production, animal production, landscape management, farming-system assessment, hydrobiology, botany, hydrology and design science.

Scientific experts participated in the workshops, the preparation steps before (Fig. 1, 2b) and between the workshops and, for the technical experts among them, the output step of the design process (Fig. 1, 4b). The coordination team relied on them for the main fields of knowledge used during the design process, and some of them presented specific fields of knowledge during workshops (Fig. 1, 3a and b).

### 2.2.2. Workshop preparation

The coordination team built an initial exploratory C—K diagram (Fig. 2). The development of the C—K diagram is done by adding or subtracting new properties to sets of concepts (Hatchuel and Weil, 2003). This operation leads to so-called “partitions” and permit to reach new concepts linked to fields of knowledge. When a gap of knowledge appears the partition is qualified as an “extension” and thus requires new knowledge search. The first distinguished indicators “not designed with farmers” from those “designed with farmers”. The former belong to the “dominant indicators” and include pollutant monitoring in water, pollutant modeling and interpretation of pollutant modeling predictions, which are related to the fields of agri-environmental indicators, environmental chemistry, hydrology and hydrogeology and the

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Initial C—K diagram proposed by the coordination team. Known (gray), achievable (light gray) or breakthrough (black) concepts are related to knowledge (by field, capital letters) that is validated (gray), in the course of acquisition (light gray) or a knowledge gap (black).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 3. The three steps used to select indicators.</figcaption></figure>

environmental fate of pollutants. Since the project focused on designing result-based indicators with farmers, indicators “designed with farmers” were classified as either “calculated and/or measured” (which seemed the most achievable) or “not calculated or measured” (e.g., based on senses, such as visual assessment of waterbodies), which can be related to the fields of sensory analysis and hydrobiology. Then, the “calculated and/or measured” indicators were classified as either “using means-based indicators based on pre-existing on-farm data” or “using new data instead of means-based indicators”, the latter of which was further classified as “using a measuring device” or “not using a measuring device”.

The coordination team listed ideas of potential indicators on the initial C—K diagram but did not show them to the participants of the K and C workshops in order to not influence co-designers and especially

end-users (farmers, technical advisors). The coordination team used the initial C—K diagram as a reference to monitor the design process by adding new concepts. The comparison between the initial C—K diagram and the final C—K diagram after exploration made by the participants during K and C workshops gives the main results of the design process.

### 2.2.3. Holding the workshops

To implement the design process, the K, C and P workshops were organized and facilitated by the coordination team over a six-month period in 2021–2022 (Fig. 1, 3a-3c). During the K and C workshops, we intentionally separated the group of farmers from the group of advisors and researchers in order to focus workshop contents on local water-quality issues of the watersheds. Separating participants in groups that share overall the same standards is suggested to reduce disparities in speaking of the various actors and allow farmers to express themselves freely about their practices, constraints and viewpoints (Darré, 2006).

### 2.3. K workshops

K is a phase of knowledge. In order to ensure that the participants share a common base of knowledge, experts make presentations on topics related to the initial concept (state of the art, existing knowledge gaps, ongoing work). The aim is to stimulate participants’ creativity during workshops C, in bringing new knowledge to participants, to help them to generate ideas from their own knowledge/expertise and to encourage dialogue between participants.

Three K workshops were organized in parallel: two with one group of farmers each (by location) and one with advisors and researchers. The K workshops began with presentation of the participants, the research project, project objectives and the initial concept. Then, depending on the group, we organized crossed presentations to share scientific and farmers’ knowledge, followed by question-and-answer sessions, including “Transfers of nutrients and contaminants at the watershed scale” and “Water-quality issues at the territory scale”. A shared knowledge session on “Effects of production practices on water quality at the farm scale” used paperboard as a medium to collect ideas from each participant. The coordination team used initial ideas expressed during the K workshops to modify the C—K diagram and to build spotlight concepts.

### 2.4. C workshops

C is a phase of concept generation that aims to explore innovations and identify new knowledge gaps (different from those presented during the K phase). Phase C is a directed-creativity approach performed in small subgroups. Facilitators propose “spotlight concepts”, which describe design objectives in the form of a short expression, in the form of an oxymoron or play on words. “Spotlight concepts” are sufficiently open to guide exploration and stimulate creativity, for example, “a hardy and advanced breed” (Labatut and Hooge, 2016), “disseminating knowledge rather than seeds” (Berthet et al., 2020) and “a traditional cow of tomorrow” (Berthet et al., 2022).

