PG tool model, raw data from sustainability assessment performed on 106 European innovative livestock farms and published manuscript

International Journal of Agricultural Sustainability ISSN: 1473-5903 (Print) 1747-762X (Online) Journal homepage: www.tandfonline.com/journals/tags20 Exploring relationships among different sustainability aspects in innovative livestock systems in Europe Elena Diaz Vicuna, Nina Adams, Laurence Smith, Magdalena. Durand, Coen van Wagenberg, Enora Caron, Agnes van den Pol-van Dasselaar, Lukas Baumgart, Soline Schetelat, Mignon Sandor, Adrian Gliga, Sandrine Espagnol, Heidi Mai-Lis Andersen, Robert Borek, Piotr Jurga, Noa van Leuffen, Jasper L.T. Heerkens, Jenny Yngvesson, Annabel Oosterwijk, Jessica Stokes, Claudio Forte & Anna Hessle To cite this article: Elena Diaz Vicuna, Nina Adams, Laurence Smith, Magdalena. Durand, Coen van Wagenberg, Enora Caron, Agnes van den Pol-van Dasselaar, Lukas Baumgart, Soline Schetelat, Mignon Sandor, Adrian Gliga, Sandrine Espagnol, Heidi Mai-Lis Andersen, Robert Borek, Piotr Jurga, Noa van Leuffen, Jasper L.T. Heerkens, Jenny Yngvesson, Annabel Oosterwijk, Jessica Stokes, Claudio Forte & Anna Hessle (2025) Exploring relationships among different sustainability aspects in innovative livestock systems in Europe, International Journal of Agricultural Sustainability, 23:1, 2601486, DOI: 10.1080/14735903.2025.2601486 To link to this article: https://doi.org/10.1080/14735903.2025.2601486 © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. View supplementary material Published online: 14 Dec 2025. Submit your article to this journal Article views: 196 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=tags20 INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 2025, VOL. 23, NO. 1, 2601486 https://doi.org/10.1080/14735903.2025.2601486 RESEARCH ARTICLE Exploring relationships among different sustainability aspects in innovative livestock systems in Europe Elena Diaz Vicuna a , Nina Adams b,c , Laurence Smith b,d , Magdalena. Durand b , Coen van Wagenberg e , Enora Caron f , Agnes van den Pol-van Dasselaar g , Lukas Baumgart h , Soline Schetelat i , Mignon Sandor j , Adrian Gliga j , Sandrine Espagnol k , Heidi Mai-Lis Andersen l , Robert Borek m , Piotr Jurga n , Noa van Leuffen g , Jasper L.T. Heerkens g , Jenny Yngvesson o , Annabel Oosterwijk e , Jessica Stokes p , Claudio Forte a and Anna Hessle o a University of Turin, Veterinary Sciences, Grugliasco, Italy; b University of Reading, School of Agriculture, Policy and Development, Reading, United Kingdom; c Ökomodell-Region Landkreis Waldeck-Frankenberg, Korbach, Germany; d Swedish University of Agricultural Sciences, Biosystems and Technology, Lomma, Sweden; e Wageningen Social & Economic Research, Part of Wageningen University & Research, Wageningen, the Netherlands; f ITAVI, Rennes, France; g Aeres University of Applied Sciences, Dronten, the Netherlands; h Research Institute of Organic Agriculture FiBL, Frick, Switzerland; i French Livestock Institute, Le Rheu, France; j University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Faculty of Agriculture, Cluj-Napoca, Romania; k French Institute for Pig and Pork Industry, Pacé, France; l Innovation Centre for Organic Farming, Livestock, Aarhus N, Denmark; m Institute of Soil Science and Plant Cultivation – State Research Institute (IUNG-PIB), Department of Bioeconomics and Agrometeorology, Puławy, Poland; n Institute of Soil Science and Plant Cultivation – State Research Institute (IUNG-PIB), Department of Geomatics, Puławy, Poland; o Swedish University of Agricultural Sciences, Applied Animal Science and Welfare, Skara, Sweden; p Royal Agricultural University, Cirencester, United Kingdom ABSTRACT European livestock farming reflects a range of systems characterized by diverse innovations to increase sustainability. This study aimed to highlight the relationships among different aspects of sustainability in various types of systems. Data were retrieved from 106 farms containing different animal species (dairy cattle, beef cattle, pigs and poultry), using a modified version of the Excel-based questionnaire public goods tool. Each farm was assigned scores for 12 spurs (indicators) of sustainability. Based on these results, the farms were allocated into five clusters. Correlations among the spurs were evaluated, both across all farms and cluster-wise, with contrasting results. When analysed across all farms, several spurs from the environmental dimension were positively correlated with each other. Further, many of the environmental spurs were negatively correlated with spur profitability, which was positively correlated with spur social wellbeing and farm business resilience. A comparison of cluster-wise correlations showed a positive correlation between environmental spurs and spur profitability for large farms but a negative correlation for small farms. The diverging, yet complementary, correlation results from this study open the field to sustainable pathways that are capable of connecting realities with differing production systems and geographical areas while also incorporating their unique features. ARTICLE HISTORY Received 13 February 2025 Accepted 4 December 2025 KEYWORDS Sustainability assessment; Public Goods tool; environmental sustainability; social sustainability; economic sustainability 1. Introduction The Food and Agriculture Organization of the United Nations (FAO) (2014) defines sustainable development as a dynamic concept that encompasses a variety of principles belonging to three main dimensions: environmental, economic and social. Respecting these three dimensions is key to ensure the sustainability of a product, sector, or supply chain (Braun & Ghosh, 2020; Paraskevopoulou et al., 2020). At the same time, the phenomena characterized in recent decades, such as the exponential growth of the global population, climate change and the diminishing availability of natural resources, have directly contributed to the Supplemental data for this article can be accessed online at https://doi.org/10.1080/14735903.2025.2601486. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. CONTACT Elena Diaz Vicuna [email protected] University of Turin, Veterinary Sciences, Largo Paolo Braccini 2, Grugliasco, TO, 10095, Italy creation of a general climate of food insecurity, particularly affecting food products of animal origin (Foley et al., 2011; García-Díez et al., 2021; Stoddart, 2013). As a result, consumers' preferences for such products have evolved, led by increasing awareness of the environmental challenges linked to their production and growing concern over their contribution to climate change (Spada et al., 2024; Stranieri et al., 2022). In response to this situation, the need to enhance the sustainability of the food supply chain has emerged as an environmental, social and economic priority, affecting the scientific community, producers and consumers alike (Kiran et al., 2023; Leip et al., 2015; Lovarelli et al., 2020). These findings underline the need for industry and institutions to align with both supply-side improvements and demand-side expectations. In the European Union (EU), the legislative response to these events has been represented by the Farm2Fork Strategy along with the various reforms of the Common Agricultural Policy (CAP), which focuses on the promotion of a more sustainable livestock sector (Kiran et al., 2023). There is a general recognition that animal farming systems continually play a pivotal role in societies owing to their numerous and diverse functions, which are attributable to both monogastrics and ruminants. These range from transforming materials that are inedible to humans, such as grass and side streams (forage, crop residues and agricultural by-products that are generally unsuitable for human consumption), into nutrient-rich food to promote the vitality of rural territories, especially through the provision of economic inputs (Gerrard et al., 2012; Leroy et al., 2022; Upton, 2004). Workers within the agricultural sector account for approximately 4% of the total EU employment (Eurostat, 2022) and then there are also suppliers, service and industry based on primary production. Of the total output from agricultural activities in the EU, 40% is derived from animal production (Peyraud & MacLeod, 2020), and more than 60% of the total utilized agricultural area, including arable land and pastureland, is used as a food source for animals (Guyomard et al., 2021). Additionally, specific environmental and cultural benefits are linked to certain livestock production systems (Dumont et al., 2019), such as forage and pasture-based rearing, particularly when grazing unfertilised, semi-natural grasslands (Eriksson, 2022; European Environment Agency, 2020). The grazing process influences flora and fauna through a combination of mechanisms, including biting and trampling, removal of biomass and redistribution of nutrients. Grazing also affects temperature and light levels in the vegetation cover and at the soil surface. Together, these mechanisms lead to grazing-tolerant, low-growing and short-lived species of vascular plants becoming more common compared to unmanaged or fertilized land (Pykälä, 2005). These systems also enhance water and soil quality and maintain local cultural values and, ultimately, the landscape's own identity (Bele et al., 2018; Guyomard et al., 2021). However, it is imperative to address livestock's less favourable impacts, especially from the most intensive systems. The sector contributes to the pollution and exploitation of natural resources, often at the expense of humans (e.g. via feed and food competition) (Muscat et al., 2020). Indeed, EU animal farming is responsible for up to 80% of the agricultural sector's environmental footprint through its negative effects on water and air quality, biodiversity and soil acidification (Lai & Kumar, 2020; Leip et al., 2015; Lovarelli et al., 2019; Post et al., 2020; Reidsma et al., 2006; Yan et al., 2024; Zucali et al., 2020). To mitigate the environmental downsides of animal-derived food supply chains, various strategies have been proposed. Although approaches differ depending on species and production systems, major efforts are focused on improving productive and reproductive efficiency, parallel to a more efficient use of available resources (Caccialanza et al., 2023; Leinonen & Kyriazakis, 2016; Shurson & Kerr, 2023; Veltman et al., 2021). Furthermore, in recent years, the European livestock sector has undergone a process of re-evaluation, shifting from being viewed primarily as a major contributor to environmental degradation to being recognized as a potential driver of a sustainable circular bioeconomy. This change in perspective has been reinforced by the emergence of numerous innovative farms offering viable approaches for a more sustainable and competitive future (Reg. EU 2021/2115). In parallel to this, attention is growing towards the variety of public goods that these systems produce in addition to food, such as contributing to the vitality of rural territories, enriching the agricultural landscape and promoting biodiversity (Nigmann et al., 2018). Understanding the range of benefits and drawbacks related to these systems and their innovations from a sustainability perspective is crucial for the development of further policies and practices which are suitable for the diverse European landscape. At present, studies assessing on-farm sustainability are typically focused on a specific animal species or perform the evaluation from a single perspective only, most often the environmental one, with less attention to economic and social aspects (Arvidsson et al., 2020; Arvidsson et al., 2021; Di Vita et al., 2 E. D. VICUNA ET AL. 2024; Gunnarsson et al., 2020a, 2020b; Kheiralipour et al., 2024; Pahmeyer & Britz, 2022; Zira et al., 2021). Moving beyond isolated assessments and toward a more integrated understanding that is capable of considering multiple farms differing in terms of geographical location, animal category and production systems would facilitate the identification of interconnections among different dimensions of sustainability while also enabling meaningful comparisons across varied farm types. The employment of a multi-level framework that integrates environmental, economic and social perspectives is therefore essential (Galdeano-Gómez et al., 2017; Sulewski et al., 2018). In recent years, various sustainability assessment tools have been developed, aimed at measuring and monitoring sustainability using an integrated approach (Binder et al., 2010; Coteur et al., 2020). One of these evaluation protocols is the Public Goods (PG) tool (Marta-Costa & Silva, 2013). In the present study, the PG tool assessment was applied for the first time to a range of innovative livestock farming systems in Europe to provide an analysis of the underlying relations among different sustainability aspects and to answer the following research question: ‘How do the environmental, economic and social pillars of sustainability interact across innovative European livestock farming systems, and are these patterns consistent across them?’. 2. Materials and methods 2.1 Public goods (PG) tool The PG tool is a Microsoft Excel-based form that was initially developed by Gerrard et al. (2012) to analyze the contribution of public goods from organic dairy farms in England. It is characterized by a vast coverage of sustainability criteria and a highly versatile protocol that is easily adaptable to best suit a specific study design (Marchand et al., 2014). These technical reasons, together with the presence of team members with extensive expertise in the tool, guided its selection and facilitated its adaptation to the needs of the present study. The adaptation of the original protocol was driven by three necessities. First, it was necessary to shift the aim of the data collection to a more sustainability-focused study rather than the delivery of public goods. Each farm's sustainability evaluation had to be conducted through the analysis of its environmental, economic and social dimensions, in accordance with the FAO (2014) definition of a ‘sustainable food value chain’. Second, the version used in this study required a collection of comparable data from a broader and more diverse sample in terms of animal species and country-specific characteristics aside from just organic dairy farms. Third, the use of literature from the last decade underpinning the assessment should be enabled. Adaptation of the tool and education on its functions were conducted in an interactive process with the persons who were to be responsible for the data collection. Experts in different areas were contacted with direct questions. Most modifications concerned economic data, social issues and agrienvironmental management, and these are reported in Table S1 (Supplementary material). Prior to the sustainability analysis, production and structural data (farm dimensions, presence and dimensions of crops, and stocking rates) were collected. Importantly, the stocking rate was considered at the farm level (LU/ha), hence referring to the total number of animals, expressed in livestock units (LU), per hectare of utilized agricultural area (UAA) of the entire farm. An evaluation of environmental, economic and social sustainability was performed using a total of 12 spurs (indicators): • Environmental sustainability: agri-environmental management, landscape and heritage, soil management, water management, manure and fertilizer, the NPK budget and energy and carbon. • Economic sustainability: Profitability, farm business resilience, system security and diversity. • Social sustainability: Animal welfare and social well-being. Each spur contained three to six activities (for a total of 52), which, in turn, were composed of three to six questions. Each question was