Nóra Gombkötő
Tamás Vinkóczi
Judit Petra Koltai- 1Department of Agricultural Economics, Albert Kázmér Faculty of Agricultural and Food Sciences, Széchenyi István University, Mosonmagyaróvár, Hungary
- 2Department of Corporate Leadership and Marketing, Kautz Gyula Faculty of Economics, Széchenyi István University, Győr, Hungary
- 3Department of Statistics, Finance and Controlling, Kautz Gyula Faculty of Economics, Széchenyi István University, Győr, Hungary
Introduction: In response to the growing demand for practical and robust sustainability assessment tools, this study introduces a new method for evaluating agricultural sustainability at the farm level. The tool relies on indicators covering environmental, economic, and mixed dimensions of sustainability. The mixed dimension integrates environmental, economic, and social indicators.
Methods: Indicators were selected based on the literature and empirical data from Hungarian farms. From 61 initial indicators, three groups were formed through factor analysis and clustering.
Results: The analysis revealed that environmental and economic factors contribute almost equally to sustainability scores, whereas the mixed dimension has a comparatively smaller impact. This suggests that immediate sustainability improvements might need to prioritize environmental and economic factors. The assessment tool allows the calculation of a complex agricultural sustainability index, which has been validated through case studies on Hungarian farms.
Discussion: This study is presented as a methodological pilot project to develop and test a farm-level sustainability assessment tool for agricultural enterprises. The results highlight the practical applicability of the tool for farmers and policymakers, as it offers a transparent, easy-to-use method for identifying sustainability strengths and weaknesses at the enterprise level. Limitations include a small, region-specific sample, which may restrict broader applicability. Additionally, there are challenges in integrating multidimensional indicators. Future research should focus on expanding the dataset, refining indicator weighting, and testing the tool’s applicability in a broader agricultural context. This enhances the robustness and guides stakeholders in sustainable agricultural development.
1 Introduction
Agriculture plays a critical role in the use of global resources and has a significant (FAO, 2022), impact on biodiversity, water and the global climate (Streich et al., 2020), posing urgent challenges for sustainable development. This study addresses these challenges by developing a comprehensive sustainability assessment tool to guide more sustainable agricultural practices and policies. Maximizing agricultural production inevitably leads to the destruction or prevention of the emergence of ecosystems. To increase productivity, agricultural production relies on synthetic pesticides and fertilisers, which also have detrimental environmental effects. Agricultural production is responsible for 21%–37% of the global anthropogenic greenhouse gas emissions (IPCC, 2023), irrigation accounts for 69% of freshwater withdrawals (FAO, 2022), while an estimated ∼13% of the produced food is lost and 17% of it is wasted throughout the value chain (FAO, 2023; Veldhuizen et al., 2020). A sustainable but still productive agriculture that protects and conserves resources is required. Therefore, agriculture must ensure profitability, environmental health, and social and economic equity (e.g., fair labour conditions and community wellbeing) (FAO, 2014). For this reason, farmers struggle to balance social and economic goals without sacrificing the environment or natural ecosystems (Kiełbasa et al., 2018; Prus, 2010).
With a complex approach, sustainable development simultaneously considers expectations for environmental issues, social needs, and economic development. These three pillars of sustainability - environmental, social, and economic - form the basis of sustainable development (Figure 1).
Figure 1. The three pillars of sustainable development and their intersections. Source: Own elaboration based on Sikdar’s (2003) model.