Approximately one month later after K workshops, three C workshops were organized with the same group composition as the K workshops. Before the C workshops, the coordination team, with the help of the scientific experts, developed several spotlight concepts based on the ideas mentioned in the K workshops, making sure to cover the entire C—K diagram (Fig. 2). The aim of the C workshops was to explore these spotlight concepts. The coordination team stimulated exploration of disruptive ideas by creating “mood boards” (i.e., a mosaic of pictures chosen to evoke feelings and ideas and to facilitate participants exploration; Berthet et al., 2020) for each spotlight concept before the workshop and showing them to the participants during the C workshop.
## 2.5. Indicators' selection for prototyping

At the end of the K and C workshops, ideas of result-based indicators needed to be classified and selected (Fig. 3). First, the coordination team classified the ideas in order to distinguish ideas of indicators from ideas of their expected properties. Then ideas of indicators were classified into four groups: (i) scientifically relevant and achievable indicators, (ii) operational indicators with little relation to water quality, (iii) breakthrough ideas and (iv) out-of-scope ideas. The final sorting of this selection was based on end-user's (farmers and agricultural advisors) practical expectations during and after workshops.

## 2.6. P workshop

P is a phase of proposals that aims to develop a roadmap to continue the innovation process (e.g., test a prototype). During phase P, subgroups develop concrete projects based on the concepts explored in phase C. Tools proposed by subgroups are the project sheet (e.g., objective; means, skills and knowledge required; actors involved) and the project roadmap, which develops an action plan to implement the project. The aim of the final P workshop implemented in this study was to focus the discussion on the way to measure the previously selected indicators on farms: the most relevant location of the measurements on the farm and the monitoring strategy. The P workshop gathered all participants. To stimulate connections between them we constituted subgroups by mixing farmers, technical advisors, scientific experts and members of the coordination team. A summary of the results of the K and C workshops was presented, and subgroups were asked to think about the implementation of selected sets of indicators on the farms. Some new scientific experts were present to enrich the discussion according to each set of indicators, if necessary.

# 3. Results

## 3.1. Result-based water-quality indicators

### 3.1.1. Final C—K diagram

During the K and C workshops, participants came up with many ideas of indicators, (both examples of indicators from which to draw inspiration and precise ideas of indicators), their expected properties, configurations and measurement conditions. The coordination team recorded and classified each idea, and then organized it in the evolving C—K diagram (Fig. 4).

First, based on the K workshop, the coordination team proposed the spotlight concept “sentinel indicators”, which could alert farmers to a disturbance (e.g., a sudden change in water color in a farm ditch) before a more accurate assessment. “Sentinel indicators” included the ideas of using the senses, identifying sensitive species, organizing alert levels into a hierarchy and delineating a specific area that receives mainly farm emissions and could serve as a “sentinel area” to monitor regularly. The main outputs of discussion were related to “alert indicators”, the need to measure indicators at the most relevant sampling locations on a farm and the use of sensitive species (e.g., macroinvertebrates) to monitor water quality. In the C—K diagram, the concept “not designed with farmers” was not deepened, and the distinction between indicators “calculated and/or measured” vs. “not calculated or measured” was moved to the top level. Indeed the coordination team deliberately oriented the exploration toward indicators designed with end-users (farmers, technical advisers) to push the participants to not fix their exploration on already known indicators. For the “calculated and/or measured” indicators, the sub-concept “using data produced on-farm” was associated with the spotlight concept “the farm as an indicator”, which it proposed to help participants think outside the box about the traditional use of means-based indicators, such as the treatment frequency index (TFI) and nitrogen balance (i.e., nitrogen inputs minus nitrogen outputs of a farm). The idea was to “transform” these means-based indicators into result-based indicators by adding new data or properties. This spotlight concept provided the opportunity to imagine a

<figure><img src="image_7.png" /><figcaption>**Fig. 4.** The C space of the final C—K diagram resulting from KCP workshops with farmers, technical advisors and researchers. Known (gray), achievable (light gray) or breakthrough (black) concepts are related to knowledge (by field, capital letters) that is validated (gray), in the course of acquisition (light gray) or a knowledge gap (black). Sun icons identify spotlight concepts. Italics indicate sets of indicators.</figcaption></figure>
new function of the farm: as an observatory of environmental quality. The concept “using data produced on-farm” had three sub-concepts: “using means-based indicators”, “economic indicators” and “using new data”. The “using means-based indicators” sub-concept included the large number of indicators based on farm practices, combined with information more strongly related to water quality, as the properties of active ingredients (e.g., toxicity, environmental fate), as proposed by several farmers.

