Farmers' perceived financial and non-financial costs of their biodiversity measures – Exploring viewpoints with Q-methodology

Farmers’ perceived financial and non-financial costs of their biodiversity measures – Exploring viewpoints with Q-methodology Verena Scherfranz a , Henning Schaak a , Jochen Kantelhardt a , Karl Reimand a , Michael Braito b , Flaviu V. Bodea c , Cristina Costache c , R˘ azvan Popa c , Reinier de Vries d , David Kleijn d , Aki Kadulin e , Indrek Melts e , Amelia S.C. Hood f , Simon G Potts f , Lena Schaller a,* a BOKU University, Institute of Agricultural and Forestry Economics, Feistmantelstraße 4, 1180 Wien, Austria b BOKU University, Institute of Sustainable Economic Development, Feistmantelstraße 4, 1180 Wien, Austria c Universitatea Babeș-Bolyai, Faculty of Biology and Geology, Str. Clinicilor 5-7, Cluj-Napoca 400006, Romania d Wageningen University & Research, Plant Ecology and Nature Conservation Group, Droevendaalsesteeg 3a, 6708 PB Wageningen, the Netherlands e Estonian University of Life Sciences, Institute of Agricultural and Environmental Sciences, Fr.R. Kreutzwaldi 5, 51006 Tartu, Estonia f Centre for Agri-Environmental Research (CAER), School of Agriculture, Policy and Development, Reading University, RG6 6AR, United Kingdom ARTICLE INFO Keywords: Financial costs Non-financial costs Q-Methodology Farmer’s perceptions Agri-environmental programs Biodiversity measures ABSTRACT Farmers’ willingness to continue participation in their agri-environmental program and maintain biodiversity measures in the long term is shaped by the nature of costs they perceive during implementation. Research emphasizes the need to account for both financial and non-financial costs, but holistic assessments which both put these costs into relation and account for farmers’ varied perceptions remain lacking. To capture the plurality of perceived costs, as well as the plurality of viewpoints farmers have of these costs, we applied Q-methodology across four European study areas. Building upon scientific literature and expert interviews, we defined a Q-set comprising 41 cost aspects from four dimensions, i.e. financial, management-related, emotional and social costs. 34 farmers with different socio-demographic and farming background Q-sorted these cost aspects. Elicited viewpoints showed that participating farmers are either most impacted by perceived governance-related uncertainty, unproductiveness, lack of support, administrative burden, underpayment, or social non-conformity. Findings give indications of highly diverse needs when implementing a biodiversity measure, within and across study areas. The systematic insights into farmers’ cost perceptions and the structure established for this Qstudy can guide research and policymakers who aim to comprehensively explore and evaluate well-targeted ways to improve farmers’ experiences of biodiversity measures within agri-environmental programs. 1. Introduction While numerous agri-environmental programs incentivize farmers’ implementation of conservation measures across Europe, their contribution in reducing nature degradation is being questioned (e.g.: Pe’er et al., 2022). Given that several environmental benefits accrue over longer time scales, a key way to improve ecological effectiveness is seen in ensuring farmers’ decision to continue participation in agrienvironmental programs and maintain their conservation measures (Defrancesco et al., 2018). An extensive body of researchers provided insights into the multi-dimensional determinants, i.e. drivers and barriers for making such agri-environmental decisions (e.g. Knowler and Bradshaw, 2007; Dessart et al., 2019; Prokopy et al., 2019; Klebl et al., 2024; Schaub et al., 2023; Sander et al., 2024; Schulze et al., 2024a). Among those, negative experiences with agri-environmental programs and conservation measures were observed as standing against farmers’ willingness for continuing participation and maintenance (e.g.: Selinske et al., 2015; Fienitz, 2018; Ranjan et al., 2019; ˇ Sumrada et al., 2021). Yet, to better understand such negative experiences and allow policy and program designers to make adaptations supporting continuation, a detailed understanding of which disbenefits, i.e. “costs” farmers perceive regarding the governance, implementation and management of their conservation measures, is needed. Most commonly, costs of agri-environmental programs and measure implementation are associated with the financial dimension, i.e. loss of economic welfare due to management costs, opportunity costs, and * Corresponding author. E-mail address: [email protected] (L. Schaller). Contents lists available at ScienceDirect Ecological Economics journal homepage: www.elsevier.com/locate/ecolecon https://doi.org/10.1016/j.ecolecon.2025.108694 Received 28 May 2024; Received in revised form 8 May 2025; Accepted 23 May 2025 Ecological Economics 236 (2025) 108694 Available online 5 June 2025 0921-8009/© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). certain transaction costs (e.g.: Ranjan et al., 2019; Tyllianakis and Martin-Ortega, 2021): Farmers need to cover expenditures for setting up and maintaining conservation measures, discard potentially more lucrative business opportunities on committed land, or spend money and time on contracting, learning or monitoring (e.g.: Knowler and Bradshaw, 2007; Mettepenningen et al., 2009; Coggan et al., 2022; Schaub et al., 2023). To outweigh the loss of economic welfare, agrienvironmental programs commonly provide financial compensation, as, for example, the European Union’s (EU) Common Agricultural Policy and its agri-environment-climate payments (Article 28 of Regulation (EU) No 1305/2013). Yet, research increasingly draws attention to potential drawbacks which farmers perceive beyond this neoclassic economic rationale of welfare losses (e.g.: Burton, 2004; Burton et al., 2008; Mettepenningen et al., 2009; Caldas et al., 2016; Selinske et al., 2016; Dessart et al., 2019; Tyllianakis and Martin-Ortega, 2021). Applications of established concepts from other disciplines have contributed to a deeper understanding of such “non-financial costs”. Currently prominent in political discussion (Matthews, 2024), this includes, inter alia, the so-called administrative burden. While specifying the transaction costs associated with policy administration (El Benni et al., 2021), which for the example of cross-compliance direct payments in Switzerland are estimated to amount to 5% of the entire budget, this concept goes beyond the spending of time and money. Defined as “an individual’s experience of policy implementation as onerous” (Burden et al., 2012: 741), the administrative burden accounts not only for learning and compliance efforts, but also psychological costs resulting from policy interactions (Moynihan et al., 2015). Building thereupon, Ritzel et al. (2020: 12), for example, assessed how perceived loss of autonomy and increased levels of stress due to administrative obligations impact Swiss farmers’ experiences with agri-environmental programs, and found them as relevant as “rational factors”, such as documentary duties per se. Additional non-financial costs arising from conservation measure implementation can be derived from Bourdieu’s capital theory. To properly reflect the real-world system, Bourdieu (1986: 15) advocated for considering “capital in all its forms and not solely in the one form recognized by economic theory”. In this sense, loss of cultural capital and social capital among the agricultural community, through loss of prestige or loss of trust, might arise whenever conservation measures do not form part of “conventional ‘good farming’ practices” (Burton and Paragahawewa, 2011: 95). Empirically applying Bourdieu’s capital theory, Burton et al. (2008), for example, investigated how management restrictions and extensification requirements hinder farmers to generate productivist symbols, like “tidy” fields signaling “good farming” in the production-oriented cultures of their German and UK study areas. While Mettepenningen et al. (2009) argue that compensation payments shall compensate for both the monetary and such non-monetary costs, Burton et al. (2008: 21) reason that even if agri-environmental payments are “apparently generous”, such barriers to cultural capital generation can leave farmers with an overall net loss (also see Cusworth, 2020). Additional insights into the financial and non-financial dimensions in which farmers perceive negative impacts from measure implementation are to be gleaned from empirical research going beyond conceptual lenses. Particularly re-enrollment research, providing informed assessments of farmers already implementing conservation measures, can inform at which (perceived) costs implementation might come and might have the potential of deterring from continuation. For example, in a study by Reimer and Prokopy (2014), investigating US farmers’ participation in diverse conservation programs, decisions to not re-enroll were found to result both from expected opportunity costs, with productive use of the land becoming more beneficial, as well as from workload, bureaucratic program requirements and perceived restrictions in autonomy. Beyond scholarly publications dealing with reenrollment, given they are scarce (Defrancesco et al., 2018), Barnes et al. (2019) and Vaske et al. (2021) provided extensive reports on US conservation programs and long-term participation, showing that dropout decisions are associated with perceived disbenefits in several dimensions. Again, financial costs were observed as important reason for farmers not wishing to re-enroll. However, reasons also comprised perceived negative impacts onto the management