The environmental pillar focuses on the conservation of natural resources and ecosystems. The social pillar emphasizes equality, justice, and quality of life. The economic pillar supports flexible and inclusive growth, ensuring opportunities for all. These three pillars must be met simultaneously. Considering the three pillars in a systematic approach means that the different characteristics of each pillar and the system relationships between them must also be taken into account (Sikdar, 2003). The usual way to represent this is to embed the three subsystems by arranging their individual circles within one another. According to Fleischer (2007), the economy is a subsystem of society, while the latter is a subsystem of the environment (the natural environment determines the structure and functioning of society, and the economy must conform to this). Although the three-pillar model of sustainability is widely used, it has been criticized for being overly simplistic and difficult to integrate the pillars in practice (Purvis et al., 2019). Some authors also mentioned a fourth pillar of sustainability: cultural diversity, which is the basis for a moral, spiritual, ethical, and sustainable lifestyle (Elliott, 2013; UNESCO, 2010), or appropriate government actions (Sachs, 2015). Complex systems must always be seen and examined in context. This study adopts Sikdar’s (2003) intersecting model to develop mixed indicators. Sikdar (2003) illustrates sustainability with three intersecting circles, where each circle represents one of the three “legs” of sustainability, i.e., economic, ecological (or environmental), and social, in order to identify the types of indicators needed to characterize progress towards sustainability. According to Tanguay et al. (2010), interactions and overlaps among dimensions should also be included in the models. The intersections are referred to as equitable (correspondence of the economic development to social needs, e.g., fair income distributions among farmers), livable (interaction between the environmental and social dimension, e.g., healthy rural communities with clean environment), viable (consistency with economic development and the environment, e.g., ecofriendly profitable farming) and sustainable (considering ecological, social and economic impacts, e.g., balanced ecological, social and economic outcomes).
Several methods were developed worldwide to estimate and evaluate the sustainability of agriculture. Due to the complexity of agriculture, these evaluation methods should be simple, inexpensive and holistic (Hayati, 2017; Talukder et al., 2017), i.e., they should include the social, economic and ecological dimensions (Quintero-Angel and González-Acevedo, 2018; Soulé et al., 2021). Furthermore, the terminology needs clarification when applying the method. The target, the target group, and the time scale must be clearly defined. Proper scaling and the method of summing scores (simple or weighted) are important for evaluation, as an incorrectly selected method can significantly skew the results. In addition, spatial (local, regional, national) and temporal (short-term, long-term) characteristics of the indicators should be considered (Soulé et al., 2021; Zhen and Routray, 2003). It is important to distinguish between “stock” indicators that express status and “flow” indicators that show changes and processes; the latter are considered relevant (Meadows, 1998). To achieve more robust results, it is advisable to analyse several indicators together (Gómez-Limón and Sanchez-Fernandez, 2010).
Several evaluation methods have been developed or applied to assess the sustainability of agriculture. Comprehensive reviews of quantitative methods for assessing sustainability can be found in Ness et al. (2007) and Phillis et al. (2010). For instance, Marcis et al. (2019) used the Sustainability Assessment for Agriculture Cooperatives (SAAC) evaluation model to assess the sustainability performance of agricultural cooperatives while Alary et al. (2020) revealed the relationship among the four dimensions of sustainability (diversification, integration, efficiency, wellbeing) using factor analysis, and Janker and Mann (2020) performed qualitative content analysis based on five main themes (human rights, working conditions, quality of life, impact on society, local conditions). Both quantitative and qualitative methods play a crucial role in agricultural sustainability research, as quantitative approaches provide measurable data on broader trends, while qualitative methods offer deeper insights into stakeholder perspectives and context-specific challenges.
The most common assessment methods are indicator-based; currently, more than 100 methods with various approaches are being used worldwide. The assessment tools can be grouped according to size and dimension. At the farm level, EVAS (Empirical Evaluation of Agricultural Sustainability), SAFE (Sustainability Assessment of Farming and Environment), and RISE (Response-Inducing Sustainability Evaluation) tools focus on specific management aspects. At the regional or national level, frameworks such as SAFA (Sustainability Assessment of Food and Agriculture Systems) and SEAMLESS (Environmental and Agricultural Modelling System; Linking European Science and Society) are used. Most tools assess environmental, economic, and social dimensions, but some focus primarily on one or two dimensions, such as environmental (e.g., SAFE), economic (e.g., EVAS), or social (e.g., RISE) factors. As the farm is considered the main management unit of the