The sub-concept “using new data” distinguished indicators “using a measuring device” from those “not using a measuring device”. The former included “in situ measurement” (e.g., pocket nitrate meter), “grab sampling” (e.g., analyzing the nitrate concentration of water samples using colorimetry in the laboratory), and “passive sampling” (e.g., adsorption of pesticides on a sorbent over a given period and analyzing them in the laboratory) using knowledge in environmental and analytical chemistry. Ideas were then proposed by farmers and technical advisors but also by researchers in the field of environmental chemistry and agronomy. For the indicators obtained “using a measuring device”, the coordination team proposed the spotlight concept “living indicators” to explore ideas about the “health status of the aquatic environment”, the community of organisms living on-farm and how observing them could provide information about water quality. It involved knowledge in the fields of hydrobiology, ecotoxicity and bioindicators. The ideas were then proposed by technical advisors and researchers in the field of hydrobiology and hydrology. The ideas generated led the participants to classify the indicators as “bioindicators inherent to the farm” (i.e., using livestock and other organisms living on-farm as indicators) or “bioindicators introduced onto the farm” (i.e., placing monitoring devices that contain living organisms at different locations on the farm).

To explore the “not calculated or measured” indicators, the coordination team proposed the spotlight concept “to feel water quality”, which encouraged farmers to imagine indicators based on on-farm observations that were easily measurable and based on appropriate references. It generated ideas of indicators based on senses, perception and social interaction. For example, some participants suggested that water pollution could be identified by smell. These types of indicators used knowledge in the fields of sensory analysis, hydrobiology and environmental chemistry.

### 3.1.2. Outputs of the selection of indicators

At the end of the K workshops, the participants had decided to focus on the effects of pesticides and nitrate on water quality. At the end of the C workshops, 38 ideas of result-based indicators were obtained (Table S2) and were sorted as described in Fig. 4. The initial sorting sets aside expected properties, configurations and measurement conditions and “alert” indicators that contributed later to the on-farm monitoring (section 3.2).

Ideas of indicators that had only an indirect relation to water quality were not considered appropriate for testing prototypes because of the difficulty in interpreting their results when considering farm-pollutant emissions. This group included indicators based on soil quality, such as “earthworms” or “soil biodegradation activity”, which are environmental indicators whose scientific knowledge has only an indirect relation with water quality.

Breakthrough ideas included the concepts with the most potential for breakthrough, while out-of-scope ideas reflected discussions parallel to the initial concept that addressed other types of issues, such as farmer exposure to pesticides. Breakthrough ideas identified in this research correspond with sets of concepts in the scope of the study, linked to indicators “not calculated and/or measured” and whose related fields of knowledge are “knowledge gaps” or “in the course of acquisition”. The most breakthrough ideas were indicators based on senses and social perception, related to the spotlight concept “to feel water quality”. The related fields of knowledge included sensory analysis, environmental chemistry and hydrobiology. Sensory analysis is defined as “examining the properties...of a product...through the senses”, which “has been

used for centuries for the purpose of accepting or rejecting food products” (Ruiz-Capillas and Herrero, 2021). Examples mentioned during the workshops were related to the color or odor of on-farm water (in ditches and streams) and visual aspects of soil erosion or intercropping. Another breakthrough idea was the degree of farmer implication in a water-quality project. While exploring the spotlight concept “living indicators”, participants proposed deepening the relation between pollutant exposure and the health and/or behavior of livestock. The idea was to biomonitor livestock exposure to agricultural micropollutants since livestock live continuously on a farm. This idea was related to current studies of human exposure to water pollutants through water consumption, skin contact and breathing.

Finally, only the indicators considered relatively easy to implement and to measure on-farm, and that that could be tested during a field campaign in the winter of 2022–2023 with the farmers involved, were selected to be compared to end-users’ practical expectations. For example, farmers suggested easily measurable indicators in the form of a decision tree (“a set of indicators with scores to give a good idea of what we’ve done for the environment”, with “a variety of assessment criteria to improve assessment of our impact: pesticides, nitrogen, hedges, pits, ditches”, farmers during the C workshops) and the importance of timing and location. During the P workshop, participants shared additional criteria expected for the indicators, such as the importance of providing results quickly to allow farmers to react quickly and potentially adapt their practices (“The faster the measurement, the faster we can set things up”), a farmer during the P workshop), consideration of the environmental fate of substances, the need to be supported by experts to benefit from their skills and an external vision (“I don’t think we have the skills to do it all ourselves.”, a farmer during the P workshop) and general screening of pollutants over time rather than precise measurements. Opinions differed on the use of technology; for example, the idea of a smartphone alert that rings when the soil is tilled parallel with the slope was considered to be oppressive. Measuring indicators might not be a constraint but instead allow for self-assessment if group training in the use of the tool is provided.