dimension, comprising limitations in land use, locally unsuitable requirements, administrative burden from complex requirements and risk for pest or fire damage, as well as perceived negative impacts on individual’s wellbeing, such as too much governmental influence on the property and reduced aesthetics or recreational opportunities. Besides re-enrollment research, insights into potentially relevant cost dimensions can be derived from general assessments of agri-environmental programs as, for example, Lim and Wachenheim (2022), reporting farmers’ (dis-) satisfaction in several financial and non-financial dimensions, or Eichhorn et al. (2020), examining diverse European agri-environmental programs. Evaluating innovative contract solutions for biodiversity protection, their ex-post SWOT analyses not only revealed negative financial impacts associated with program participation, such as expenses for nature protection certification and increased competition among farmers with similar environmentally friendly business models. The report also draws attention to disbenefits in the legal sphere, with the measure’s requirements potentially coming at cost of other contractual obligations on the farm (Eichhorn et al., 2020). Summing up, both conceptual and empirical research indicate the need to account for perceived disbenefits, i.e. costs in not only financial, but also non-financial terms to avoid negative experiences with agrienvironmental programs and sustainably anchor conservation measure implementation in farming. Yet, research systematically synthesizing the varied financial and non-financial costs which farmers perceive in the course of measure implementation and investigating them in their entirety is, to the authors’ best knowledge, limited. While there is research increasing awareness for the diversity of financial or practical burdens, with for example Schaub et al. (2023: 617) extensively investigating into “farmers’ forgone utility when choosing to participate” or Coggan et al. (2022) outlining farmers’ varied transaction costs, existing studies do not account for the full variety of costs, leaving aside aspects such as psychological onerosities or loss of non-economic capital as outlined above. At the same time, empirical studies so far have missed to examine the diverse perceptions farmers have of these varied costs. This is surprising given the broad empirical evidence on the heterogenous farmers’ types and viewpoints even within European regions, which might be worth to be reflected in more diverse policy mechanisms to increase their efficiency and effectiveness (Bartkowski et al., 2022). Therefore, this study is guided by the aim to both gain a more comprehensive understanding of the plurality of costs, through synthesizing multi-dimensional burdens, as well as exploring the plurality of perceptions thereof. Hereby, it focuses on agri-environmental measures specifically targeting biodiversity conservation, in the following denoted as “biodiversity measures”. This responds to their limited success so far (European Union, 2020), mirrored in an unfavorable status or trends of species and habitats across the EU (European Environment Agency, 2020), and the sustained efforts needed for protecting them (e. g., Race and Curtis, 2009; Reimer et al., 2014; Drechsler et al., 2017), emphasizing the necessity for continued implementation. Accordingly, we seek to answer the following research question: Which different viewpoints do farmers hold about the diverse financial and non-financial costs of their biodiversity measures, as part of agri-environmental programs? To this end, this study applies Q-methodology, an exploratory mixedmethods approach allowing to “reliably, scientifically and experimentally” assess people’s subjectivity and, subsequently, elicit shared viewpoints (Watts and Stenner, 2005, 2012: 44). Building on an extensive set-up process to comprehensively capture the multidimensionality of costs, we conducted interviews with farmers across four substantially different European study areas to elicit potentially varied perceptions of these costs. This study makes three major contributions: First, it contributes to a more comprehensive understanding of potential negative experiences, i.e. perceived costs as equivalent, multi-dimensional counterpart to V. Scherfranz et al. Ecological Economics 236 (2025) 108694 2 perceived benefits, also going far beyond the financial dimension. It thus complements research on the various drivers and barriers of farmers’ agri-environmental behavior through providing new, synthesized insights into one such determinant, i.e. negative experiences farmers make with current implementation. Second, it captures diverse viewpoints on these multi-dimensional costs, informing a more comprehensive definition of support tools and program adjustments targeted at farmers’ varied needs to mitigate potentially negative experiences with biodiversity measures and thus ensure continued implementation. Third, while this study does not aim to directly draw conclusions on continuation, the synthesis of financial and non-financial costs feeding the Qmethodological approach as well as the identified viewpoints provide systematic foundations to fuel the scarce, yet important field of continuation research (Race and Curtis, 2009; Reimer et al., 2014; Defrancesco et al., 2018; Gatto et al., 2019; Ranjan et al., 2019) in both qualitative and quantitative terms. The remainder of this paper is structured as follows: In Section 2, we detail the Q-methodological approach, including data collection and analysis. The elicited viewpoints, building on statistical results and narrative-style descriptions, are presented in Section 3. In Section 4, we discuss overall perception trends, the elicited viewpoints and their implications for policy-making, alongside the limitations of this study. Lastly, conclusions are drawn in Section 5. 2. Methods In this study, Q-methodology explores shared viewpoints on financial and non-financial costs of measure implementation to better understand the multi-faceted burdens which different groups within the farming community experience and, in a next step, to allow for welltargeted policy responses. Q-methodology is a technique widely used in socio-environmental sciences (Sneegas et al., 2021). Recent applications of Q-methodology relating to agriculture and food production comprise the assessment of viewpoints on food labels (Schulze et al., 2024b), agri-environmental contract design (Schulze and Matzdorf, 2023), food system sustainability (R¨ o¨ os et al., 2023), and advisory systems (Chowdhury and Kabir, 2023). This application of Q-methodology was guided by Watts and Stenner (2005, 2012) and, as common, comprised two major steps (also see Dieteren et al., 2023) which are briefly outlined as follows: In the first step (data collection), the Q-set, i.e. items to sort such as statements or pictures on the research topic, is defined. The Q-set needs to be “broadly representative” of the so-called concourse (Watts and Stenner, 2012: 67), which is the every-day communication about the research topic (Brown, 1993). The size of the Q-set should lie within the “house standard” of 40 to 80 items (Watts and Stenner, 2012: 67). Subsequently, participants, i.e. the P-set, are asked to sort these items relative to each other according to their perceived importance, agreement, preference or the like. For insightful Q-studies, the P-set needs to be relatively small, deemed sufficient with even less than 40 participants (Watts and Stenner, 2005) and rarely exceeding 50 (Brown, 1993). Given the rationale of Q-methodology and the subsequent analysis with participants’ Q-sorts serving as the variables (see below), the P-set is sampled strategically according to the research question (Watts and Stenner, 2012). 1 Importantly, the P-set does not need to be representative of the population, but diverse to ensure that as many potential viewpoints as possible are captured (Watts and Stenner, 2005, 2012). Through sorting, participants transfer numerous items into their individual gestalt configurations, the so-called Q-sorts (Watts and Stenner, 2012). To ensure comparability between Q-sorts, sorting usually follows a forced distribution. This means that items cannot be assigned freely, but participants are asked to all sort into the same-shaped grid. 2 In the second step (data analysis), Q-sorts of all participants are subjected to a joint by-person factor analysis which extracts so-called “factors”, grouping farmers with similar Q-sorts and thus indicating shared viewpoints (Watts and Stenner, 2005, 2012). The number of factors to keep from factor analysis is guided both by qualitative considerations, such as a factors’ real-life significance, and quantitative criteria (Brown, 1980; Watts and Stenner, 2012). Subsequently, factor arrays, i.e. “best-estimate Q-sorts” representing the viewpoints, are generated for each factor (Watts and Stenner, 2005: 82). To facilitate the interpretation of these viewpoints, the quantitative Q-sorting is typically accompanied and/or followed by qualitative interviews, during which participants explain the reasoning behind their ranking (Watts and Stenner, 2005; Watts and Stenner, 2012). 2.1. Q-set The Q-set in this study comprises the multi-faceted perceptions farmers might state about costs of their biodiversity measure. The Q-set sampling is described in Fig. 1. To capture the diversity of potentially perceived costs, this process was unstructured, i.e. not guided by predefined theories (Watts and Stenner, 2012). Concourse identification Fig. 1. Process of Q-set sampling. 