agricultural system (Payraudeau and van der Werf, 2005), most indicators in the literature are developed at the farm level. Considering agriculture sustainability at the farm level, de Olde et al. (2016), Iakovidis et al. (2022), Robling et al. (2023), Schader et al. (2014), van der Werf and Petit (2002), and van Passel and Meul (2012) offer an overview of indicator-based approaches to assess the sustainability performance in the food systems, considering farms, farming systems and supply chains. For instance, de Olde et al. (2016) examined 16 sustainability assessment tools, highlighting their strengths and limitations. The Sustainability Assessment of Food and Agriculture Systems (SAFA) is also applicable at the macro level - meaning to all enterprise sizes and types, and in all contexts, however, it does not contain full rating scales, therefore customized indicators have to be developed (FAO, 2012), moreover according to Talukder et al. (2017) not all indicators are applicable to every agricultural system in the world, as different circumstances (such as climate, soil, economic structure, and social conditions) require unique indicators to accurately reflect sustainability performance. There are some indicators in the literature that estimate sustainability at the regional level (Chopin et al., 2017; Nambiar et al., 2001; Gerdessen and Pascucci, 2013; van Cauwenbergh et al., 2007; Abdar et al., 2022), although they have some limitations. Moreover, the System for Environmental and Agricultural Modelling; Linking European Science and Society (SEAMLESS) method is suitable for estimating agricultural sustainability at the national, regional and farm levels as well. However, it does not provide outputs from all models required to calculate every indicator across the full range of spatial scales (Olsson et al., 2009; Talukder et al., 2020). Most indicators take into account and evaluate all three dimensions of sustainability (environment, economy, society) simultaneously, but there are some that use only an economic (Pannell and Glenn, 2000) or social approach (Leknoi et al., 2023), others evaluate from an agri-environmental aspect, emphasizing the key role of environmental factors in sustainability (Bausch et al., 2014; Dabkiene et al., 2021; Gaillard and Nemecek, 2009; Girardin et al., 2000). Sustainability is closely related to the environment, so it might be thought that organic farming is one of the most sustainable forms of farming, but the results of the methods used to test this show that organic farming may have disadvantages compared to conventional farming in other areas (e.g., certain economic or social indicators). For this reason, it is advisable to include organic and conventional production systems in assessments conducted using the various methods (Fernandes and Woodhouse, 2008; Ladu and Morone, 2021; Rigby et al., 2001; Trabelsi et al., 2016; Trabelsi et al., 2019). Some assessment tools were developed for a specific agricultural sector: for crop production (Committee on Sustainability Assessment, 2014; Sarkar et al., 2021), cereal production (Areal et al., 2018), wheat production (Valizadeh and Hayati, 2021), maize production (Leknoi et al., 2023), rice production (Roy et al., 2014), sheep farming (Ripoll-Bosch et al., 2012), grazing livestock (Alary et al., 2022) as well as fruit production, beef production, greenhouse production and arable farming (Coteur et al., 2018), all of which inhibit the widespread use of the method. Other systems require far too complex basic information, and there are some that use a small number of indicators (e.g., Sustainability Assessment of Farming and Environment, SAFE (van Cauwenbergh et al., 2007); Empirical Evaluation of Agricultural Sustainability, EVAS (Gomez-Limon and Sanchez-Fernandez, 2010); and Response-Inducing Sustainability Evaluation, RISE (Häni et al., 2003) that may skew the expected result. Besides, most of the sustainability assessment tools do not take the intersections of the sustainability dimensions into consideration or do not implement the environment-economy-society division of the dimensions (Alary et al., 2020; Ripoll-Bosch et al., 2012; Rodrigues et al., 2010; Roy et al., 2014; Sarkar et al., 2021), making the comparison with other tools difficult. Moreover, some indicator systems apply the same number of environmental, economic, and social indicators (Sharma and Shardendu, 2011), while others consist predominantly of social indicators (Fernandes and Woodhouse, 2008), neglecting the dominance of the environmental dimension.
Research to date has used broad indicator-based approaches to assess agricultural sustainability, but most studies rely on small samples and often focus on a single region or type of farming. The integration and weighting of indicators remains challenging, particularly given the complex interplay between the economic, environmental, and social dimensions of sustainability. The “mixed dimension” refers to a complex factor that integrates indicators across the environmental, economic, and social dimensions of sustainability to account for the interrelationships and combined effects of these pillars when assessing overall sustainability performance. In addition, the underrepresentation of “mixed” dimensions may limit the comprehensiveness and applicability of assessments. Taken together, these shortcomings call for the development of more widely tested and refined methodological frameworks that better reflect the diversity of agricultural systems and provide more effective support to decision-makers.