The next part of the paper focuses on selected result-based indicators as the aim of this study is to favor operational indicators (i.e. scientifically relevant and achievable).

### 3.1.3. Description of the indicators selected

Consequently, the indicators selected were mainly tools and techniques already developed and validated for streams, waterways and soil water (Table 1). In the C—K diagram, they belonged to the “calculated and/or measured” indicators “using data produced on farm” and “using new data” (Fig. 4). In the P workshop and during discussion with scientific experts, ideas related to chemical measurements and bioindicators were deepened, including consideration of how to implement them on a farm. The first group of indicators, “using a measuring device”, relied on approaches to measure targeted substances (e.g., nitrate, pesticides) related to environmental and analytical chemistry fields. The methods considered were not high-tech or too expensive (e.g., autosamplers for subsequent chemical analysis of water). Complementary methods with *in situ*, grab and passive measurements were proposed to improve the representativeness of the results. For example, to assess nitrate dynamics at the field scale during the drainage period in Brittany, participants proposed installing anion-exchange membranes in the topsoil of fields (to monitor nitrate flows) and measuring nitrate concentrations regularly at drainage-system outlets. Participants proposed using multi-pesticide samplers (e.g., polar organic chemical integrative samplers) and grab sampling of on-farm water (e.g., streams, ditches, wells) to monitor pesticide occurrence, concentration and dynamics along with collecting information about treatment periods and the types of substances applied. The second group of indicators, “not using a measuring device”, which was inspired by the spotlight concept “living indicators”, explored on-farm biodiversity. Passive and active bio-monitoring were chosen because they provide an integrated
<table><caption>**Table 1**

Result-based indicators selected to be tested on-farm that resulted from the Knowledge-Concepts-Proposals design process.</caption><thead><tr><th>Type</th><th>Parameters to be measured</th><th>Result-based indicators to be tested</th><th>On-farm measurement locations</th><th>References</th></tr></thead><tbody><tr><td rowspan="3">Integrated indicators</td><td>Nitrate flows in water of the topsoil</td><td>Anion-exchange membranes</td><td>Field soil water</td><td>Qian and Schoenau, 2002; Shaw and DeForest, 2013</td></tr><tr><td>Herbicide and fungicide emissions to water</td><td>Integrative pesticide sampler</td><td>Ditches, drainage-system outlets, streams, wells</td><td>Alvarez et al., 2004</td></tr><tr><td>Impact of overall toxicity on living organisms</td><td>Index based on macroinvertebrates in ditches</td><td>Ditches and streams</td><td>Verdonschot et al., 2012; Verdonschot and Verdonschot, 2014; Hill et al., 2016; Leslie and Lamp, 2017</td></tr><tr><td rowspan="3">Grab indicators</td><td>Impact of overall toxicity on living organisms</td><td>Caged <i>Gammarus fossarum</i></td><td>Ditches, drainage-system outlets, streams</td><td>Besse et al., 2013</td></tr><tr><td>Nitrate that could be leached toward groundwater during the drainage period</td><td>Residual soil nitrate</td><td>Field soil water</td><td>Prost et al., 2018; Deltares, 2023</td></tr><tr><td></td><td><i>In situ</i> nitrate measurement using a smartphone application (Nitrate App) and/or portable nitrate analyzer</td><td>Drainage-system outlets</td><td></td></tr><tr><td></td><td>Herbicide and fungicide emissions to water</td><td>Measuring nitrate in the laboratory</td><td></td><td></td></tr><tr><td></td><td></td><td>Pesticide grab sampling and laboratory analysis</td><td>Ditches, drainage-system outlets, streams, wells</td><td></td></tr></tbody></table>

measurement over time, and they can be sensitive to micropollutants such as herbicides and to the trophic status of waterbodies. Discussion led participants to focus mainly on macroinvertebrates and to choose to use the caged bioindicator *Gammarus fossarum* and designing a macro-invertebrate index in agricultural ditches. Indicators based on flora or periphyton were also mentioned but not investigated due to the lack of an available expert in this field.

A third group, “using means-based indicators”, consisted of new combinations of means-based indicators using on-farm data already provided by agricultural activities (e.g., factors that determine residual soil nitrate before winter), but it was not discussed during the project due to a lack of time.

## 3.2. How to measure indicators on the farm?

Several elements helped develop the steps of “farm characterization” to identify relevant locations of the measurements and “on-farm monitoring strategy” during the workshops. They were closely related to original outputs of the design process and were necessary to choose and develop result-based indicators suitable for farms.