1 Q-methodology applies factor analysis, which is preferably run with less variables than observations. Given that factor analysis is run by-person (inverted) with participants’ Q-sorts serving as variables and Q-items serving as observations, the P-set therefore is preferably smaller than the Q-set (Watts and Stenner, 2005; Webler et al., 2009; Watts and Stenner, 2012). Besides, small samples shall ensure focus on “essential qualities” and “subtle nuances” in the data (Watts and Stenner, 2005: 79; 2012). 2 It is important to note that sorting is relative. Therefore, the middle of the grid does not necessarily separate items which are deemed, e.g., most important or most agreed with from those which are deemed most unimportant or most disagreed with (Watts and Stenner, 2012). V. Scherfranz et al. Ecological Economics 236 (2025) 108694 3 (1) was based on peer-reviewed publications, project and institutional documents, reporting about disadvantages which farmers encounter within their agri-environmental programs, or which they consider when deciding on further participation. To this end, literature search was focused on non-financial cost concepts as well as re-enrollment literature. Key words used to identify peer-reviewed literature comprised “non-financial cost*”, “non-economic cost*”, “non-monetary cost*”, “reenroll*”, “continu*”, “post program”, “post contract”, “contract end” and “end of contract”, combined with the term “agri-environment*”. Snowballing was applied until no new insights could be gained, i.e. saturation. Project and institutional, i.e. government-related documents comprised project evaluations resulting from the literature search outlined above, deliverables from preceding EU projects on the social impact of ecological farming approaches for farmers (LIFT 3 ) and on impacts of innovative agri-environmental contract solutions (CONSOLE 4 ), as well as an evaluation of agri-environmental payments by the European Union (2011). The literature search was conducted from February to June 2023. Expert meetings with an agricultural economist and a representative of a European farmers’ association were conducted to cross-check for missing topics. Overall, 116 cost aspects, i.e. potential disbenefits encountered during the implementation of agri-environmental programs, were identified. Since publications mostly did not report them as direct citations from farmers, or reports were too specific on single agrienvironmental practices, the conceptualizing authors generated short statements in easy-read and plain language from the identified cost aspects and abstracted them as much as needed to fit all investigated biodiversity measures and study areas (for practical checks see below). Inductive clustering of the identified cost aspects elicited four “cost dimensions”: Financial costs relate to financial loss and uncertainty. Management-related costs deal with administrative and physical impediments. Emotional costs comprise impacts on values, preferences and well-being. Social costs regard adverse effects in farmers’ social environment. While elicited cost aspects on Bourdieu’s (1986) concept of social and cultural capital could be assigned to only one dimension (“social costs”), elicited cost aspects from the concepts of transaction costs and administrative burden were assigned to several dimensions, given they are multi-faceted in themselves and touch upon diverse aspects, e.g. information seeking coming with burden on time or money or fear of penalties leading to emotional stress. In the pre-selection (2), statements were cleared from redundancies and adjusted for similar levels of abstraction. To validate and complement the remaining 66 statements in terms of practical and local relevance (3), we conducted individual online interviews with seven further experts (three advisors, three agro-economists, one farmers’ representative) who are familiar with the agricultural context of the respective study areas and biodiversity measures (see Section 2.3). Building thereupon, the conceptualizing co-authors defined the quasi-final selection of 41 statements. For fine-tuning (4), the quasi-final Q-set was pre-tested by farmers (n=3) with different production systems, resulting in minor changes in wording and the replacement of two statements due to perceived redundancies. 5 Subsequently, we discussed the wording among the entire team of co-authors to ensure unambiguous translatability. The final Q-set is shown in Table 4, while Appendix A details the main sources of each statement. 2.2. P-set Given the scope of this study aiming to more comprehensively understand the plurality of costs perceived in the course of implementation, the P-set comprises exclusively farmers who are already implementing the investigated biodiversity measures as part of an agrienvironmental program (see Section 2.3). Recruiting was conducted within the networks of local research partners from academic research institutions with agro-ecological focus and non-governmental agrienvironmental organizations providing advisory services for farmers. To ensure that, in case viewpoints vary among the farming community, they are captured in their full plurality, we aimed for farmers with varied socio-economic and farm characteristics, especially gender, age, education, farm type, farm size, and farm management. The final P-set is presented in Section 3.1. 2.3. Study areas and their biodiversity measures Study areas are located in Estonia, the Netherlands, Romania, and the United Kingdom (Fig. 2). Each area faces substantial threats to biodiversity which originate from land use change and are being addressed by public or private programs, incentivizing the implementation of corresponding biodiversity measures. Table 1 gives an overview. Details of the agricultural context and program administration are provided in Appendix B. 2.4. Q-sorting The Q-sorting was carried out in individual face-to-face interviews. Interviews took place in autumn 2023 and lasted between 0.5 and 2 hours. Interviews were conducted by native-speaking research partners (for Estonian, Romanian, and Dutch) 6 or the first author (for English or German). The first author additionally assisted each interview to ensure uniform data collection. We used farmers’ native language, except if explicitly preferred otherwise. 7 Prior to sorting, the Q-set was carefully translated and printed onto 41 cards. Following Watts and Stenner (2012), farmers first familiarized themselves with the statements through assigning the cards to three piles (disagree, agree, neutral/undecided). Based on this rough classification, farmers sorted the statements relative to each other on a scale Fig. 2. Location of study areas across Europe. 3 https://www.lift-h2020.eu/ 4 https://console-project.eu/ 5 These statements asked about emotional distress resulting from administrative tasks and farmers’ perception of deviating from what their peers do, recommend or prioritize, which appeared too close to statements on the overall amount of paperwork and being seen as good farmer, respectively. 6 Regularly, one local research partner was involved in the interviews per study area. Only in the Romanian study area, 3 research partners were involved who alternately took the lead role. 7 In 2 cases, farmers opted for an interview in English respectively German because they were fluent in these languages and wanted to engage with the first author. Yet, throughout the interviews, native-speaking assistance was guaranteed through local research partners. V. Scherfranz et al. Ecological Economics 236 (2025) 108694 4 from −4 (most disagree) to +4 (most agree), guided by the question “How do you perceive your biodiversity measure?”. 8 For an illustration of the grid for Q-sorting, i.e. the distribution format applied, see Fig. 3. Sorting was followed by qualitative questions, inter alia on motivations for sorting to the extreme ends, and surprising, confusing, or missing statements. The qualitative follow-ups were audio-recorded or, if the farmer preferred, protocolled by means of written notes. 9 Farm characteristics and socio-demographics were assessed through a short questionnaire. 2.5. Q pattern analysis Q-sorts were analyzed jointly to identify different and shared patterns across participants from all study areas. For quantitative analysis, we used the open-source software KADE, version 1.2.1 (Banasick, 2019). First, by-person principal component analysis with subsequent Varimax rotation was run on the intercorrelated Q-sorts. In this study, Q-sorts are deemed as loading significantly on a factor if their loading exceeds ±0.403(P<0.01; calculated after Brown, 1980). Second, and based on extensive discussions among the conceptualizing co-authors on the one hand, as well as Humphrey’s rule (product of factor’s highest two loadings exceed once or, stricter, twice the standard error), the KaiserGuttman criterion (factor’s eigenvalue ≥1), and the number of significantly loading Q-sorts per factor (≥2) on the other hand, we decided to retain 5 factors (also see Brown, 1980; Watts and Stenner, 2012). For generating the factor arrays, only Q-sorts which have a minimum loading of ±0.403 on the respective factor and do not exceed this level for another factor were used for factor array calculation and further analysis. Additionally, consensus and distinguishing statements (P<0.01) were calculated. 