assigned a score ranging from 1 (poor sustainability) to 5 (excellent sustainability) based on the answers provided by the respondent. There were questions with quantitative INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 3 answers retrieved from farm recordings and questions with semi-quantitative and qualitative answers designed as multiple-choice questions with a list of options. For questions identified as ambiguous, an explanatory box was added. Additionally, to ensure comparability across the data collected from such diverse farm types (dairy, beef, pork and poultry), the PG tool was adapted to account for species-specific management practices. This included the incorporation of a ‘not applicable’ (n/a) option for questions not relevant to certain livestock types (e.g. milking routines for monogastrics), thereby avoiding artificial score inflation or deflation. The core structure of the tool remained consistent across farms, enabling a common sustainability assessment framework while accommodating key structural differences. These adaptations can be found in the PG tool file uploaded in the data repository (https://doi.org/10.5281/zenodo.17709094) and were implemented in collaboration with field experts during the tool refinement phase. The arithmetical mean of the scores for the questions within an activity composed the score for that activity, rounded off to the nearest integer. Thus, an activity consisting of several questions was not weighted more heavily than one requiring only one question. In turn, the arithmetic mean of the scores for the activities within a spur composed the summarised score for that specific spur. All the evaluated parameters (questions, activities and spurs) were organised in an Excel file workbook, used to conduct the interviews, and structured with a sheet per spur each with its related questions and activities. The scoring methodologies for each spur and references for the scoring are reported in Table S1 (Supplementary material), and a description of the spurs' reliance on quantitative data is reported in Table S2 (Supplementary material). 2.2 Farms and data collection The data collection process involved interviews with farmers belonging to a total of 106 farms and was divided into 13 groups, each organized around a theme of innovation in practice and located in nine different European countries. Each group was identified as a practice Hub (PH). The PHs were selected to cover the four types of innovation defined by the Organisation for Economic Cooperation and Development (OECD): product, processing, marketing and organization (Gault, 2018), which were applied in the context of animal farming. The study sample was therefore characterized by a broad range of existing innovations on production, marketing and organisation levels, intensive to extensive systems and mainstream to niche products. Furthermore, the PHs contained different animal species, including farm operations with dairy cattle, beef cattle, pigs and poultry for meat and eggs (Table 1; List S3, Supplementary material). The number of farms per PH varied owing to the availability of farms and heterogeneity within each PH. To apply the PG tool evaluation, an interview was carried out with one or two representatives of each farm (i.e. owners, farm managers). The interviews were conducted by one or two trained researchers. Each interview Table 1. Description of practice hubs (PHs) with innovative European livestock farms where sustainability data was collected, country of residence, breed of main livestock species, description of innovation and number of farms surveyed. PH 7 did not complete the data collection and was therefore not included. PH NO. Country Species Innovation Farms 1 Germany Dairy 100% pasture-fed cow-calf dairy systems 10 2 France Dairy Management for maximisation of C sequestration in pasture 5 3 Romania Dairy Dairy with agroforestry aiming for self-sufficiency in protein-based feed 10 4 Sweden Dairy More-from-less dairy systems utilising on-farm advice and carbon footprinting tool 7 5 France Pig Conventional production utilising manure for biogas 5 6 Denmark Pig Organic farmers utilising ‘green-protein’ from grass/clover working with feed company and refinery 3 8 Netherlands Pig Conventional pig production with innovative flooring (solid floor with a layer of material for rooting for all ages) and a focus on biodiversity 4 9 United Kingdom Beef 100% pasture-fed beef systems utilising mob grazing, herbal leys and mobile slaughterhouses and Community Supported Agriculture 19 10 Italy Beef New breeding methodology for ‘mountain pasture’ with own certification/label development 6 11 Sweden Beef Quality assured (IP SIGILL) production system for beef and sheep on HNV semi-natural pastures together with label development 10 12 Poland Poultry Agri-tech innovation for improved welfare 11 13 France Poultry Farmer co-operative producing and sharing of compost from plant-based litter 13 14 Netherlands Poultry Closed-loop egg production feeding food processing waste and recycling manure 3 4 E. D. VICUNA ET AL. lasted four to eight hours and was conducted either in person or remotely from December 2022 to May 2023. All the subjects involved provided written informed consent and were assured of confidentiality and anonymity. 2.3 Statistical analysis All the analyses were performed using R statistical software (v4.3.0; R Core Team 2023). To classify the diverse range of farms into homogeneous groups based on the spurs' results, a heatmap cluster analysis of the farms was performed on scaled data using the ‘ComplexHeatmap’ package (Gu et al., 2016; Gu, 2022), which applies hierarchical clustering by default, using complete linkage and Euclidean distance. This method was preferred over alternative approaches, such as k-means, as it does not require pre-specifying the number of clusters and can better accommodate unbalanced group sizes, which aligns with the heterogeneous nature of the dataset (e.g. different species, innovation types and geographical contexts) (Wani, 2024). Given the exploratory aim of classifying farms based on a novel scoring system (the ‘spurs’), hierarchical clustering allows for the assessment and visualization of the nested similarity structure among farms without imposing assumptions on cluster shape or count while also providing an intuitive representation of relationships via the heatmap. Subsequently, Kruskal‒Wallis analysis and post hoc analysis, including Benjamini‒Hochberg's correction, were performed using the ‘agricolae’ package (de Mendiburu, 2023) to determine whether differences in spur scores between the identified clusters were statistically significant. The results were plotted using the ‘ggplot2’ package (Wickham et al., 2016). Within each cluster and across all farms together, a Spearman correlation analysis using the rcorr function from the ‘Hmisc’ package (Harrell & Dupont, 2024) was performed to measure both the strength and direction of association between the spurs, following confirmation that all the variable pairs met the monotonicity assumption. Correlations found to be statistically significant (p-value < 0.05) were further investigated by identifying the questions leading the correlation based on the relative weight of each question in the spur. Additionally, the interquartile range (IQR) and median of the scores of each question were calculated. 