### 3.2.1. Farm characterization

Farmers proposed the “farm characterization” step, which consists of identifying elements of on-farm water circulation, connections to the hydrographic network and fields with high risks of transferring pollutants to waterways. It was first necessary to identify suitable monitoring sites on the farm. The farmers’ involvement in this step was a result in itself toward improving water quality because it required farmers to identify factors that influenced water quality on their farm and increased their understanding of transfer processes. During the P workshop, subgroups worked on an approach to characterize farms based on available scientific resources and knowledge of farmers and technical advisors. The characterization also requires identifying when water is present on the farm and water dynamics (e.g., drainage periods). Certain factors determine the characteristics of a water-quality monitoring strategy, such as a farm’s environmental context (e.g., soil properties), agricultural practices and configuration (e.g., obstacles to surface water flow) and farm location in the watershed (e.g., connection of farm ditches to a wider network of ditches) (Fig. S2). On-farm water circulation and “types of water” can be understood by combining a farm’s environmental context, agricultural practices and configuration. For example, the presence and volume of drainage in a ditch is influenced, among other things, by rainfall intensity and ditch management (e.g., water supplied by a field’s drainage-system outlet). Thus, water in drainage ditches can represent the water that circulates “under the crop”

and can contain substances such as nitrate and pesticides. In addition, participants proposed identifying “alert indicators” during the characterization step (i.e., visual and/or olfactory signs that indicate a potential disturbance, such as a change in waterway color, leaching from manure or connections between fields and waterways along furrows or through field entrances) (Table S3 and Fig. S3). Farmers also expressed the idea of indicators that would indicate short-term pollution (e.g., a change in turbidity), and some of them requested “quick-result tools”.

### 3.2.2. On-farm water quality monitoring strategy

During the P workshop, participants discussed the scale and location of indicator measurements and compared monitoring using indicators specific to each farm vs a “standard” set indicators, which would ease comparisons among farms. They also discussed the location of measurements in terms of the receiving environment, sensitive areas and/or “sentinel” areas under the direct influence of farm emissions that could act as an “alert”. Throughout the design process, they often mentioned physical characteristics or configurations specific to their own farm, because water quality depends on a farm’s environmental context, agricultural practices and configuration (e.g., “Each territory is different: a variety of constraints and thus a variety of solutions to obtain a result”, a farmer during the C workshop).

Since the tools selected assessed hydrographic networks, on-farm “water types” were listed: water in agricultural ditches, waterways and their sources; runoff from farmyards and paths; ponds; wells and soil water. Importance was given to the representativeness of the sampled water, which should not be contaminated by water from neighboring activities. To support this approach, participants developed a simplified representation of the farm to identify relevant monitoring sites along with characteristics (e.g., source, pollutant concentrations) of the water sampled and “alert indicators” (Fig. S3). They decided to consider specific characteristics of each farm to select relevant monitoring locations and sampling strategies.

For each indicator to be tested, the potential degree of end-user (i.e., farmer) involvement was discussed and placed on a diagram (Fig. S4), from minimal involvement (i.e., providing access to the farm to researchers to sample water) to full autonomy (e.g., water monitoring using a portable nitrate analyzer). Finally, prototype testing for the project has started through farm visits and work with each farmer: drawing a map of suitable sampling locations and diagrams of drainage periods, as well as defining sampling campaigns, farmers’ interventions on ditches and fertilizer and pesticide applications.
# 4. Discussion

## 4.1. Design process method

### 4.1.1. A collaborative participatory research

This research project can be described as “collaborative participatory research” (Haklay, 2013) or a “co-innovation” approach (Lacombe et al., 2018). Indeed the actors in the project helped define the methods, experimental protocols and data collection, production, analysis and interpretation, while the coordination team of researchers was involved in the project design and funding plan, definition of the research question and coordination (Shirk et al., 2012). Nonetheless, one major difficulty at the beginning of the project was to inform farmers about it and to encourage them to join the design process. Participants were able to combine their knowledge to choose suitable indicators to measure in the field (e.g. index based on macroinvertebrates in ditches and caged Gammarus fossarum, see Table 1) but were unable to explore more innovative indicators (e.g. invention of a technical device related to sensory analysis), which they could have done had participants from other fields of knowledge been involved. In addition, participants needed time to understand the initial concept. In general, much time was spent explaining ideas (e.g., scientific, technical, organizational) from the fields of knowledge of each group of participants and the opinions of the subjects that were addressed. Although these moments were essential, researchers and farmers had difficulty talking on an equal footing (Robichaud and Schwimmer, 2020). According to previous studies of the design process, the description and sharing of initial concepts are crucial steps. Setting a “common goal” from the beginning with the actors involved would have helped avoid misunderstanding and identify the solutions or ideas relevant and suited to the collective (Darré, 2006).