10 Factor 3 was found bipolar, meaning that Q-sorts load significantly in both negative and positive terms. Following Brown (1980), we split this factor into sub-factors (3a/b), coming with separate factor arrays and, consequently, separate interpretations. Fig. 3 graphically illustrates the array for an exemplary factor/viewpoint. Functioning as best-estimate of all Q sorts that have been flagged for the respective factor, it is a specific arrangement of the Q-set with its 41 cost statements from four dimensions. For interpreting the factors and represented viewpoints, we largely adhered to “crib sheets” (Watts and Stenner, 2012): for each factor array, a crib sheet highlights which statements are ranked significantly differently (distinguishing statements), or simply more highly/lowly compared to all other arrays. Interpretation was supported by audiorecordings or protocols from the qualitative follow-ups. Audio-recordings were transcribed and translated by means of artificial intelligence, i.e. Whisper (Open AI, 2022) and DeepL Pro, with subsequent manual corrections. 3. Results 3.1. Factor characteristics Across study areas, valid Q-sorts and qualitative data from interviews with 34 farmers were collected. 11 The characteristics of the five extracted factors, including bipolar Factors 3a/b, are presented in Table 2. Inter-factor correlation was limited overall, ranging from ∣ 0.002∣ to ∣0.417∣, indicating high variation between the elicited factors. With an overall explained variance of 50%, we obtained a solution which in Q literature such as Watts and Stenner (2012) is viewed as statistically satisfactory and aligns with recent multi-national applications of Q methodology (also see Section 4.1). The extent to which each farmer’s Q-sort loads onto the factors can be seen in Appendix C. In Table 3, we describe the P-set and the farmers defining the respective factors. Fulfilling a pre-requisite for capturing potentially Table 1 Description of study areas by biodiversity threats and corresponding measures. Estonia (EE) Netherlands (NL) Romania (RO) United Kingdom (UK) Region West Estonian coast-line without islands South Limburg Uplands of Romanian North-West & Center Southern England Agricultural context Flat land, mainly crop and livestock farming, farms typically sized larger than 100 ha (Aamisepp et al., 2023) Loess-covered, incised plateau with terraces and slopes (van de Westeringh, 1980); mainly arable crops and grassland, average farm size lower than 30 ha (Agrimatie, 2018) Hilly/mountainous land, mainly mixed farming with extensive pastures and hay meadows, arable land and traditional orchards, high abundance of small-scale family farms (Page et al., 2012; Page and Popa, 2013. Flat/hilly land, mainly arable crops and livestock grazing, more than 70% of the land is part of farms larger than 100 ha (DEFRA, 2023b, DEFRA, 2023c). Threats to biodiversity Abandonment of coastal meadows, i.e. biodiversity-rich, semi-natural habitats created through traditional agriculture (Melts et al., 2018; Lotman and Rannap, 2020) Intensification of extensive permanent grassland, rich in biodiversity and determinant for scenic landscape (WallisDeVries et al., 2002) Intensification or abandonment of high nature value permanent grassland, associated with small-scale family farms (Page et al., 2012; Page and Popa, 2013) Intensification of farmland and loss of biodiversity in one of the most nature-depauperate countries worldwide (Boatman et al., 2007; Burns et al., 2023) Investigated biodiversity measure and requirements Conservation/restoration of coastal meadows, e.g. through land clearing, extensive grazing, delayed mowing Conservation/restoration of extensive grassland, e.g. through delayed mowing, extensive grazing, and ceased fertilization Conservation/restoration of highnature value grasslands, e.g. through delayed mowing, reduced machinery use, fertilization andgrazing pressure Winter cover cropping in arable systems to cover soil between summer harvest and spring cropping Governance Public (Estonian agri-environmental program) Public (Dutch agri-environmental program with local collective) Public (Romanian agri-environmental program) Public (UK agri-environmental program); private by certain water companies 8 For example, a farmer might strongly agree to perceive her measure like described in statement Q11 (“There is too much paperwork coming with the biodiversity measure.”) and thus assigned it to +4, while she agrees slightly less strongly to perceive the measure like described in statement Q1 (“The biodiversity measure is restricting the flexibility on my farm.”) and therefore assigned it to +3. 9 Out of 34 valid interviews (see Section 3.1), 32 farmers agreed to be audiorecorded and 2 farmers preferred protocolling by means of written notes. 10 In case of a distinguishing statement, a factor’s z score on an item differs significantly from those of other factors; in case of a consensus, it is similar across factors. The z score is based on the average of the ranks that the flagged Q-sorts of one factor assigned to an item, weighed by the sorts’ factor loadings (see, e.g., Zabala et al., 2018). 11 Despite comprehensive explanations, four Q-sorts had been excluded because statements were assigned only to the extreme ends (2), or Q-sorts and follow-ups showed substantial discrepancies (2). V. Scherfranz et al. Ecological Economics 236 (2025) 108694 5 diverse viewpoints, the P-set is diverse for most of the outlined selection criteria, including farm size 12 , farm types, farm management, years in the biodiversity measure, and age. These criteria are relatively wellbalanced, meaning that viewpoints of the respective sub-groups are similarly likely to be reflected in the data. Particularly in terms of gender, however, the P-set is overall not balanced and comprises more male (31) than female (3) farmers or farmers indicating “other” (0), risking that their viewpoints are captured to a lesser extent, or not at all. As can be further seen in Table 3, factors show a high variety in composition. Only farmers associated with Factor 3b tend to be similar for most characteristics, i.e. all applying conventional management as well as having long experience with biodiversity measures and nonuniversity education, while farmers associated with Factor 4 appear to have particularly few similarities. Noteworthy characteristics further relate to farmers associated with Factor 5, sharing a non-conventional farm management, and to Factor 4, coming with a comparatively high share of non-family farms. Table 4 shows the calculated factor arrays. It further reveals in which cost aspects the represented viewpoints differ from all others, as indicated through distinguishing statements (P<0.01). While accounted for in the calculations, no consensus statement, indicating convergent ratings, was observed across factors. Throughout reading, it is important to note that factor arrays are averaged best-estimates which are typical for the represented viewpoints, but can deviate from individual farmers’ flagged Q-sorts. Moreover, the Q-methodological approach helps to gain a more holistic understanding of what burdens farmers in the course of measure implementation through putting various cost aspects into relation and identifying viewpoints of farmers who deem similar costs relatively most or least relevant. Yet, no conclusions can be drawn about the absolute level of perceived costs and farmers’ (dis-)satisfaction with measure implementation. Similarly, the Q methodological approach is based on a strategically sampled, but not-representative P-set and, while capturing diverse viewpoints, does not inform about their relative abundance, i.e. distribution, among the European farming community (also see Sections 2and 4.3). Lastly, given the scope of this study, the viewpoints capture the perception of costs, while neither farmers’ motivations to (re-) implement their biodiversity measure, nor any effect on measure implementation can be deduced. 3.2. Presentation of factors Factor 1: governance-related uncertainty Farmers sharing the viewpoint represented by Factor 1 emphasize the problem of unstable or unclear regulations, or other forms of governance-related uncertainty coming with their biodiversity measure. More than any other group, they feel too much insecurity due to changing requirements (Q20:+4 13 ). Like Farmer RO-5 14 , arguing that Fig. 3. Graphical illustration of an exemplary factor array. Table 2 Summary of factor characteristics: explained variance, defining variables, and correlations of the five-factor-solution with split bipolar factors 3a/b. Factor 1 2 3a 3b 4 5 Explained variance (%) 13 9 8 14 6 Number of defining variables/flagged Q-sorts 7 6 3 2 8 3 Correlations between factor arrays Factor 1 2 3a 3b 4 5 1 1 0.232 0.186 0.155 0.417 −0.002 2 1 −0.025 0.079 0.232 0.067 3a 1 −0.254 0.385 0.061 3b 1 0.116 −0.056 4 1 0.071 5 1 12 With an average farm size of 86, 35, 4, and 82 ha in Estonia, the Netherlands, Romania, and the United Kingdom, respectively (European Commission, 2025, based on data from 2020; DEFRA, 2024, based on data from 2023), Table 3 shows that also farmers widely diverging from the national averages were included in the P-set to enable capturing of potentially diverse, rather than most common viewpoints. 