3. Results 3.1 Descriptive data Figure 1 illustrates the average, median and range of values for each spur across all farms. Considering the average, Profitability was reported with the lowest scores, whereas Soil management presented the highest scores. The heatmap analysis allowed for the identification of five distinct clusters (Figure 2), described in both Tables 2 and S4 (Supplementary material). Cluster 1 was a mixed cluster with both monogastric and ruminant farms, with a majority of monogastric (69%), medium-sized farms, and a medium stocking rate expressed as livestock units per farm hectare (LU/ha). This cluster represented all four animal types and seven countries and displayed the highest Profitability score among all the clusters. Conversely, the majority of the environmental spurs reported lower-than-average scores, with the exception of Manure management, which, together with Animal welfare, Social wellbeing and Farm business resilience, presented higher-than-average scores. Cluster 2 was an exclusively monogastric cluster characterized by a small farm size and very high stocking rate, with the French broiler farms from PH13 comprising 57% of the cluster. It reached the lowest scores in seven spurs (Agri-environmental management, Landscape and heritage, Water management, NPK budget, Energy and carbon, Animal welfare and System security and diversity) but higher-than-average scores for Profitability and Soil management. Cluster 3 was a solely ruminant cluster, predominantly consisting of dairy farms, characterized by a small farm size and very low stocking rate. The cluster was dominated by the Romanian dairy farms from PH3, which represented 60% of the farms in the cluster. The lowest scores were reached for six spurs (Soil, Water and Manure management, Social wellbeing, Profitability, and Farm business resilience) but presented higher than average performances for Agri-environmental management, Landscape and heritage, NPK budget and Energy and carbon. INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 5 Cluster 4 was a ruminant-dominated cluster, with a minor share of poultry farms. It was characterized by an average large farm size and low stocking rate and was represented by farms from seven countries. This cluster reached higher scores than the other clusters for eight spurs, Agri-environmental management, Landscape and heritage, Water management, Manure and fertilizer, NPK budget, Animal welfare, Social wellbeing, and Farm business resilience and a higher-than-average score for all spurs. Finally, cluster 5 was a ruminant-dominated cluster with a minor share of poultry farms, characterized by a medium farm size and a low stocking rate. British (PH10) and Swedish (PH11) beef farms and German dairy farms (PH1) comprised the largest share. The highest scores were reached for three spurs (Soil management, Energy and carbon and System security and diversity), and higher-than-average scores were obtained for Agri-environmental management, Landscape and heritage, Water management, Manure and fertilizer, NPK budget, Animal welfare and Social wellbeing. Lower-than-average scores were observed for the two remaining spurs, Profitability and Farm business resilience. The analysis showed significant differences in sustainability performance between the clusters (Supplementary material, Table S5). For example, clusters 1 and 2 had lower scores for the spurs Agrienvironmental management, Landscape and heritage and NPK budget compared to clusters 3, 4 and 5; however, cluster 1 presented a higher score for Manure and Fertilizer, and cluster 2 for Soil management. 3.2 Correlations Across all investigated farms, there were 33 significant correlations among the different spurs, out of 66 theoretically possible ones (Table 3, column all; supplementary material, Table S6). Spurs related to environmental sustainability presented seven correlations with spurs of Social sustainability, six of which Figure 1. Average, median and range (min & max) for each of the 12 sustainability spurs analysed with the Public Goods tool (1 is poor sustainability, 5 is excellent sustainability) for 106 European innovative livestock farms. 6 E. D. VICUNA ET AL. were positive correlations. Spurs related to environmental sustainability also presented eleven correlations with spurs of economic sustainability, of which six were positive correlations. Economic spurs presented three positive correlations with the social ones. Out of the 33 spur‒spur correlations reported across all farms, a total of 31 were also observed in at least one of the five clusters (Table 3; Supplementary material, Tables S6–S11). In 21 of these cases, the clusters' correlations were inconsistent with the same correlations across all farms, being positive within clusters while negative across all farms or vice versa. Nine of them also showed inconsistency across clusters. Inconsistency across clusters was also present in a further five correlations that lacked a corresponding Table 2. Characteristics of the five clusters composed of European innovative livestock farms, where allocation was based on the sustainability scores of the farms. Farm size indicates the utilised agricultural area, ha is hectare and a livestock unit (LU) is standardised according to Eurostat ( 2024). Cluster Livestock type (% and number of farms) Farm size (ha) Stocking rate (LU/ha) Dairy (%) Dairy (n) Beef (%) Beef (n) Pork (%) Pork (n) Poultry (%) Poultry (n) Mean Median Mean Median 1 21 4 11 2 32 6 37 7 147.5 85.0 9.2 1.3 2 – – – – 26 6 74 17 36.3 20.0 78.3 15.6 3 67 10 33 5 – – – – 49.0 24.2 0.5 0.5 4 32 7 64 14 – – 5 1 552.4 141.3 0.8 0.5 5 41 11 52 14 – – 7 2 152.8 107.0 0.8 0.5 Figure 2. Heatmap of standardised values (spur mean is 0, variance is 1) of the different spur scores; each column identifies a single spur, each row identifies a single farm, and each cell represents a specific farm's spur score. The colour intensity of the cells across a column reflects the distance from the mean of the spur, where the light colour is a short distance and the dark colour is a long distance. Grey identifies n/a. INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 7 Table 3. Graphical representation of correlations between 12 sustainability spurs of environmental, economic and social dimensions, based on PG tool data across all the 106 European livestock farms studied (All) and cluster wise (1–5). For significant correlations only, Spearman's correlation coefficient value is reported; strength of correlation is illustrated by *(p-value ranging between 0.05 and 0.01), **(p-value ranging between 0.01 and 0.001) and ***(p-values lower than 0.001). Details can be found in Supplementary materials, Tables S6–11. Spur 1 Spur 2 All 1 2 3 4 5 Agri-environmental management Landscape & heritage 0.53** −0.15** Soil management −0.09** −0.46** Water management 0.49** 0.63*** Manure & fertiliser −0.46** 0.02* NPK budget 0.75** 0.17* Energy & carbon 0.55** −0.37* Landscape & heritage Soil management −0.03* −0.44** −0.22* 0.22* Water management −0.45* Manure & fertiliser 0.37*** −0.33** NPK budget 0.50*** −0.25* −0.04** Energy & carbon 0.38** −0.24** Soil management Water management 0.47* Manure & fertiliser −0.10* NPK budget −0.23** −0.31** −0.45*** Energy & carbon −0.09** 0.22** −0.17* Water management Manure & fertiliser −0.49* −0.27* −0.41* NPK budget 0.36** Energy & carbon 0.56*** Manure & fertiliser NPK budget Energy & carbon 0.68* 0.57** NPK budget Energy & carbon 0.47*** −0.58** −0.45** 0.45** Animal welfare Social wellbeing 0.41* System security & diversity Profitability −0.23*** Farm business resilience Profitability Farm business resilience 0.29** 0.58*** Animal welfare Agri-environmental management 0.46* −0.47*** −0.28** Landscape & heritage 0.53*** 0.50* 0.14* Soil management −0.17* −0.83*** −0.25* 0.24* Water management −0.56*** −0.40** Manure & fertiliser 0.34* NPK budget 0.37* −0.40* Energy & carbon 0.36* −0.34** Social wellbeing Agri-environmental management 0.56** Landscape & heritage 0.17* Soil management 0.25* −0.38* −0.47** Water management Manure & fertiliser 0.57* −0.53*** 0.60*** NPK budget 0.39** Energy & carbon 0.43* −0.67** System security & diversity Agri-environmental management 0.52*** −0.31* Landscape & heritage 0.56*** 0.54** Soil management −0.13** −0.40** Water management −0.39** Manure & fertiliser −0.09* 0.54** NPK budget 0.55*** Energy & carbon 0.47*** −0.43* Profitability Agri-environmental management −0.22** −0.31* 