### 4.1.2. Design process implementation

The KCP method used in our study allowed us to explore numerous ideas and generate collectively various types of indicators; most of them were not intrinsically new, not so “outside the box”. Two main differences from the other studies in which the KCP method was applied lies on the one hand in the need to reach operational indicators in a limited time, and on another hand in the relatively restricted initial concept.

To find compromise between the relatively short time for the KCP workshops, the limited availability of farmers and an initial concrete proposal to test prototypes with end-users in the time of the research project, the design process was simplified as regards to other studies (e.g. Berthet, 2013): performed in six months without iterations between phases K and C. Consequently, the most original ideas have yet to be deepened. As another consequence, we added a step by the coordination team, between workshop C and workshop P in order to select indicators that would be developed in the proposal step and subsequently tested in farms. On the contrary, as performed in previous study, some steps were not performed in our study because the type of object we wanted to design was not of the same nature as those covered by the studies reported by for example Della Rossa et al. (2022), that needed to begin by a diagnosis of the socio-technical system. Our choice to start with an initial concept previously defined has undoubtedly limited what is possible, but has enabled us to develop an indicator that is ready to be tested.

Indeed, the initial concept designed by the coordination team had been delimited and may be was not a “desirable unknown” but the KCP method avoided to stay in the “dominant indicators”. This initial concept did not change greatly during the design process, but its scale did (i.e., from the “farm scale” to “on-farm”), because it was recognized that the water-sampling locations identified could not reflect pollutant emissions of the entire farm. The design process did not produce new knowledge (Toffolini et al., 2020) but instead an original combination of knowledge from researchers’ and farmers’ fields of knowledge to reach operational outputs. This result differs from those of other design

studies, which produced many ideas (Berthet et al., 2022) and opened many innovative approaches in the design process but whose potential for implementation remains uncertain or less focused on design-process outputs for farmers (Lacombe et al., 2018). In addition to thinking about and selecting result-based indicators adapted to a farm, the “types of water” and their representativeness were original outputs that could not be displayed in the final C—K diagram. In addition, the design process results in building a dashboard with each farmer and could evolve in the future to help farmers change their agricultural practices (Prost et al., 2018; Toffolini et al., 2016).

Fixation effects can be avoided or limited using several methods when preparing and holding the design workshops, as summarized by Jeuffroy et al. (2022). Although we identified them and tried to avoid them in our preparation of the workshops, especially with “mood boards” or by encouraging the participants to think of new properties, the main fixation effects were related to the focus on means-based indicators and regulation constraints. It was illustrated by what farmers said during feedback after workshops: “We had problems thinking outside the regulations: we have already done a lot, and we don’t know what else we can do to optimize.” and “It’s not easy to tell the difference between practices and indicators.” Another fixation effect was due to scientists themselves, as most created indicators were linked to scientists’ expertise. The lack of diverse participants and of disruptive knowledge sharing may have contributed to the difficulty in overcoming these limits. A power asymmetry between farmers and scientists might also contribute to explain this bias (Fielke et al., 2022).

## 4.2. Design-process outputs

### 4.2.1. Selected result-based indicators

The originality, advantages and limits of each result-based indicator selected to be tested varied (Table 2). Overall, their main originality lies in the fact that those existing indicators were considered on farms as they are usually measured in natural waters and disconnected from the agricultural actors. They were complementary indicators that combined integrated and grab measurements of either targeted substances (e.g., nitrate or pesticides) or the integrated effects of water quality on living aquatic organisms. The two main limits were the scale of measurement and the need for water to be present for at least a short period. Since the research objective was to identify suitable indicators at the farm scale, monitoring emissions from the entire farming system does not seem realistic since it would require installing a large amount of measurement equipment. Thus, working at smaller scales, such as fields or pollution hotspots (e.g., drainage-system outlets), constitutes an initial approach that has relatively interpretable results. Comparing them to existing indicators such as soil mineral nitrogen before winter (Vandenberghe and Delesalle, 2022), we find the same limitations in terms of scale, as we cannot carry out this measurement on all the plots on the farms. However, measuring in water rather than in soil is closer to our objective of result-based indicators, and we enrich the panel of pollutants considered with this type of indicator.