13 see Table 4; denoted as follows: (Q[statement ID]:[sorting value]) 14 see Table C.1 in Appendix C; the ID of Q-sorts/farmers is composed of an abbreviation of the study area (RO =Romania, NL =Netherlands, UK=United Kingdom, EE =Estonia) and a randomized number V. Scherfranz et al. Ecological Economics 236 (2025) 108694 6 “It’s not really explained. […] We are in the fog every year”, Farmer RO-3 reasons that “policies change from year to year, and we have to adapt on the fly, and we can’t make an exact plan”. Farmer EE-6 relates uncertainty to potentially not receiving money when applying for grants, even in return for major investments: “And then, how do you build your activities, like, on sand?” In addition, worries are expressed that lease contracts might be cancelled despite ongoing obligations to maintain the biodiversity measure on the leased land (RO-3), funding might be stopped because of lacking state budget (EE-6), or payments are too dependent on unclear outcomes rather than controllable efforts (EE-5). Relatedly, farmers tend to agree to the statement that the agents making the biodiversity measures lack practical understanding (Q27:+3), resulting in inadequate rules (RO-2) or even barriers to biodiversity protection: “We do [biodiversity conservation] for pleasure. […] But agri-environmental measures don’t let us” (RO-3). Similarly, farming is deemed to have become more inflexible (Q1:+3). Compared to other groups, farmers sharing this viewpoint also agree more strongly that implementing their biodiversity measure comes at cost of time for family and friends (Q32:+2). This can be related to the process of funding application: “So if you don’t have anything at all right from the start, you’re like forced to write these project proposals [to apply for grants for biodiversity measures], […] all that running around, all of that is free time and night-time hours when you’re writing it, right” (EE-6), but also to increased handwork, as stated by several farmers. In line, it is typically strongly agreed that compensation is insufficient (Q39:+4): “There are many traditional practices, clearing the land is difficult, you can’t use machinery, the land is uneven, lack of labor, lack of people. […] You don’t find people. They’re expensive” (RO-5). Whereas farmers perceive relatively high costs associated with governance and practical work, as laid out above, they perceive their biodiversity measure as fitting their local context relatively well. Looking at the on-farm fit on the one hand, farmers in this group typically disagree to the statements that their measure negatively impacts essential operations (Q4:−4) and threats from pests or diseases increased due to implementation (Q13:−3). Accordingly, EE-6 concludes: “There’s nothing else to do here after all!“ From an aesthetical point of view, farmers seem to even enjoy their biodiversity measure (Q29:−3): “I find it ok to be ‘manicured’, chemical-free, traditional” (RO-5). Looking at the social fit, on the other hand, farmers tend not to feel stigmatized because of their biodiversity measure, least from the farming community (Q30:−4). Farmers also disagree, more strongly than any other group, that society pushes them into the biodiversity measure without taking action itself (Q25:−3* 15 ). Rather than social pressure, Farmer RO-2 experienced a certain indifference (“People want to have a full stomach and then, then they might read what’s written on [the product]”), and other farmers such as RO-5 experienced only positively motivating behavior from society: “They are even excited, [saying] ‘luckily you do that’…’that’s good!’” Factor 2: unproductiveness Factor 2 highlights a perceived discrepancy between farmers’ own, more production-oriented idea of farming and the need to reduce productiveness when implementing a biodiversity measure. This, on the one hand, results in emotional costs, including loss of identity: More than in any other group, farmers agree strongly that their farmland now looks less appealing to them (Q29:+3*). Farmer NL-10, while acknowledging his measure’s results, argues: “I prefer to see a straight field, like what is being mown every four weeks” and continues: “The school I went to, the agricultural school, they say you have to produce. Potatoes, milk, beet; and this has nothing to do with production”. Similarly, they agree relatively strongly that the work associated with their biodiversity measure is not part of a farmer’s job (Q19:+2). On the other hand, farmers feel that their biodiversity measures practically hinder production, coming with management-related costs for their farm. Importantly, farmers are most concerned that requirements sometimes do not fit the local conditions (Q10:+4*), as illustrated by NL-1“The person who has to control everything, he is driving around [one day before it is allowed] to see if somebody has mown the grass” but “I have to work with the climate, with the weather, and I don’t have to work with the calendar” (NL-1). Additionally, farmers sharing this viewpoint tend to agree that the agents involved in the biodiversity measure have too little practical understanding (Q27:+3). Besides, threats to farming are deemed more relevant due to the measure (Q13:+4*): Farmer NL-2 assumes that “there will be more insects and critters and so on that can be undesirable for the regular agriculture”, and Farmer NL-10 argues that “the herb goes into the manure. Then I drive it back to the other fields. […] Well, on our farm it is very important that everything we grow is weed-free. […] And that’s why we need much less pesticides”. Table 3 Description of total P-set (n =34) and, by factor, farmers with flagged Q-sorts (n =29). As detailed, this study differentiates between family farms and non-family farms based on who manages the farm, i.e. members of the owner’s family or external persons, such as employed farm managers without family ties. Like in Calus and Van Huylenbroeck (2010), this characteristic does not relate to the share of rented/owned land. Flagged Q sorts (farmers) by factors 1 2 3a 3b 4 5 Total 7 6 3 2 8 3 study area Estonia 9 4 2 – – 2– Netherlands 10 –4–1 2 2 Romania 7 3 –2 1 – – United Kingdom 8 – – 1–4 1 Farm characteristics farm size up to 50 ha 8 1 2 1 1 1 – 51–100 ha 5 1 1 –1 1 1 101–200 ha 7 3 1 1 – – 1 201–500 ha 5 1 1 – – 2– 501–1.000 ha 3 –1 1 –1– more than 1.000 ha 6 1 – – – 3 1 farm type (selfdeclared) mixed 16 4 3 2 1 4 1 mainly animal husbandry 7 3 1 –1– – mainly arable 9 –1 1 –4 1 mainly dairy 2 –1– – – 1 farm management conventional 19 3 4 2 2 5 – organic (certified) 9 3 1 – – 1 2 transition/others/ both 6 1 1 1 –2 1 years in biodiversity program less than 5 years 6 2 – – – 2– 5–10 years 10 1 3 2 –2 2 more than 10 years 18 4 3 1 2 4 1 family farm managed by owner’s family 27 6 6 2 2 4 2 managed by external person 7 1 –1–4 1 Farmer characteristics on-farm employment full or major time (>50%) in farming 26 5 3 1 2 7 3 half or minor time in farming 8 2 3 2 –1– gender female 3 –1 1 –1– male 31 7 5 2 2 7 3 other – – – – – – – age younger than 50 years 18 3 3 2 1 4 1 50 years and older 16 4 3 1 1 4 2 general education no university degree 12 2 2 1 2 1 2 university degree 21 5 4 2 –7 1 not disclosed 1 – – – – – – 15 Distinguishing statements (see Table 4) are indicated with asterisks throughout factor presentation. V. Scherfranz et al. Ecological Economics 236 (2025) 108694 7 Despite perceived conflicts between biodiversity measure and production, farmers sharing this viewpoint have comparatively less concern in terms of workload and complexity (e.g.: Q5:−2*; Q32:−3), as well as financial insecurity (Q38:−3) or insufficient payment (Q39:0). On the one hand, this can be because the business model is already based on extensive farming. For example, Farmers NL-1 reasons that “it’s not work-intensive because it’s […] in the strategy of the company”, while “the intensive ones are not going to fit because you have to change really a lot”. On the other hand, this can be because only disadvantaged plots are enrolled for which the measure still brings some income while not compromising production or, as argued by Farmer NL-10, the long-term value of the soil. Farmer NL-8 explains: “On that field, yes, there is no other function that brings more reward” but “if it was really an interesting arable plot, I wouldn’t do this”. Farmer NL-8 also links this to short contracts which do not allow for major changes, but which would be needed to “organize your whole business around that”. Relatedly, insecurity with the regulatory framework is ranked relatively high (Q2:+3). In line with a more production-oriented idea of farming, farmers sharing this viewpoint agree more strongly than other groups that societal pressure pushes them into their biodiversity measure, while society itself is not doing enough to protect biodiversity (Q25:+3). Farmer NL-8 reasons that, generally in agriculture, people tell farmers how to farm, although “I don’t think they themselves have ever touched a cow or anything”; and even if they manage a small garden, they “just do it on the side. If you’re a farmer and have to live […] of it, then it’s a different story”. Yet, in return for implementing their biodiversity measure, potentially even beyond personal preferences, farmers sharing this perspective typically ranked social cost aspects particularly low. Importantly, farmers most strongly disagree that they are no longer seen as good farmers among their peers (Q30:−4) and that agri-business actors view them more negatively (Q34:−4). According to NL-10, the biodiversity measure could rather serve as “license to produce”, legitimizing the more Table 4 Factor arrays for five-factor solution including bipolar Factors 3a/b. Distinguishing statements (P<0.01) are indicated in bold. Cost-dimension ID Cost aspect/Statement 1 2 3a 3b 4 5 managementrelated costs 1 The biodiversity measure is restricting the flexibility on my farm. 3 1 ¡42−1 1 2 There is too much insecurity with the biodiversity measure, e.g. because of changing policies, rules and requirements, funding or participation criteria. 2 3 1 0 2 1 3 The biodiversity measure conflicts with other rules and requirements on my farm. 1 0 ¡41 0 0 4 The biodiversity measure negatively impacts essential operations on my farm. −4−3−3 0 −1−1 5 My farming has become more complex with implementing the biodiversity measure. 0 ¡22 1 3 2 6 There is too much conflicting information around the biodiversity measure: I do not know which advice to follow. 