0.58** Landscape &heritage −0.21** −0.20* −0.43* 0.31* Soil management <0.01* −0.50* Water management 0.06* 0.59*** 0.54* Manure & fertiliser −0.24* NPK budget −0.29*** −0.22* 0.58*** −0.41* Energy & carbon −0.11** −0.40* Farm business resilience Agri-environmental management 0.89*** Landscape & heritage 0.29** Soil management 0.35*** Water management 0.31* 0.76*** Manure & fertiliser 0.42* −0.52** 0.24* NPK budget −0.08* 0.21* −0.37*** Energy & carbon −0.42* −0.18** System security & diversity Animal welfare 0.42 ** 0.52*** Social wellbeing 0.36* Profitability Animal welfare <0.01** −0.32* 0.55** Social wellbeing 0.18** −0.53*** 0.50* Farm business resilience Animal welfare −0.41** Social wellbeing 0.56* 0.47** 0.46** 8 E. D. VICUNA ET AL. Taken together, our study demonstrates that using a single questionnaire for farms with such differences in livestock species, geographic conditions and sustainability focuses can be challenging. Nonetheless, although no universal key can unlock a sustainable future for all types of animal farming, understanding the high heterogeneity of realities shaping the sector, combined with the involvement of the actors currently leading the transition, represents a crucial first step towards a more environmentally, economically and socially sustainable livestock sector. Acknowledgements All farmers are gratefully acknowledged for their time contributions and their sharing of data and thoughts. Helene Chambaut, Majken Husted, Julie Cherono Schmidt Henriksen, Mariël Benus, Denis Hahn, Jenna Thompson, Elisabeth Rowe, Emanuela Tullo, Erika Frigo, Emanuele Ferri, Vincent Blazy Anna and Ian Jamieson are also acknowledged, as they, in addition to the authors, provided valuable inputs and/or collected data. Author contributions E. Diaz Vicuna: Investigation, Data curation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing, N. Adams: Data curation, Formal analysis, Investigation, Visualization, Writing – review & editing, L.G. Smith: Data curation, Investigation, Writing – review & editing, Funding acquisition, M. Durand, C. van Wagenberg, E. Caron, A. van den Pol-van Dasselaar, L. Baumgart, S. Schetelat, M. Sandor, A. Gliga, S. Espagnol, H. M-L. Andersen, R. Borek, P. Jurga, N. van Leuffen, J.L.T. Heerkens, J. Yngvesson, A. Oosterwijk, J. Stokes, C. Forte: Investigation, Data curation, Writing – review & editing, A. Hessle: Conceptualization, Validation, Investigation, Data curation, Writing – review & editing, Supervision, Project administration. All authors agree to be accountable for all aspects of the work. Disclosure statement No potential conflict of interest was reported by the author(s). Funding The study was funded by the H2020 research programme on Food Security Sustainable Agriculture and Forestry Marine Maritime and Inland Water Research and the Bioeconomy through a project entitled ‘PATHWAYS’ (Grant Agreement No. 101000395). The funders had no role in the design of the study, collection, analyses, or interpretation of the data, nor in the writing of the manuscript. ORCID Elena Diaz Vicuna 0000-0002-0197-518X Nina Adams 0000-0001-5699-7063 Laurence Smith 0000-0002-9898-9288 Coen van Wagenberg 0000-0002-8813-3742 Agnes van den Pol-van Dasselaar 0000-0002-9070-9704 Lukas Baumgart 0000-0002-5858-5123 Mignon Sandor 0000-0002-2007-992X Robert Borek 0000-0001-9414-3181 Piotr Jurga 0000-0002-9188-1565 Jenny Yngvesson 0000-0003-3123-2229 Annabel Oosterwijk 0009-0004-5406-4189 Claudio Forte 0000-0002-0060-3851 Anna Hessle 0000-0002-5195-1186 Data availability statement The data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.1 7709094. INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 15 Ethics statement This research was conducted in accordance with the ethical guidelines established by the Coordination Team with input from an independent Ethics Advisor for the H2020 PATHWAYS project (grant agreement No 101000395). Through this process, we adhered to the principles outlined in the European Convention on Human Rights and the European Charter of Fundamental Rights to ensure ethically and socially responsible research and dissemination methods, particularly concerning informed consent and data anonymization. Informed consent was obtained through a written formal consent form provided in advance of data collection, which outlined the study purpose, voluntary participation, data handling, anonymization and withdrawal rights. References Adams, N., Sans, A., Trier Kreutzfeldt, K. E., Arias Escobar, M. A., Oudshoorn, F. W., Bolduc, N., Aubert, P. M., & Smith, L. G. (2024). Assessing the impacts of EU agricultural policies on the sustainability of the livestock sector: A review of the recent literature. Agriculture and Human Values, 42, 1–20. https://doi.org/10.1007/s10460-024-10595-y Åkerfeldt, M. P., Gunnarsson, S., Bernes, G., & Blanco-Penedo, I. (2021). Health and welfare in organic livestock production systems—A systematic mapping of current knowledge. Organic Agriculture, 11(1), 105–132. https://doi.org/10.1007/ s13165-020-00334-y Arvidsson, S. K., Hansson, H., Sonesson, U., & Gunnarsson, S. (2020). Research on environmental, economic, and social sustainability in dairy farming: A systematic mapping of current literature. Sustainability, 12(14), 5502. https://doi.org/ 10.3390/su12145502 Arvidsson, S. K., Hansson, H., Sonesson, U., & Gunnarsson, S. (2021). A systematic mapping of current literature on sustainability at farm-level in beef and lamb meat production. Sustainability, 13(5), 2488. https://doi.org/10.3390/ su13052488 Bele, B., Norderhaug, A., & Sickel, H. (2018). Localized agri-food systems and biodiversity. Agriculture, 8(2), 22. https:// doi.org/10.3390/agriculture8020022 Binder, C. R., Feola, G., & Steinberger, J. K. (2010). Considering the normative, systemic and procedural dimensions in indicator-based sustainability assessments in agriculture. Environmental Impact Assessment Review, 30(2), 71–81. https://doi.org/10.1016/j.eiar.2009.06.002 Blagojevic, B., Nesbakken, T., Alvseike, O., Vågsholm, I., Antic, D., Johler, S., Houf, K., Meemken, D., Nastasijevic, I., Vieira-Pinto, Madalena, Antunovic, B., Georgiev, M., & Alban, L. (2021). Drivers, opportunities, and challenges of the European risk-based meat safety assurance system. Food Control, 124. 107870. https://doi.org/10.1016/j.foodcont.2021.107870 Braun, G., & Ghosh, K. (2020). Transforming food and agriculture to achieve the sustainable development goals (SDGs): Good practices from FAO/GEF projects around the world. FAO. https://doi.org/10.4060/ca8768en Caccialanza, A., Cerrato, D., & Galli, D. (2023). Sustainability practices and challenges in the meat supply chain: A systematic literature review. British Food Journal, 125(12), 4470–4497. https://doi.org/10.1108/BFJ-10-2022-0866 Coteur, I., Wustenberghs, H., Debruyne, L., Lauwers, L., & Marchand, F. (2020). How do current sustainability assessment tools support farmers' strategic decision making? Ecological Indicators, 114. 