### 4.2.2. From breakthrough ideas to research avenues

New approaches identified in the final C—K diagram were related to acquiring new on-farm data using chemical and biological measurements and to innovative ideas based on livestock sensitivity to environmental pollutants and on the use of senses. However, livestock sensitivity to environmental pollutants through water consumption, especially pollutant effects on livestock health and behavior, remain knowledge gaps, for both individual substances and their combinations. Participants also proposed the concepts of considering the farm as a biodiversity hotspot or a system that provides high-quality water, but an explicit relation between pollutants and farm biodiversity or water quality was not identified. In addition, to our knowledge, there is no result-based water-quality indicator based on senses. Organoleptic properties (i.e., stimuli related to senses) do exist that indicate
<table><caption>**Table 2**
Originality, advantages and limits of selected result-based indicators from the design process to be tested in the field.</caption><thead><tr><th>Type</th><th>Result-based indicator selected</th><th>Originality and advantages</th><th>Limits</th></tr></thead><tbody><tr><td rowspan="10">Integrated indicators</td><td>Anion-exchange membranes</td><td>Adaptation from crop nitrate uptake to nitrate-flow monitoring<br>Integrated measure over time</td><td>Single spatial measurement<br>Time-consuming<br>Requires laboratory analysis</td></tr><tr><td>Integrative pesticide samplers</td><td>Inexpensive<br>Adaptation from water-resource monitoring to on-farm sampling sites<br>Integrated measure over time<br>Relatively easy to implement<br>Combines results of integrated sampling with biomonitoring<br>Complements grab-sampling water monitoring</td><td>Does not provide a concentration in water (i.e., semi-quantitative)<br>Requires continuous water presence and flow over the sampling period<br>Requires laboratory analysis<br>Expensive (e.g., materials, analysis)</td></tr><tr><td>Index based on benthic macroinvertebrates in ditches</td><td>Adaptation from stream monitoring to on-farm sampling sites<br>Overview of water-pollutant impacts on organisms living on-farm<br>Integrated measure over time<br>Results can be acquired by identifying organisms to family<br>End-users can be trained</td><td>Requires a short period of water presence<br>Sampling and identifying organisms are time-consuming<br>Expensive if done by experts (ca. 150€/sample)<br>Influence of factors besides organic pollutants on benthic macroinvertebrates (e.g., climate)</td></tr><tr><td>Caged <i>Gammarus fossarum</i></td><td>Adaptation from water-resource monitoring to on-farm sampling sites<br>Complements pesticide water sampling<br>Relatively easy to implement</td><td>Requires continuous water presence and flow over the sampling period<br>Requires monitoring the device for 7 days<br>Influence of factors besides organic pollutants on benthic macroinvertebrates (e.g., climate)</td></tr><tr><td>Residual soil nitrate</td><td>Teaching device: at the end of the exposure period, one can see how many individuals are alive or dead<br>Combines several field nitrate measurements to ensure representativeness of the results, and the most suitable indicators among these methods can be chosen</td><td>Overview of nitrate only at the field scale<br>Need for a drained field to measure concentrations in water</td></tr><tr><td><i>In situ</i> nitrate measurements using a smartphone application (Nitrate App) and/or a portable nitrate analyzer</td><td>Farmers can be involved in measurement to different degrees<br>Easy to implement</td><td></td></tr><tr><td>Measuring nitrate in the laboratory</td><td>Relatively easy to implement</td><td>Requires water to be present</td></tr><tr><td>Pesticide grab sampling</td><td>Complements integrated water monitoring<br>Provides pesticide concentrations in water</td><td>Expensive analysis<br>Provides a single result in time, which lacks representativeness</td></tr></tbody></table>

disturbance of the aquatic environment. For example, white foam on water indicates an excess of organic matter but not its source (e.g., phosphate from humus decomposition). Thus, such indicators represent mainly observed situations and seem difficult to use as indicators with a suitable frame of reference. However, combining this sensitivity-based approach with relevant sensory analysis references used in food processing may be a breakthrough idea to explore.

After proposal of the spotlight concept “sentinel indicators”, the main outputs of discussion were related to “alert indicators”, the need to measure indicators at the most relevant sampling locations on a farm and the use of sensitive species (e.g., macroinvertebrates) to monitor water quality. Farmers also expressed the idea of indicators that would indicate short-term pollution (e.g., a change in turbidity), and some of them requested “quick-result tools” (section 3.1.2). We did not consider that communicating sensors giving real-time results were compatible with our low-cost objective. However, this approach reducing the burden of monitoring might have been interesting to experiment, as it gives encouraging results in other regions (Fielke et al., 2022).

### 4.2.3. Involvement of end-users during the design process

It was difficult to know exactly why the farmers volunteered to become involved in the research project, besides its local context and certain remarks of farmers during the K and C workshops: “If we are here, it’s because we’re aware of this [water quality] issue”, “We have no choice [but to change agricultural practices] for everyone’s health”, “We’re tired of working on nitrate! [...] We’re always searching to understand”. Exchanges during workshops, especially exploration of the spotlight concept “to feel water quality”, showed that, for some farmers, agricultural streams “represent a hazard” (farmer during the C workshop). Farmers reported that they regularly observed stream color, appearance, plants, algae and water levels. Thus, some “alert indicators” identified were already used to assess the entire farm and could support

additional measurements of result-based indicators.