2 1 2 ¡21 2 7 I hardly receive feedback, e.g. on what is going well or how I could improve the biodiversity measure. −1 2 1 −2 2 2 8 There is too little practical information available on the biodiversity measure. 0 −13−1 0 −3 9It is difficult to access the materials required for the biodiversity measure (e.g. equipment, seeds or breeds). 2 0 0 3 −4−4 10 The requirements of the biodiversity measure are sometimes unsuitable for the local conditions of my farm, such as soil or weather. 04−3−2−1 1 11 There is too much paperwork coming with the biodiversity measure. 1 0 −2 3 2 3 12 Due to the biodiversity measure, the overall workload on my farm has increased. 2 −1 0 0 3 0 13 Due to the biodiversity measure, I increasingly have to deal with pests, diseases or other threats.−34−2 1 1 −2 14 The biodiversity measure hinders me from adapting my farm to climate change.−1−1−3 0 −4−3 15 With the biodiversity measure, farm work has become physically more straining. 1 −2−1−2 1 −1 emotional costs 16 There is too much external interference coming with the biodiversity measure: I often feel surveilled or lectured.1 2 −1 3 3 −2 17 I feel that my own knowledge is ignored by the agents involved in the biodiversity measure. 0 −13−1 0 0 18 I sometimes feel overwhelmed by all the requirements of the biodiversity measure. 0 0 0 2 3 3 19 Managing such a biodiversity measure does not feel like being part of a farmer’s job.−1 2 −2 3 −3−1 20 Since having opened my farm for the biodiversity measure, I feel exposed to ever new requirements. 4 1 −1 2 −1 0 21 I am stressed that I will be penalized harshly if I accidentally make a mistake with the biodiversity measure. 1 2 1 4 2 1 22 I sometimes feel that my efforts spent on the biodiversity measure will not make any difference.−1−1 0 −4 0 −3 23 I sometimes feel like being left alone with everything related to the biodiversity measure. 0 −3 1 −4−1−2 24 I sometimes feel treated unfairly with regard to the biodiversity measure. 0 0 0 ¡32 2 25 I feel frustrated that societal pressure pushed me into the biodiversity measure, while society itself does not do enough to protect biodiversity. ¡33 1 0 1 0 26 I feel that my efforts spent on the biodiversity measure are not acknowledged by society.−1 1 4−2 0 1 27 I feel the agents making such a biodiversity measure have too little understanding of farming. 3 3 2 1 0 ¡2 28 I feel overwhelmed with all the responsibility for protecting biodiversity that is now resting on my shoulders. 1−2−241−3 29 Due to the biodiversity measure, my farm land looks less appealing to me. −33−3−1−3 1 social costs 30 Due to the biodiversity measure, other farmers no longer see me as a good farmer.−4−4−2 2 −2 3 31 Due to the biodiversity measure, some neighbors are worried about pests, diseases or other threats coming from my farm. −2 1 −1−3−24 32 The biodiversity measure comes at cost of time for my family or friends. 2 −3 1 −1−2−1 33 People would judge me harshly if they feel that I make a mistake with regard to the biodiversity measure. −2−2 3 −3−1 2 34 Due to the biodiversity measure, actors such as banks, fertilizer or crop protection suppliers view me more negatively.−3−4 0 1 −3 3 financial costs 35 Generally, the biodiversity measure resulted in higher prices for buying or renting new farm land. 3 1 2 −1−2 4 36 Due to the biodiversity measure, my farm has a disadvantage compared to my competitors.−2−1−1−3−3−1 37 My efforts spent on the biodiversity measure are not reflected in higher prices on the market. 3 2 4 0 4−1 38 Financial uncertainty has increased due to the biodiversity measure. −2−3−1 2 0 −2 39 The implementation and management of the biodiversity measure causes expenses which are only partially covered by the payments. 4 0 3 1 4 0 40 Due to the biodiversity measure, my farm has to forego more lucrative business opportunities.−2−2 0 0 −2 0 41 Due to the biodiversity measure, it is more difficult for my farm to respond to changed economic conditions.−1 0 2 −1 1 −4 V. Scherfranz et al. Ecological Economics 236 (2025) 108694 8 intensive production on their remaining farmland in front of business partners, who seek to improve their image. Similarly, Farmer EE-7 who is implementing biodiversity measures against land abandonment (shrub encroachment) argues that those owning her rented land live in cities, but “they come here once a year for a holiday [and they] are indeed happy that... well, that the surrounding fields are clean […]”. Factor 3a: lack of support More than any other group, farmers sharing this perspective feel insufficient support. This, on the one hand, refers to the public: Farmers are most concerned that their efforts are not acknowledged by society (Q26:+4*), which would rather judge any seeming mistakes harshly (Q33:+3), nor by the market (Q37:+4). Farmer RO-1 illustrates “that’s two of us from like, I do not know, 2.000 people” who appreciate the biodiversity measure in the region, while “your clients, where you are selling the milk and so on, they don’t look […] if you give only hay to the cows”. On the other hand, farmers tend to wish for more support from the administrative bodies. Compared to other groups, farmers ranked more highly that agents involved in the respective biodiversity measure ignore farmers’ knowledge (Q17:+3). Additionally, seeking unambiguous and practical information is perceived as, while not impossible, at least challenging (Q8:+3* // 6:+2). Farmer UK-2 illustrates: “I think there is more information becoming available, it’s just knowing where to find it […] and how to interpret it and how to put it to use on your own farm. […] I’m confident we’re doing the right thing, yeah, it’s not impacting our business so actually let’s keep doing it. But it will be nice to get some information back to say, this is what’s happening“ (UK-2). Even though farmers in this group thus feel more left alone with everything related to their biodiversity measures than all other groups, as shown in comparatively most agreement with statement Q23 (+1), there is little doubt about the measure’s general fit to their management, intrinsic values and administration. Farmers disagree relatively strongly that flexibility is restricted (Q1:−4*), essential operations are impacted (Q4:−3), and that the measure conflicts with other rules and requirements (Q3:−4*), unsuitable local conditions (Q10:−3), or aesthetical preferences (Q29:−3). Farmer RO-1 illustrates: “You don’t have to feel constrained just by some rules that you […] have to apply in order to maintain something you think that it’s valuable for you and for the community”. Similarly, farmers perceive the bureaucratic side of their biodiversity measure as hardly onerous. Compared to other groups, there is no outstanding overload with administrative topics, particularly in terms of excessive paperwork (Q11:−2). Factor 3b: administrative burden Given its bipolarity, farmers sharing the viewpoint represented in Factor 3b are least concerned about lacking appreciation and support related to their biodiversity measure, as farmers associated with Factor 3a are. This particularly manifests in strong disagreement to feeling left alone with everything related to the measure (Q23:−4). They are also most sure about being treated fairly (Q24:−3) and having unambiguous advice (Q6:−2*). Likewise, farmers tend to relatively disagree that they feel judged or unacknowledged by society (Q33:−3; Q26:−2), which expands to neighbors who do not appear worried about negative impacts from the biodiversity measure (Q31:−3). Farmer NL-3 illustrates: “Nature management is […] being valued by the social environment, there are people who compliment you”. It is, in contrast, the measure’s administration which is perceived most burdensome among the farmers sharing this viewpoint. Farmers are most concerned of harsh penalties in case of accidental mistakes (Q21:+4), excessive paperwork (Q11:+3), difficulties to access the required materials (Q9:+3), too much responsibility resting on their shoulders (Q28:+4*), and high levels of surveilling or lecturing interference (Q16:+3): “Every year, a week would go just on inspection”, Farmer RO-4 illustrates. In line, farmers sharing this perspective agree more strongly than all other groups that financial insecurity has increased (Q38:+2). This, not least, can be related to such administrative issues: “If [the inspector] caught you not complying, there was the problem that he could take money from you 5 years in advance. If someone else ploughed your land by mistake, you couldn’t prove it, it was still your fault” (RO-4). Compared to other groups, farmers in this group also strongly agree that their biodiversity measure does not feel like part of a farmer’s job (Q19:+3): “On the one hand, you’re a farmer and on the other hand a nature manager. That conflicts from early morning to late evening” (NL-3). This supposedly also leads to a perceived loss of reputation among the farming community (Q30:+2). Farmer NL-3 argues that for an intensive arable farmer, “it looks like a mess”, but “you have natural management, you also produce something different”. As a result, farmers sharing this viewpoint strongly disagree that the biodiversity measure would not make a difference (Q22:−4) and still do not feel like having put themselves into a disadvantageous economic position compared to their competitors (Q36:−3). Factor 4: underpayment Farmers sharing the viewpoint represented in Factor 4 are most concerned that their physical and cognitive efforts are not sufficiently acknowledged in financial terms. Firstly, this relates to the market price that does not rise in response to the measures (Q37:+4*). Farmer UK-6 reasons: “There are added benefits that we’re bringing that we’re not being rewarded for, […] whether that be the marketplace that rewards that or another stakeholder in our environment should