106298. https://doi.org/10.1016/ j.ecolind.2020.106298 Council Directive 2007/43/EC of 28 June 2007 laying down minimum rules for the protection of chickens kept for meat production. (2019). http://data.europa.eu/eli/EFSA Journal, 2021/2007/43/2019-12-14 Council Directive 2008/120/EC of 18 December 2008 laying down minimum standards for the protection of pigs. (2019). http://data.europa.eu/eli/dir/2008/120/2019-12-14 Cradock-Henry, N. A. (2021). Linking the social, economic, and agroecological: A resilience framework for dairy farming. Ecology and Society, 26(1), 3. https://doi.org/10.5751/ES-12122-260103 Dawkins, M. S. (2016). Animal welfare and efficient farming: Is conflict inevitable? Animal Production Science, 57(2), 201–208. https://doi.org/10.1071/AN15383 de Mendiburu, F. (2023). Agricolae: Statistical procedures for agricultural research (version 1.3-7). R Package. de Vos, C. J., & Elbers, A. R. (2024). Quantitative risk assessment of wind-supported transmission of highly pathogenic avian influenza virus to Dutch poultry farms via fecal particles from infected wild birds in the environment. Pathogens, 13(7), 571. https://doi.org/10.3390/pathogens13070571 Di Vita, G., Zanchini, R., De Cianni, R., Pippinato, L., Mancuso, T., & Brun, F. (2024). Sustainable livestock farming in the European Union: A study on beef farms in NUTS 2 regions. Sustainability, 16(3), 1098. https://doi.org/10.3390/ su16031098 Dumont, B., Ryschawy, J., Duru, M., Benoit, M., Chatellier, V., Delaby, L., Donnars, C., Dupraz, P., Lemauviel-Lavenant, S., Méda, B., Vollet, D., & Sabatier, R. (2019). Associations among goods, impacts and ecosystem services provided by livestock farming. Animal, 13(8), 1773–1784. https://doi.org/10.1017/S1751731118002586 Dupas, M. C., Pinotti, F., Joshi, C., Joshi, M., Thanapongtharm, W., Dhingra, M., Blake, D., Tomley, F., Gilbert, M., & Fournié, G. (2024). Spatial distribution of poultry farms using point pattern modelling: A method to address livestock environmental impacts and disease transmission risks. PLoS Computational Biology, 20(10), e1011980. https:// doi.org/10.1371/journal.pcbi.1011980 16 E. D. VICUNA ET AL. EFSA AHAW Panel (EFSA Panel on Animal Health and Welfare)., Nielsen, S. S., Alvarez, J., Bicout, D. J., Calistri, P., Canali, E., Drewe, J. A., Garin-Bastuji, B., Gonzales Rojas, J. L., Herskin, M., Miranda Chueca, M. A., Michel, V., Padalino, B., Pasquali, P., Roberts, H. C., Sihvonen, L. H., Spoolder, H., Stahl, K., Velarde, A., … Schmidt, C. G. (2021). African swine fever and outdoor farming of pigs. EFSA Journal, 19(6), e06639. https://doi.org/10.2903/j.efsa.2021.6639 Elahi, E., Weijun, C., Jha, S. K., & Zhang, H. (2019). Estimation of realistic renewable and non-renewable energy use targets for livestock production systems utilising an artificial neural network method: A step towards livestock sustainability. Energy, 183, 191–204. https://doi.org/10.1016/j.energy.2019.06.084 Eriksson, O. (2021). The importance of traditional agricultural landscapes for preventing species extinctions. Biodiversity and Conservation, 30(5), 1341–1357. https://doi.org/10.1007/s10531-021-02145-3 Eriksson, O. (2022). Coproduction of food, cultural heritage and biodiversity by livestock grazing in Swedish semi-natural grasslands. Frontiers in Sustainable Food Systems, 6. 801327. https://doi.org/10.3389/fsufs.2022.801327 European Union. Commission Implementing Regulation (EU) 2020/464 of 26 March 2020 laying down certain rules for the application of Regulation (EU) 2018/848 of the European Parliament and of the Council as regards the documents needed for the retroactive recognition of periods for the purpose of conversion, the production of organic products and information to be provided by Member States. (2021). http://data.europa.eu/eli/reg_impl/2020/464/2021-11-25 European Environment Agency. (2020). State of the nature in EU (Report 10) Estimation of Annex I habitats' areas to be improved and area to be recreated per region. Retrieved November 22, 2024, from Available at https:// www.eea.europa.eu/publications/state-of-nature-in-the-eu-2020 European Environment Agency. (2024). Explore nature reporting data: Estimation of Annex I habitats' areas to be improved and area to be recreated per region. Retrieved November 22, 2024, from https://tableaupublic.discomap.eea.europa.eu/views/SONrestoration/Story1/d14f11e7-687d-4ccf-acd2-18f374b89c44/a5301d17b660-4996-8792-bd4858ecf538?%3Adisplay_count=n&%3AshowVizHome=n Eurostat. (2022). Farmers and the agricultural labour force – statistics. Eurostat. Retrieved February 20, 2024, from. https:// ec.europa.eu/Eurostat/statistics-explained/index.php?title=Farmers_and_the_agricultural_labour_force_-_statistics. Eurostat. (2024). Glossary: Livestock unit (LSU). Retrieved November 19, 2024, from https://ec.europa.eu/eurostat/ statistics-explained/index.php?title=Glossary:Livestock_unit_(LSU) FAO. (2014). Developing sustainable food value chains – Guiding principles. Ferreira, V. H. B., Simoni, A., Germain, K., Leterrier, C., Lansade, L., Collin, A., Mignon-Grasteau, S., Le Bihan-Duval, E., Guettier, E., Leruste, H., Løvlie, H., Calandreau, L., & Guesdon, V. (2022). Foraging behavior shows individual consistency over time, and predicts range use in slow-growing free-range male broiler chickens. Frontiers in Veterinary Science, 9. 814054. https://doi.org/10.3389/fvets.2022.814054 Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O'Connell, C., Ray, D. K., West, P. C., Balzer, C., Bennett, E. M., Carpenter, S. R., Hill, J., Monfreda, C., Polasky, S., Rockström, J., Sheehan, J., Siebert, S., … Zaks, D. P. M. (2011). Solutions for a cultivated planet. Nature, 478(7369), 337–342. https://doi.org/ 10.1038/nature10452 Fraser, M. D., Vallin, H. E., & Roberts, B. P. (2022). Animal board invited review: Grassland-based livestock farming and biodiversity. Animal, 16(12), 100671. https://doi.org/10.1016/j.animal.2022.100671 Galdeano-Gómez, E., Aznar-Sánchez, J. A., Pérez-Mesa, J. C., & Piedra-Muñoz, L. (2017). Exploring synergies among agricultural sustainability dimensions: An empirical study on farming systems in Almería (Southeast Spain). Ecological Economics, 140, 99–109. https://doi.org/10.1016/j.ecolecon.2017.05.001 García-Díez, J., Gonçalves, C., Grispoldi, L., Cenci-Goga, B., & Saraiva, C. (2021). Determining food stability to achieve food security. Sustainability, 13(13), 7222. https://doi.org/10.3390/su13137222 Gault, F. (2018). Defining and measuring innovation in all sectors of the economy. Research Policy, 47, 617–622. https:// doi.org/10.1016/j.respol.2018.01.007 Gerrard, C. L., Smith, L. G., Pearce, B., Padel, S., Hitchings, R., Measures, M., & Cooper, N. (2012). Public goods and farming. In E. Lichtfouse (Ed.), Farming for food and water security (sustainable agriculture reviews) (Vol. 10, pp. 1–22). Springer. https://doi.org/10.1007/978-94-007-4500-1_1 Grove, R., Evans Pim, J., Serrano, M., Cidrás, D., Viles, H., & Sanmartín, P. (2020). Pastoral stone enclosures as biological cultural heritage: Galician and Cornish examples of community conservation. Land, 9(1), 9. https://doi.org/10.3390/ land9010009 Gu, Z. (2022). Complex heatmap visualization. iMeta, 1(3), e43. https://doi.org/10.1002/imt2.43 Gu, Z., Eils, R., & Schlesner, M. (2016). Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics, 32(18), 2847–2849. https://doi.org/10.1093/bioinformatics/btw313 Gunnarsson, S., Arvidsson, S. K., Göransson, L., Hansson, H., & Sonesson, U. (2020a). Systematic mapping of research on farmlevel sustainability in egg and chicken meat production. Sustainability, 12, 3033. https://doi.org/10.3390/su12073033 Gunnarsson, S., Arvidsson, S. K., Wallgren, T., Hansson, H., & Sonesson, U. (2020b). A systematic mapping of research on sustainability dimensions at farm-level in pig production. Sustainability, 12, 4352. https://doi.org/10.3390/su12114352 Guyomard, H., Bouamra-Mechemache, Z., Chatellier, V., Delaby, L., Détang-Dessendre, C., Peyraud, J. L., & Requillart, V. (2021). Why and how to regulate animal production and consumption: The case of the European Union. Animal, 15, 100283. https://doi.org/10.1016/j.animal.2021.100283 Harrell, F. E., Jr., & Dupont, C. (2024). HMISC: Harrell miscellaneous. R package version 4.0. Retrieved February 13, 2024, from https://hbiostat.org/R/Hmisc/ INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 17 Henryson, K., Meurer, K. H., Bolinder, M. A., Kätterer, T., & Tidåker, P. (2022). Higher carbon sequestration on Swedish dairy farms compared with other farm types as revealed by national soil inventories. Carbon Management, 13(1), 266–278. https://doi.org/10.1080/17583004.2022.2074315 Hessle, A., & Danielsson, R. (2024). Cattle population required for favorable conservation status of managementdependent semi-natural grasslands and forests, and associated increase in enteric methane emissions. Journal for Nature Conservation, 78. 