Several studies describe psychological factors that lead farmers to become involved in water-management projects, especially the desire to belong to a collective (McGuire et al., 2013; Upadhaya and Arbuckle, 2021) and relations between their ways of thinking and practices (Vuillot et al., 2016). Since we aimed to collect farmers’ viewpoints, the workshop design gave farmers the time to express their views of related topics, including environmental regulations, technical constraints and perceived misunderstandings of society. The feedback “We’re not used to this approach; usually no one asks for farmers’ viewpoints” (a farmer after the K workshop) leads us to think that their workshop experiences were meaningful (i.e., they could be heard and help set objectives) (Nesheim et al., 2021).

## 5. Conclusion and perspectives

This participatory research project allowed participants to think collectively about on-farm water quality and aimed to help mitigate agricultural impacts from their sources of emission. The KCP method allowed participants to produce ideas about result-based water-quality indicators that helped them understand pollutant emissions directly from the farm to water. Outputs of the design process included sets of measuring tools to test in the field. Participants proposed measuring pesticides and nitrate in waterways and soil water using both grab and passive sampling to ensure representativeness of the results. Using the caged bioindicator *Gammarus fossarum* and designing a macro-invertebrate index in agricultural ditches would supplement chemical measurements. These tools, already developed for monitoring water at the watershed scale, are innovative in how they can be applied at the farm scale. However, they do not fully meet our goal of providing simple, inexpensive and reliable results-based water quality indicators at the farm level. However, creating new sets of result-based indicators
could increase the sensitivity of more farmers to water-quality issues and their ability to control their own impacts Other important results of the design process are the steps of “farm characterization” and “on-farm water monitoring strategy”, which help identify relevant “types of water” and sampling locations that are not usually considered. The co-ordination team’s focus on exploring operational tools and the lack of participants with knowledge outside of agronomy and water quality did not favor the emergence of more breakthrough or innovative ideas. As mentioned, the main perspective of this study is to test selected indicators during sampling campaigns on the seven farms still involved in the project. Since the breakthrough part of the C—K diagram was not investigated, a longer research project, complementary workshops and improvement of the facilitation to push the participants to overcome fixation effects and to be more creative would help the group to be more innovative. A future workshop with group discussion of the results of the indicators would be necessary to see how farmers react to their results and how the results influence their practices. Broadening this approach to the watershed scale and involving all local farmers would build a common concern about local water quality, which seems like a responsible and unifying perspective. In terms of research perspectives, we are now interested in exploring the potential of flora or periphyton to be used as indicators by linking their presence to pollutants. Finally, some breakthrough ideas could be deepened to evaluate their potential to generate relevant knowledge, so as the use of the sensory analysis as practical environmental indicators.

## Ethics statement

Written and verbal informed consent was obtained from all anony-
mized participants to publish their information in this article.

## CRediT authorship contribution statement

**Perrine Branchet:** Writing – review & editing, Writing – original draft, Validation, Resources, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Olivier Godinot:** Writing – review & editing, Supervision, Project administration, Methodology, Funding acquisition, Conceptualization. **Nouraya Akkal-Corfini:** Writing – review & editing, Conceptualization. **Matthieu Carof:** Writing – review & editing, Writing – original draft, Conceptualization. **Christophe Jaeger:** Writing – review & editing, Formal analysis, Conceptualization. **Bénédicte Roche:** Writing – review & editing, Writing – original draft, Formal analysis, Conceptualization. **Françoise Vertes:** Writing – review & editing. **Virginie Parnaude:** Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization.

## Declaration of competing interest

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.

## Acknowledgments

We would like to thank Eau du Bassin Rennais, the local authority responsible for water production and supply (Rennes, Brittany, France) with its project "Terres de Sources", and the French government and its partner the Banque des territoires as part of its program "France 2030", for their funding. We warmly acknowledge all those actively involved in this research project, including farmers, advisors, water managers and all researchers of the research unit Sol, Agro et hydrosystèmes, Spatialisation (INRAE/Institut Agro, Rennes, France) and the experimental station of Saint-Laurent-de-la-Prée (INRAE/Institut Agro, France). We thank the anonymous reviewers who helped improve the quality of the manuscript and Michael Corson for proofreading its English.

# Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.agsy.2025.104275.

## Data availability

Data will be made available on request.

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