be rewarding for that”. Secondly, it relates to the level of compensation payments from the program (Q39:+4). Farmers argue that the measure comes with too restrictive regulations, allowing only partial funding of the area under the measure (UK-7), as well as monetary drawbacks which are not covered, such as inflation (NL-4, NL-7), machinery wearing out on rocky, extensive land (EE-1), grazing livestock killed by wild animals (EE-1), or additional work to handle slug pressure on cover crops (UK-3). Relating to this, farmers agree relatively strongly that they encounter unfair treatment (Q24:+2). This is, for example, put down to unreasonable controls (e.g. UK-3, NL-4), suppliers benefitting at their costs (UK-1), or unequal payments compared to non-agricultural nature managers: “When natural areas are being mowed for municipalities, then the horticulturists get […] very high rates per hour to mow it with very special machines. We do it all by hand and we do the same, but for trifle” (NL-4). In line with perceived underpayment, farmers in this group view their measures as exacting substantial efforts management-wise. This is indicated through high rankings of increased complexity in farming (Q5:+3) or overall workload (Q12:+3). Cover-cropping Farmer UK-3 illustrates: “I think making another job at the busiest month of the year is not good news and, and definitely it’s more complex”. Also, it relates to economics: “If I am going to mow around bird nests and I am going to mow in rows and I am going to mow in phases in two times on the plot, then I have much higher costs” (NL-4). Similarly, farmers sharing this perspective feel that their biodiversity comes with substantial administrative work (Q18:+3; Q16:+3). Interestingly, even though farmers sharing this viewpoint feel that their efforts are underpaid, i.e. not sufficiently acknowledged, they are relatively confident that they still made the right decision with implementing their biodiversity measure. They tend not to see more lucrative business opportunities (Q40:−2) and cannot see why they should be looked at more negatively by agri-business actors (Q34:−3) or be less competitive (Q36:−3). Farmer UK-6 argues: “There’s a cost included, but I don’t feel that that cost disadvantages me”. Such observation might relate to the fact that, beyond feeling entitled to more financial rewards for their work, the farmers’ values as well as their social environment and business concept harmonize particularly well with the respective measure, similar to, e.g., Factor 1. Those sharing cost-viewpoint 4 not only experience the implementation and management of such biodiversity measures as part of their job (Q19:−3) and enjoy them aesthetically (Q29:−3). They also ranked several costs related to their social environment and deficient local embeddedness of the biodiversity measure V. Scherfranz et al. Ecological Economics 236 (2025) 108694 9 European Union, 2020. Biodiversity on farmland: CAP contribution has not halted the decline. Special Report No. 13/2020.. Available at https://www.eca.europa.eu/lists/ ecadocuments/sr20_13/sr_biodiversity_on_farmland_en.pdf. Fienitz, M., 2018. Agri-environmental payments in Romania: farmers’ motivations and impediments for participation. Territorial Identity and Development 3, 52–74. https://doi.org/10.23740/TID120184. Gatto, P., Mozzato, D., Defrancesco, E., 2019. Analysing the role of factors affecting farmers’ decisions to continue with Agri-environmental schemes from a temporal perspective. Environ. Sci. Pol. 92, 237–244. https://doi.org/10.1016/j. envsci.2018.12.001. Greiner, R., Lankester, A., Patterson, L., 2007. Incentives to enhance the adoption of’best management practices’ by landholders: Achieving water quality Improvements in the Burdekin River catchment. Research Report for the Burdekin Dry Tropics NRM and the Coastal Catchment Initiative (Burdekin). Grosjean, P., Kontoleon, A., 2009. How sustainable are sustainable development programs? The case of the sloping land conversion program in China. World Dev. 37, 268–285. https://doi.org/10.1016/j.worlddev.2008.05.003. Ingram, J., Gaskell, P., Mills, J., Short, C., 2013. Incorporating Agri-environment schemes into farm development pathways: A temporal analysis of farmer motivations. Land Use Policy 31, 267–279. https://doi.org/10.1016/j. landusepol.2012.07.007. Janssen, L., Klein, N., Taylor, G., Opoku, E., Holbeck, M., 2008. Conservation reserve program in South Dakota - major findings from 2007 survey of South Dakota CRP respondents (no. 2008–1). Economics Research Report. http://econ.sdstate.edu/Res earch/CRP2008.pdf. Jordbruksverket, 2022. Result and Value Based Payments for Field Elements and Forest Edges – Project Summary.. Available at https://webbutiken.jordbruksverket.se/en /artiklar/result-and-value-based-payments-for-field-elements-and-forest-edges.html. Klebl, F., Feindt, P.H., Piorr, A., 2024. Farmers’ behavioural determinants of on-farm biodiversity management in Europe: a systematic review. Agric. Hum. Values 41, 831–861. https://doi.org/10.1007/s10460-023-10505-8. Knowler, D., Bradshaw, B., 2007. Farmers’ adoption of conservation agriculture: A review and synthesis of recent research. Food Policy 32, 25–48. https://doi.org/ 10.1016/j.foodpol.2006.01.003. Kuhfuss, L., Pr´ eget, R., Thoyer, S., Hanley, N., Le Coent, P., D´ esol´ e, M., 2016. Nudges, social norms, and permanence in Agri-environmental schemes. Land Econ. 92, 641–655. https://doi.org/10.3368/le.92.4.641. Lim, S., Wachenheim, C., 2022. Predicted enrollment in alternative attribute conservation reserve program contracts. Land Use Policy 117, 106090. https://doi. org/10.1016/j.landusepol.2022.106090. Lotman, S., Rannap, R., 2020. Coastal meadows maintenance plan. Environmental Board, 1–67. Available at https://keskkonnaamet.ee/sites/default/files/documents /2022-09/Maintenance%20plan%20for%20coastal%20meadows.pdf. Lutter, S.H., Dayer, A.A., Larkin, J.L., 2019. Young Forest conservation incentive programs: explaining re-enrollment and post-program persistence. Environ. Manag. 63, 270–281. https://doi.org/10.1007/s00267-018-1127-1. Maaeluminister, 2022. Perioodi 2023–2027 p¨ arandniidu hooldamise toetus. RT I, 29.12.2022, 21 [WWW Document]. URL. https://www.riigiteataja.ee/akt/129 122022021 (accessed 5.24.24). Mack, G., Kohler, A., Heitk¨ amper, K., El-Benni, N., 2019. Determinants of the perceived administrative transaction costs caused by the uptake of an Agri-environmental program. J. Environ. Plan. Manag. 62, 1802–1819. https://doi.org/10.1080/ 09640568.2018.1515311. Mack, G., Ritzel, C., Heitk¨ amper, K., El Benni, N., 2020. The effect of administrative Burden on farmers’ perceptions of cross-compliance-based direct payment policy. Public Adm. Rev. 81, 664–675. https://doi.org/10.1111/puar.13335. MADR, 2022. Raportul privind planului strategic. PAC 2021.. Available at https://www. madr.ro/docs/dezvoltare-rurala/2022/PNS_2023-2027_vers-_1.0_sfc2021-202 3RO06AFSP001.pdf. Matthews, A., 2024. Farmer protests and the 2024 European Parliament elections. Intereconomics 59, 83–87. https://doi.org/10.2478/ie-2024-0018. Melts, I., Lanno, K., Sammul, M., Uchida, K., Heinsoo, K., Kull, T., Laanisto, L., 2018. Fertilising semi-natural grasslands may cause long-term negative effects on both biodiversity and ecosystem stability. J. Appl. Ecol. 55, 1951–1955. https://doi.org/ 10.1111/1365-2664.13129. Mettepenningen, E., Verspecht, A., Van Huylenbroeck, G., 2009. Measuring private transaction costs of European Agri-environmental schemes. J. Environ. Plan. Manag. 52, 649–667. https://doi.org/10.1080/09640560902958206. Moynihan, D., Herd, P., Harvey, H., 2015. Administrative Burden: learning, psychological, and compliance costs in citizen-state interactions. J. Public Adm. Res. Theory 25, 43–69. https://doi.org/10.1093/jopart/muu009. Murphy, G., Hynes, S., Murphy, E., O’Donoghue, C., 2014. An investigation into the type of farmer who chose to participate in rural environment protection scheme (REPS) and the role of institutional change in influencing scheme effectiveness. Land Use Policy 39, 199–210. https://doi.org/10.1016/j.landusepol.2014.02.015. Natuurrijk Limburg, 2022. Toelichting Collectief en Regionale collectief. Available at https://www.natuurrijklimburg.nl/wp-content/uploads/20221215-NaLi-beheerco ntract-bijlage-6-Toelichting-Collectief-en-Regionale-collectief-v11.0.pdf. Natuurrijk Limburg, 2024. Bijlage 2: Beheerpakketten Limburg – beheervoorschriften behorende bij het beheerpakket. Available at https://www.natuurrijklimburg.nl /wp-content/uploads/Bijlage-2-Beheerpakketten-Limburg-v19.4.pdf. Open AI, 2022. Whisper [Large language model v3] (downloaded December 2023) Available at https://openai.com/index/whisper/. Page, N., Popa, R., 2013. Family farming in Romania. Fundația ADEPT Transilvania, 1–13. Page, N., Bˆ alan, A., Huband, S., Popa, R., R´ akosy, L., Sutcliffe, L., 2012. Romˆ ania/ Romania. In: Oppermann, R., Beaufoy, G., Jones, G. (Eds.), High Nature Value Farming in Europe. Verlag Regionalkultur, Ubstadt-Weiher. Pe’er, G., Finn, J.A., Díaz, M., Birkenstock, M., Lakner, S., R¨ oder, N., Kazakova, Y., ˇ Sumrada, T., Bez´ ak, P., Concepci´ on, E.D., D¨ anhardt, J., Morales, M.B., Rac, I., ˇ Spulerov´ a, J., Schindler, S., Stavrinides, M., Targetti, S., Viaggi, D., Vogiatzakis, I.N., Guyomard, H., 2022. How can the European common agricultural policy help halt biodiversity loss? Recommendations by over 300 experts. Conserv. Lett. 15, e12901. https://doi.org/10.1111/conl.12901. Pratt, B., Wallander, S., 2022. Cover Practice Definitions and Incentives in the Conservation Reserve Program. EIB-233,. U.S. Department of Agriculture, Economic Research Service. Prokopy, L.S., Floress, K., Arbuckle, J.G., Church, S.P., Eanes, F.R., Gao, Y., Gramig, B.M., Ranjan, P., Singh, A.S., 2019. Adoption of agricultural conservation practices in the United States: evidence from 35 years of quantitative literature. J. Soil Water Conserv. 