126571. https://doi.org/10.1016/j.jnc.2024.126571 Kheiralipour, K., Rafiee, S., Karimi, M., Nadimi, M., & Paliwal, J. (2024). The environmental impacts of commercial poultry production systems using life cycle assessment: A review. World's Poultry Science Journal, 80(1), 33–54. https:// doi.org/10.1080/00439339.2023.2250326 Kiran, B. R., Prasad, M. N. V., & Mohan, S. V. (2023). Farm to fork: Sustainable agrifood systems. In Sustainable and circular management of resources and waste towards a green deal (pp. 25–38). Elsevier. https://doi.org/10.1016/B978-0-32395278-1.00012-7 Knickel, K., Redman, M., Darnhofer, I., Ashkenazy, A., Chebach, T. C., Šūmane, S., Tisenkopfs, T., Zemeckis, R., Atkociuniene, V., Rivera, M., Strauss, A., Kristensen, L. S., Schiller, S., Koopmans, M. E., & Rogge, E. (2018). Between aspirations and reality: Making farming, food systems and rural areas more resilient, sustainable and equitable. Journal of Rural Studies, 59, 197–210. https://doi.org/10.1016/j.jrurstud.2017.04.012 Lai, L., & Kumar, S. (2020). A global meta-analysis of livestock grazing impacts on soil properties. PLoS One, 15(8), e0236638. https://doi.org/10.1371/journal.pone.0236638 Leinonen, I., & Kyriazakis, I. (2016). How can we improve the environmental sustainability of poultry production? Proceedings of the Nutrition Society, 75(3), 265–273. https://doi.org/10.1017/s0029665116000094 Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. A., de Vries, W., & Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication, and biodiversity. Environmental Research Letters, 10(11), 115004. https://doi.org/ 10.1088/1748-9326/10/11/115004 Leroy, F., Abraini, F., Beal, T., Dominguez-Salas, P., Gregorini, P., Manzano, P., Rowntree, J., & Van Vliet, S. (2022). Animal board invited review: Animal source foods in healthy, sustainable, and ethical diets – An argument against drastic limitation of livestock in the food system. Animal, 16(3), 100457. https://doi.org/10.1016/j.animal.2022.100457 LNV. (2022). Ontwikkeldocument Nationaal Programma Landelijk Gebied. Ministerie van Landbouw. Natuur en Voedselkwaliteit. Retrieved January 8, 2025, from Available at: https://www.rijksoverheid.nl/documenten/publicaties/2 022/11/25/ontwikkeldocument-nationaal-programma-landelijk-gebied LNV. (2023). Regeling van de Minister voor Natuur en Stikstof, nr. WJZ/27312934, houdende regels voor de verstrekking van subsidie voor het sluiten van veehouderijlocaties met piekbelasting op natuurgebieden (Landelijke beëindigingsregeling veehouderijlocaties met piekbelasting). Staatscourant nr, 15029. Retrieved January 13, 2025, fromhttps://zoek.officielebekendmakingen.nl/stcrt-2023-15029.pdf Lovarelli, D., Bacenetti, J., & Guarino, M. (2020). A review on dairy cattle farming: Is precision livestock farming the compromise for an environmental, economic and social sustainable production? Journal of Cleaner Production, 262, 121409. https://doi.org/10.1016/j.jclepro.2020.121409 Lovarelli, D., Bava, L., Zucali, M., D'Imporzano, G., Adani, F., Tamburini, A., & Sandrucci, A. (2019). Improvements to dairy farms for environmental sustainability in Grana Padano and Parmigiano Reggiano production systems. Italian Journal of Animal Science, 18(1), 1035–1048. https://doi.org/10.1080/1828051X.2019.1651452 LRF. (2023). Costs of the green transition of agriculture: A Swedish example. An analysis from the federation of Swedish farmers regarding the total costs of the green transition in agriculture. Retrieved January 8, 2025, from Available at: https://www.lrf.se/nyheter/ny-rapport-fran-lrf-20-miljarder-for-att-stalla-om-sveriges-jordbruk/ Maes, D., Kyriazakis, I., Chantziaras, I., Van Limbergen, T., & Christensen, J. P. (2021). Bacterial diseases in pigs and poultry: Occurrence, epidemiology, and biosecurity measures. In Advancements and technologies in pig and poultry bacterial disease control (pp. 25–51). Academic Press. https://doi.org/10.1016/B978-0-12-818030-3.00001-5 Magnusson, U., Boqvist, S., Doyle, R., & Robinson, T. (2022). Animal health and welfare for sustainable livestock systems. Global Agenda for Sustainable Livestock. Retrieved December 23, 2024, fromhttps://hdl.handle.net/10568/125970 Marchand, F., Debruyne, L., Triste, L., Gerrard, C., Padel, S., & Lauwers, L. (2014). Key characteristics for tool choice in indicator-based sustainability assessment at farm level. Ecology and Society, 19(3):46. https://doi.org/10.5751/ES06265-190319 Marta-Costa, A. A., & Silva, E. (2013). Approaches for sustainable farming systems assessment. In A. Marta-Costa, & E. Soares da Silva (Eds.), Methods and procedures for building sustainable farming systems (pp. 21–29) (pp. 21–29). Springer. https://doi.org/10.1007/978-94-007-5003-6_3 Martin, G., Barth, K., Benoit, M., Brock, C., Destruel, M., Dumont, B., Grillot, M., Hübner, S., Magne, M. -A., Moerman, M., Mosnier, C., Parsons, D., Ronchi, B., Schanz, L., Steinmetz, L., Werne, S., Winckler, C., & Primi, R. (2020). Potential of multi-species livestock farming to improve the sustainability of livestock farms: A review. Agricultural Systems, 181. 102821. https://doi.org/10.1016/j.agsy.2020.102821 McAllister, T. A., Stanford, K., Chaves, A. V., Evans, P. R., de Souza Figueiredo, E. E., & Ribeiro, G. (2020). Nutrition, feeding, and management of beef cattle in intensive and extensive production systems. In Animal Agriculture (pp. 75–98). Academic Press. https://doi.org/10.1016/B978-0-12-817052-6.00005-7 18 E. D. VICUNA ET AL. Milazzo, F., Francksen, R. M., Zavattaro, L., Abdalla, M., Hejduk, S., Enri, S. R., Pittarello, M., Newell Price, P., Schils, R. L. M., Smith, P., & Vanwalleghem, T. (2023). The role of grassland for erosion and flood mitigation in Europe: A metaanalysis. Agriculture, Ecosystems & Environment, 348, 108443. https://doi.org/10.1016/j.agee.2023.108443 Monteiro, M. S., Muro, B. B., Carnevale, R. F., Poor, A. P., Araújo, K. M., Viana, C. H., Almond, G. W., Moreno, A. M., Garbossa, C. A. P., & Leal, D. F. (2023). The beneficial effects of providing prepartum sows with nesting materials on farrowing traits, piglet performance, and maternal behavior: A systematic review and meta-analysis. Applied Animal Behaviour Science, 259, 105795. https://doi.org/10.1016/j.applanim.2023.105795 Muscat, A., De Olde, E. M., de Boer, I. J., & Ripoll-Bosch, R. (2020). The battle for biomass: A systematic review of foodfeed-fuel competition. Global Food Security, 25, 100330. https://doi.org/10.1016/j.gfs.2020.100330 Neumann, K., Elbersen, B. S., Verburg, P. H., Staritsky, I., Pérez-Soba, M., de Vries, W., & Rienks, W. A. (2009). Modelling the spatial distribution of livestock in Europe. Landscape Ecology, 24, 1207–1222. https://doi.org/10.1007/s10980-0099351-3 Nicholas-Davies, P., Fowler, S., Midmore, P., Coopmans, I., Draganova, M., Petitt, A., & Senni, S. (2021). Evidence of resilience capacity in farmers' narratives: Accounts of robustness, adaptability, and transformability across five different European farming systems. Journal of Rural Studies, 88, 388–399. https://doi.org/10.1016/j.jrurstud.2021.03.013 Nigmann, T., Dax, T., & Hovorka, G. (2018). Applying a social-ecological approach to enhancing provision of public goods through agriculture and forestry activities across the European Union. Studies in Agricultural Economics, 120(1), 1–7. https://doi.org/10.7896/j.1713 Norton, L., Maskell, L., McVittie, A., Smith, L., Wagner, M., Waterton, C., & Watson, C. (2022). Learning from innovative practitioners: Evidence for the sustainability and resilience of pasture-fed livestock systems. Frontiers in Sustainable Food Systems, 6. 