The farm-level assessment tool presented in the study addresses the shortcomings of indicators found in the literature. Since the assessment tool was not designed for a specific agricultural system, it can be applied generally in the sector. The data required for the model can be easily provided by the farmers. The applied indicators evaluate not only the three dimensions of sustainability, but also their intersections. The total of 61 indicators emphasizes the environment (e.g., 11 items) to reflect its dominance. This study was conducted as a methodological pilot project to develop and test a farm-level sustainability assessment tool for agricultural enterprises.
The two aims of the research are to assign indicators to the three dimensions of sustainability (ecological, economic, and social) and their intersections (liveable, viable, equitable, and sustainable), which can be easily measured by a farmer, and to develop an evaluation method based on index numbers. Furthermore, it aims to demonstrate, through case studies conducted at farms with different specializations, that the developed method can be applied to evaluate the sustainability of agricultural enterprises. Moreover, the study aims to identify the weaknesses of the examined farms and to recommend interventions based on index scores.
In this study, the terms farmers, agribusinesses, and companies are used interchangeably; “farmers” refers to agricultural producers who directly manage farming operations. “Agricultural entrepreneurs” are economic actors who engage in larger-scale or multi-farm activities and are involved in more comprehensive farming decisions. The term “companies” refers to companies operating in the agricultural sector that are not necessarily producers but, for example, processors or agricultural service providers.
1.1 Hypothesis development
In light of the findings of the literature review, the objective was to devise a set of indices for the identified indicator groups that could independently assess company performance within 1-1 groups. Additionally, the objective was to develop a potential indicator for sustainability by weighting these group indices in accordance with the following hypotheses:
H1. The environment factor is a significant determinant of companies’ ability to be sustainable.
H2. The environment and economy factor is a significant determinant of companies’ ability to be sustainable.
H3. The environment and society factor is a significant determinant of companies’ ability to be sustainable.
2 Methodology
2.1 Data collection
To analyse the hypotheses, it was necessary to collect specific data. A questionnaire survey (details in Table 1) was therefore conducted, with data collected in July and August 2023. A pilot survey of 20 respondents was conducted before the final questionnaire was designed in order to ensure that the items could adequately measure the effects and values identified in the relevant literature. Although paper-based surveys are considered traditional, they can still yield higher response rates than e-mail surveys (Sakshaug et al., 2019), which may be more realistic for business surveys. Furthermore, the personal contact involved in completing the questionnaire may increase the likelihood that respondents will make an unanticipated error.
Table 1. Details of the sample and data collection.
The selection of continuous scales, mainly linked to company performance and operations, was central to developing the indices. However, using continuous scales in questionnaires involves a broader range of self-assessment modifications (Chyung et al., 2018). Data were collected purposively to assess the sustainability of agricultural companies. The survey took place in a university setting. Participants were agricultural business owners attending a specialised seminar in 2023. Purposive sampling can be disadvantaged by researcher bias since prior assumptions may distort generalizability. While this method was suitable for a provisional analysis, applying it to a representative sample requires clear selection criteria, such as gender or region. In this study, 60 participants in a university training programme were invited to complete the questionnaire. Of these, 49 agreed, yielding a response rate of 81.67%. It is important to survey company owners, as their responses are most relevant to the analysis. These individuals have the necessary insight and background knowledge to provide valuable input. This is due to their familiarity with their companies and operations. The questionnaire was distributed among agricultural enterprises, but only 49 responses were received. Because of this low response rate, the indicator construction served as an experimental study. After data cleaning for incomplete responses and outliers, 30 valid responses remained (61.22%).
The limited sample size has a statistically significant impact on the indicator’s development. However, since the study is experimental, conclusions and results should be treated with caution. Both the statistical validity and potential bias may be considerable. Even so, describing the analysis process adds value for researchers. For generalisable insights, a sample size of at least 100 is recommended, and a larger sample is preferable (Mundfrom et al., 2005). According to the G*Power evaluation (f2 = 0.15; p = 0.05; predictors: 3), it is advisable to undertake factor analysis with three predictors on a sample of at least 119 individuals.