74, 520–534. https://doi.org/10.2489/jswc.74.5.520. Race, D., Curtis, A., 2009. Building long-term commitment by landholders to conservation of native vegetation: Characteristics of successful programs. Presented at the 18th World IMACS / MODSIM Congress, Cairns, Australia. Ranjan, P., Church, S.P., Floress, K., Prokopy, L.S., 2019. Synthesizing conservation motivations and barriers: what have we learned from qualitative studies of farmers’ behaviors in the United States? Soc. Nat. Resour. 32, 1171–1199. https://doi.org/ 10.1080/08941920.2019.1648710. Rannap, R., Briggs, L., Lotman, K., Lepik, I., Rannap, V., 2004. Coastal meadow management. Best Practice Guidelines. The experiences of LIFE-nature project “Boreal Baltic coastal Meadow Preservationin Estonia”. - LIFE00NAT/EE/7083. Ministry of the Environment of the Republic of Estonia, Tallinn. Regulation (EU) No 1305/2013 of the European Parliament and of the Council of 17 December 2013 on support for rural development by the European Agricultural Fund for Rural Development (EAFRD) and repealing Council Regulation (EC) No 1698/ 2005. Regulation (EU) 2021/2115 of the European Parliament and of the Council of 2 December 2021 establishing rules on support for strategic plans to be drawn up by Member States under the common agricultural policy (CAP Strategic Plans) and financed by the European Agricultural Guarantee Fund (EAGF) and by the European Agricultural Fund for Rural Development (EAFRD) and repealing Regulations (EU) No 1305/2013 and (EU) No 1307/2013. Reimer, A.P., Prokopy, L.S., 2014. Farmer participation in U.S. Farm Bill Conservation Programs. Environmental Management 53, 318–332. https://doi.org/10.1007/ s00267-013-0184-8. Reimer, A., Thompson, A., Prokopy, L.S., Arbuckle, G., Genskow, K., Jackson-Smith, D., Lynne, G., McCann, L., Morton, L., Nowak, P., 2014. People, place, behavior, and context: A research agenda for expanding our understanding of what motivates farmers’ conservation behaviors. J. Soil Water Conserv. 69, 57A–61A. https://doi. org/10.2489/jswc.69.2.57A. Riley, M., 2016. How does longer term participation in Agri-environment schemes [re] shape farmers’ environmental dispositions and identities? Land Use Policy 52, 62–75. https://doi.org/10.1016/j.landusepol.2015.12.010. Ritzel, C., Mack, G., Portmann, M., Heitk¨ amper, K., Benni, N.E., 2020. Empirical evidence on factors influencing farmers’ administrative burden: A structural equation modeling approach. PLoS One 15, e0241075. https://doi.org/10.1371/ journal.pone.0241075. R¨ o¨ os, E., Wood, A., S¨ all, S., Abu Hatab, A., Ahlgren, S., Hallstr¨ om, E., Tidåker, P., Hansson, H., 2023. Diagnostic, regenerative or fossil-free - exploring stakeholder perceptions of Swedish food system sustainability. Ecol. Econ. 203, 107623. https:// doi.org/10.1016/j.ecolecon.2022.107623. Rural Payments Agency, Natural England, 2022. SW6: Winter cover crops [WWW Document]. URL. https://www.gov.uk/countryside-stewardship-grants/wintercover-crops-sw6 (accessed 3.20.24). Sammul, M., Kattai, K., Lanno, K., Meltsov, V., Otsus, M., N˜ ouakas, L., Kukk, D., Mesipuu, M., Kana, S., Kukk, T., 2008. Wooded meadows of Estonia: conservation efforts for a traditional habitat. Agric. Food Sci. 17, 413–429. https://doi.org/ 10.2137/145960608787235513. Sander, A., Ghazoul, J., Finger, R., Schaub, S., 2024. Participation in individual and collective Agri-environmental schemes: A synthesis using the theory of planned behaviour. J. Rural. Stud. 107, 103255. https://doi.org/10.1016/j. jrurstud.2024.103255. Schaller, L., Scherfranz, V., Kohrs, M., H¨ afner, K., Klebl, F., Ruiz, J., Kantelhardt, J., Piorr, A., Adler, A., Alonso, E.V., Barreiro, S., Bodea, F.V., de Vries, R., Santos, A.D., Ganz, M., Hood, A., Mauchline, A., Melts, I., M¨ ohring, N., Razvan, P., Ruck, A., Segre, H., Vajna, F., 2022. Report on EBA incentives, drivers and key determinants of uptake of biodiversity management by farmers (deliverable D2.2, D9) (no. (2022) 2829901).. Available at https://showcase-project.eu/storage/app/uploads/public /635/a1c/317/635a1c3173400778536638.pdf. Schaub, S., Ghazoul, J., Huber, R., Zhang, W., Sander, A., Rees, C., Banerjee, S., Finger, R., 2023. The role of behavioural factors and opportunity costs in farmers’ participation in voluntary Agri-environmental schemes: A systematic review. Aust. J. Agric. Econ. 74, 617–660. https://doi.org/10.1111/1477-9552.12538. Scherfranz, V., Moon, K., Kantelhardt, J., Adler, A., Barreiro, S., Bodea, F.V., Bretagnolle, V., Br¨ onnimann, V., De Vries, J.P.R., Dos Santos, A., Ganz, M., Herrera, J.M., Hood, A.S.C., Leisch, F., Mauchline, A.L., Melts, I., Popa, R., Rivera Gir´ on, V.M., Ruck, A., Vajna, F., Velado-Alonso, E., Schaller, L., 2024. Using a perception matrix to elicit farmers‘ perceptions towards stakeholders in the context of biodiversity-friendly farming. J. Rural. Stud. 108, 103282. https://doi.org/ 10.1016/j.jrurstud.2024.103282. V. Scherfranz et al. Ecological Economics 236 (2025) 108694 16 Schulze, C., Matzdorf, B., 2023. The institutional design of Agri-environmental contracts—how stakeholder attitudes can inform policy making. Q Open 3, 1–24. https://doi.org/10.1093/qopen/qoad001. Schulze, C., Zag´ orska, K., H¨ afner, K., Markiewicz, O., Czajkowski, M., Matzdorf, B., 2024a. Using farmers’ ex ante preferences to design Agri-environmental contracts: A systematic review. J. Agric. Econ. 75, 44–83. https://doi.org/10.1111/14779552.12570. Schulze, C., Matzdorf, B., Rommel, J., Czajkowski, M., García-Llorente, M., Guti´ errezBrice˜ no, I., Larsson, L., Zag´ orska, K., Zawadzki, W., 2024b. Between farms and forks: food industry perspectives on the future of EU food labelling. Ecol. Econ. 217, 108066. https://doi.org/10.1016/j.ecolecon.2023.108066. Selinske, M.J., Coetzee, J., Purnell, K., Knight, A.T., 2015. Understanding the motivations, satisfaction, and retention of landowners in private land conservation programs. Conserv. Lett. 8, 282–289. https://doi.org/10.1111/conl.12154. Selinske, M.J., Cooke, B., Torabi, N., Hardy, M.J., Knight, A.T., Bekessy, S.A., 2016. Locating financial incentives among diverse motivations for long-term private land conservation. E&S 22, 7. https://doi.org/10.5751/ES-09148-220207. Sneegas, G., Beckner, S., Brannstrom, C., Jepson, W., Lee, K., Seghezzo, L., 2021. Using Q-methodology in environmental sustainability research: A bibliometric analysis and systematic review. Ecol. Econ. 180, 106864. https://doi.org/10.1016/j. ecolecon.2020.106864. Stuart, D., Gillon, S., 2013. Scaling up to address new challenges to conservation on US farmland. Land Use Policy 31, 223–236. https://doi.org/10.1016/j. landusepol.2012.07.003. ˇ Sumrada, T., Vreˇ s, B., ˇ Celik, T., ˇ Silc, U., Rac, I., Udovˇ c, A., Erjavec, E., 2021. Are resultbased schemes a superior approach to the conservation of high nature value grasslands? Evidence from Slovenia. Land Use Policy 111, 105749. https://doi.org/ 10.1016/j.landusepol.2021.105749. Suske Consult, n.d. Results-based Nature Conservation Plan: Experiences and Conclusions from the Project 2013–2020. Available at https://www.rbpnetwork. eu/media/fachbericht_en_final_klein.pdf. Swann, E., Richards, R., 2016. What factors influence the effectiveness of financial incentives on long-term natural resource management practice change? Evidence Base 2016, 1–32. Thames Water, 2023. Catchment Fund Handbook - Nitrate Options - Spring 2023.. Available at https://www.thameswater.co.uk/media-library/home/about-us/resp onsibility/smarter-water-catchments/catchment-fund-handbook-nitrate.pdf. Tyllianakis, E., Martin-Ortega, J., 2021. Agri-environmental schemes for biodiversity and environmental protection: How we are not yet “hitting the right keys”. Land Use Policy 109, 105620. https://doi.org/10.1016/j.landusepol.2021.105620. van de Westeringh, Q., 1980. Soils and their geology in the Geul valley. Mededelingen van de Landbouwhogeschool Wageningen 80 (1980), 1–25. Vaske, J.J., Pallazza, S., Milley, B., 2021. Persistence of Voluntary Conservation Actions on Private Lands: The Impact of the Partners for Fish and Wildlife Program. Verburg, R.W., Verberne, E., Negro, S.O., 2022. Accelerating the transition towards sustainable agriculture: The case of organic dairy farming in the Netherlands. Agric. Syst. 198, 103368. https://doi.org/10.1016/j.agsy.2022.103368. Walder, P., Kantelhardt, J., 2018. The environmental behaviour of farmers – capturing the diversity of perspectives with a Q methodological approach. Ecol. Econ. 143, 55–63. https://doi.org/10.1016/j.ecolecon.2017.06.018. WallisDeVries, M.F., Poschlod, P., Willems, J.H., 2002. Challenges for the conservation of calcareous grasslands in northwestern Europe: integrating the requirements of flora and fauna. Biol. Conserv. 104, 265–273. https://doi.org/10.1016/S0006-3207(01) 00191-4. Watts, S., Stenner, P., 2005. Doing Q methodology: theory, method and interpretation. Qual. Res. Psychol. 2, 67–91. https://doi.org/10.1191/1478088705qp022oa. Watts, S., Stenner, P., 2012. Doing Q Methodological Research - Theory, Method & Interpretation. SAGE Publications, London. Webler, T., Danielson, S., Tuler, S., 2009. Using Q Method to Reveal Social Perspectives in Environmental Research. Social and Environmental Research Institute, Greenfield. Zabala, A., Sandbrook, C., Mukherjee, N., 2018. When and how to use Q methodology to understand perspectives in conservation research. Conserv. Biol. 32, 1185–1194. https://doi.org/10.1111/cobi.13123. V. Scherfranz et al. Ecological Economics 236 (2025) 108694 17