1012691. https://doi.org/10.3389/fsufs.2022.1012691 Pahmeyer, C., & Britz, W. (2022). Life cycle sustainability assessment of European beef production systems based on a farmlevel optimization model. Journal of Cleaner Production, 376, 134285. https://doi.org/10.1016/j.jclepro.2022.134285 Paraskevopoulou, C., Theodoridis, A., Johnson, M., Ragkos, A., Arguile, L., Smith, L., Vlachos, D., & Arsenos, G. (2020). Sustainability assessment of goat and sheep farms: A comparison between European countries. Sustainability, 12(8), 3099. https://doi.org/10.3390/su12083099 Paris, B., Vandorou, F., Tyris, D., Balafoutis, A. T., Vaiopoulos, K., Kyriakarakos, G., Manolakos, D., & Papadakis, G. (2022). Energy use in the EU livestock sector: A review recommending energy efficiency measures and renewable energy sources adoption. Applied Sciences, 12(4), 2142. https://doi.org/10.3390/app12042142 Perrin, A., San Cristobal, M., Milestad, R., & Martin, G. (2020). Identification of resilience factors of organic dairy cattle farms. Agricultural Systems, 183, 102875. https://doi.org/10.1016/j.agsy.2020.102875 Peyraud, J. L., & MacLeod, M. (2020). Study on the future of EU livestock: How to contribute to a sustainable agricultural sector? Executive summary. Publications Office of the European Union. https://doi.org/10.2762/810306 Post, P. M., Hogerwerf, L., Bokkers, E. A., Baumann, B., Fischer, P., Rutledge-Jonker, S., Hilderink, H., Hollander, A., Hoogsteen, M. J. J., Liebman, A., Mangen, M. J. J., Manuel, H. J., Mughini-Gras, L., Poll, R., Posthuma, L., Pul, A., Rutgers, M., Schmitt, H., van Steenbergen, J., … de Boer, I. J. (2020). Effects of Dutch livestock production on human health and the environment. Science of the Total Environment, 737, 139702. https://doi.org/10.1016/j.scitotenv.2020.139702 Pulina, G., Acciaro, M., Atzori, A. S., Battacone, G., Crovetto, G. M., Mele, M., Pirlo, G., & Rassu, S. P. G. (2021). Animal board invited review—Beef for future: Technologies for a sustainable and profitable beef industry. Animal, 15(11), 100358. https://doi.org/10.1016/j.animal.2021.100358 Pykälä, J. (2005). Cattle grazing increases plant species richness of most species trait groups in mesic semi-natural grasslands. Plant Ecology, 175, 217–226. https://doi.org/10.1007/s11258-004-0167-y Ramos-García, M., Guzmán, G. I., & de Molina, M. G. (2022). Organic management of cattle and pigs in Mediterranean systems: Energy efficiency and ecosystem services. Agronomy for Sustainable Development, 42(6), 111. https:// doi.org/10.1007/s13593-022-00842-z R. Core Team. (2023). R: A language and environment for statistical computing, R Foundation for Statistical Computing. Rederink.dk (Danish government official website). (2024). Aftale om et Grønt Danmark (Green Tripartite Agreement). Retrieved January 8, 2025, from https://www.regeringen.dk/nyheder/2024/regeringen-og-parterne-i-groen-trepartindgaar-historisk-aftale-om-et-groent-danmark/ 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. (2024). Retrieved February 20, 2024, from http://data.europa.eu/eli/reg/2021/2115/oj Regulation (EU) 2024/1991 of the European Parliament and of the Council of 24 June 2024 on nature restoration and amending Regulation (EU) 2022/869 (Text with EEA relevance). (29 July 2024). Official Journal of the European Union, L 199. Retrieved January 8, 2025, from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32024R1991 Reidsma, P., Tekelenburg, T., Van den Berg, M., & Alkemade, R. (2006). Impacts of land-use change on biodiversity: An assessment of agricultural biodiversity in the European Union Agricultural land-use change and its drivers in mountain landscapes: A case study in the Pyrenees. Agriculture, Ecosystems & Environment, 114(1), 86–102. https://doi.org/10.1016/j.agee.2005.11.017 INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 19 Rivera-Huerta, A., Rubio Lozano, M. D. L. S., Padilla-Rivera, A., & Güereca, L. P. (2019). Social sustainability assessment in livestock production: A social life cycle assessment approach. Sustainability, 11(16), 4419. https://doi.org/10.3390/ su11164419 Rivero, M. J., & Lee, M. R. (2022). A perspective on animal welfare of grazing ruminants and its relationship with sustainability. Animal Production Science, 62(18), 1739–1748. https://doi.org/10.1071/AN22208 Seidl, R., Müller, J., & Wohlgemuth, T. (2022). Disturbance and biodiversity. In Wohlgemuth, In T, Jentsch, A., Seidl, R. (Eds.), Disturbance ecology (Vol. 32, pp. 51–66). Springer. https://doi.org/10.1007/978-3-030-98756-5_4 Shurson, G. C., & Kerr, B. J. (2023). Challenges and opportunities for improving nitrogen utilization efficiency for more sustainable pork production. Frontiers in Animal Science, 4. https://doi.org/10.3389/fanim.2023.1204863 Spada, E., De Cianni, R., Di Vita, G., & Mancuso, T. (2024). Balancing freshness and sustainability: Charting a course for meat industry innovation and consumer acceptance. Foods, 13(7), 1092. https://doi.org/10.3390/foods13071092 Spiegel, A., Slijper, T., de Mey, Y., Meuwissen, M. P., Poortvliet, P. M., Rommel, J., Hansson, H., Vigani, M., Soriano, B., Wauters, E., Appel, F., Antonioli, F., Gavrilescu, C., Gradziuk, P., Finger, R., & Feindt, P. H. (2021). Resilience capacities as perceived by European farmers. Agricultural Systems, 193. 103224. https://doi.org/10.1016/j.agsy.2021.103224 Spigarelli, C., Zuliani, A., Battini, M., Mattiello, S., & Bovolenta, S. (2020). Welfare assessment on pasture: A review on animal-based measures for ruminants. Animals, 10(4), 609. https://doi.org/10.3390/ani10040609 Stoddart, A. F. (2013). Climate change and hunger as the challenges in the global food system. Journal of International Development, 25(4), 456–472. https://doi.org/10.1002/jid.3380010208 Stranieri, S., Ricci, E. C., Stiletto, A., & Trestini, S. (2022). How about choosing environmentally friendly beef? exploring purchase intentions among italian consumers. Renewable Agriculture and Food Systems, 38, e2. https://doi.org/ 10.1017/S1742170522000357 Sulewski, P., Kłoczko-Gajewska, A., & Sroka, W. (2018). Relations between agri-environmental, economic and social dimensions of farms' sustainability. Sustainability, 10(12), 4629. https://doi.org/10.3390/su10124629 Upton, M. (2024). The role of livestock in economic development and poverty reduction. Food and Agriculture Organization of the United Nations (FAO). Retrieved February 10, 2024, fromhttps://www.fao.org/3/bp224e/bp224e.pdf Veltman, K., Rotz, C. A., Chase, L., Cooper, J., Forest, C. E., Ingraham, P. A., Jolliet, O., Izaurralde, R. C., Jones, C. D., Nicholas, R. E., Ruark, M. D., Salas, W., & Thoma, G. (2021). Assessing and reducing the environmental impact of dairy production systems in the northern US in a changing climate. Agricultural Systems, 192, 103170. https://doi.org/ 10.1016/j.agsy.2021.103170 Wani, A. A. (2024). Comprehensive analysis of clustering algorithms: Exploring limitations and innovative solutions. PeerJ Computer Science, 10, e2286. https://doi.org/10.7717/peerj-cs.2286 Wickham, H., Chang, W., & Wickham, M. H. (2016). Package ‘ggplot2’. Create elegant data visualisations using the grammar of graphics (Version 2.1). Retrieved February 15, 2024, from. https://cran.r-project.org/web/packages/ ggplot2/ggplot2.pdf Yan, X., Ying, Y., Li, K., Zhang, Q., & Wang, K. (2024). A review of mitigation technologies and management strategies for greenhouse gas and air pollutant emissions in livestock production. Journal of Environmental Management, 352, 120028. https://doi.org/10.1016/j.jenvman.2023.120028 Zagata, L., & Sutherland, L. A. (2015). Deconstructing the ‘young farmer problem in Europe’: Towards a research agenda. Journal of Rural Studies, 38, 39–51. https://doi.org/10.1016/j.jrurstud.2014.11.001 Zira, S., Rydhmer, L., Ivarsson, E., Hoffmann, R., & Röös, E. (2021). A life cycle sustainability assessment of organic and conventional pork supply chains in Sweden. Sustainable Production and Consumption, 28, 21–38. https://doi.org/ 10.1016/j.spc.2021.03.028 Zucali, M., Lovarelli, D., Celozzi, S., Bacenetti, J., Sandrucci, A., & Bava, L. (2020). Management options to reduce the environmental impact of dairy goat milk production. Livestock Science, 231, 103888. https://doi.org/10.1016/ j.livsci.2019.103888 20 E. D. VICUNA ET AL.