2.2 Introduction of the sample
A brief descriptive summary of the survey sample (Table 2) helps explore the field of analysis’s coverage. The survey was conducted in the NUTS-2 region of Western Transdanubia in Hungary, specifically in Győr, as this was the location of the seminar. Consequently, a significant number of the companies represented by the respondents are located in this region. In terms of sample size, it would have been unfortunate to exclude companies from other regions, as this would have led to a less comprehensive analysis.
Table 2. Sample descriptive statistics.
The experimental nature of the research renders the consideration of regional differences unnecessary. From an analytical perspective, however, it would be advantageous to select a specific region or weight regions based on the distribution of companies within them. These distribution characteristics should be based on company size, number of employees, and industry. It is imperative that these conditions for the method’s application be given full consideration when adapting the analytical techniques.
Regarding the standard production value, the distribution of the companies involved appears satisfactory, particularly when outliers are taken into account. To gain a unified perspective on sustainability, it was essential to conduct questionnaires with both livestock and crop farmers. This constituted a crucial aspect of the research process. The sample was deemed sufficient for the research, although certain limitations were acknowledged.
2.3 Indicator development
A detailed literature review preceding the analysis led to the development of seven groups of indicators: environment, society, economy, environment + economy, environment + society, society + economy, environment + economy + society. The aforementioned sets of indicators were populated through the administration of a questionnaire encompassing a multitude of variables (items). The variables included in each group, along with their respective units of measurement, are presented in Table 3.
Table 3. Variables (items) used in the analysis.
In light of the significant differences in data measurement types, a standardization process has been implemented to ensure values are treated uniformly. It was then possible to exclude outliers (standardized value >3) from the analysis. This was achieved through a descriptive analysis of the variables. Following data preparation (data cleaning), the initial stage of the methodology involved factor analyses conducted by the indicator group. This methodology is well-suited to the estimation of factor structure in sample data, in which the meanings of the included items are categorised into one or more factors (Matsunaga, 2010). In the analysis, therefore, a one-to-one factor analysis was conducted for each indicator group to determine the individual item weights (extractions).
To avoid oblique rotation, varimax rotation was employed, as the oblique method ensures that factors are not rotated 90° from each other, thereby facilitating potential correlations between them (Yong and Pearce, 2013). Consequently, the methodology was adapted to the subsequent clustering, ensuring that overlaps between dimensions did not lead to multicollinearity, which could have posed a problem in the context of oblique rotation (Hair et al., 2010). Given the potential non-normality of the available data, factor analyses were conducted using the principal axis factoring method and varimax rotation, in accordance with recommendations in the literature (Costello and Osborne, 2005; Fabrigar et al., 1999; Sürücü et al., 2022).
The use of statistical methods, such as factor analysis or principal component analysis, for indicator weighting and aggregation is widely recommended and applied in sustainability assessments (Gan et al., 2017). The analysis was performed in line with the basic conditions of factor analysis:
• substantial number of Pearson correlation >0.3;
• Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) >0.5;
• Bartlett’s Test sig. <0.05;
• Communalities >0.25;
• Total Variance Explained >33% (Habing, 2003; Barna and Székelyi, 2008; Beavers et al., 2013).
The selected factor analysis method (principal axis factoring) has the limitation of not incorporating goodness-of-fit (GOF) considerations. However, despite this limitation, this approach is commonly employed in exploratory factor analysis (de Winter and Dodou, 2011). The factor analyses were conducted to determine the internal consistency and reliability of the data. To ensure Cronbach’s alphas were not lower than 0.7 (Cronbach, 1951), the number of items per factor was selected. The weights and factor scores for the included items were determined for each group of variables, taking the thresholds into account.
In the second stage, factor scores were used to cluster the sample, which also allowed weighting the item groups measuring the indicator groups, as the F value is used for this purpose in the literature (Yan et al., 2023). Given the relatively small number of sample elements in the grouping, hierarchical clustering (McNeish and Harring, 2016) was employed to identify the optimal number of groups. Thereafter, K-means clustering was used to categorize respondents, and an ANOVA table based on the clustering was used to determine significant F values. In terms of the clustering design procedure, the literature recommendations set forth by Sajtos and Mitev (2007) were taken into account:
• use of standardised values;
• Pearson correlation between the variables involved <0.9;
• verification of the relevance of variables.
The development of specific group portfolios was not the primary objective of this study; however, it may provide insight into how and in what ways agricultural companies may be differentiated in terms of sustainability activities. The combination of the two stages (Table 4) thus provided an opportunity to develop a weighted sustainability indicator for agricultural companies.
Table 4. Steps for complex indicator development.
The methodology is suitable for indicator development, but the limited sample size is a significant limitation that should be considered when evaluating and using the results.
2.4 Results
The results of the factor analysis supported the first hypothesis (H1), which posits that sustainability indicators can be divided into three main dimensions: environmental, economic, and social (Table 5). A total of nine items were utilized for the factors, all of which met the preliminary criteria. The rationale for excluding four factors from the analysis was their low communalities and excessively low explained variances, indicating that the fundamental conditions for factor analysis were not met. The variables assigned to the factors were therefore not sufficient to explain a significant portion of the variance in the original variables. In instances where factors are implicated in exclusion, it is advised that data be supplemented, rethought, and replaced, as this may facilitate comprehension and exploration of these dimensions. However, the present study developed its hypothetical factor compositions based on previously validated research. The results suggest that these factors may not necessarily be used together appropriately.
Table 5. Designed factors and their associated items.
Regarding the correlations, the values for the environment factor ranged from 0.389 to 0.816, while those for the economy factor ranged from 0.399 to 0.621. Furthermore, the correlation between the two factors was 0.546 for the environment + economy + society factor. In addition to the observed correlations, the communalities (weights) of the items comprising the factors also met the threshold, with values above 0.25 in all cases. According to the results of the correlation analysis, hypothesis H2, which posits that the economic dimension has a significantly stronger relationship with the overall sustainability assessment, was rejected because the relationship was not significant (r = 0.12, p > 0.05). The Bartlett’s Test significance value also met the criteria for factor analysis, and the KMO value was above the 0.5 threshold in the analyses. In conclusion, the factor analysis results are deemed satisfactory, as corroborated by the reliability test, which yielded the following Cronbach’s alpha values: environment (0.815), economy (0.763), and environment + economy + society (0.705). The item-total correlations related to Cronbach’s alpha were as follows: environment (EN1: 0.677; EN2: 0.685; EN3: 0.550; EN4: 0.560), economy (EC1: 0.515; EC2: 0.690; EC3:.593), environment + economy + society (EES: 0.546; EES2: 0.546). In accordance with the recommendation of Gharaibeh et al. (2017), item-total correlations exceeding 0.3 are considered acceptable. The results of this study were therefore deemed acceptable.
The results thus lend support to the applicability and reliability of the factor constructs with the items included.
A total of one respondent was excluded from the cluster analysis due to the outlier environment + economy + society factor value (standardized value: 3.912), as determined by the repeated outlier analysis conducted prior to the cluster analysis; thus, 30 responses were involved in this phase of the analyses. The conditions for conducting a cluster analysis were based on the evaluation of standardized values, the examination of correlations among variables, and the assessment of the relevance of the included items. These criteria were considered as follows. The initial stage was based on the application of standardised values, which also satisfied the requisite clustering criteria. The relevance of the included variables is reflected in the literature review and supported by the factor analysis results. The required correlation strengths are indicated by the fact that the correlations between the values of the three factors ranged from 0.045 to 0.811, which are below the threshold of 0.9. The optimal clustering, as determined by the hierarchical method and illustrated in the dendrogram (Figure 2), was 3 clusters. This suggests the potential for sustainable classifications to be divided into three distinct groups when the indicator in development is utilized.
Figure 2. Hierarchical clustering dendrogram diagram. Note: Dendrogram showing 3 clusters based on factor scores. Source: Own elaboration based on questionnaire data.
The K-means method was used to generate three groups, which were selected using a hierarchical clustering approach. The K-means analysis yielded three clusters; however, one of them contained only a single element. It is possible to draw several conclusions from the average factor values of the three clusters (Figure 3). The three clusters are as follows: the first is economy-oriented, the second is environmentally conscious, and the third is a mixed profile. The initial cluster achieved a high score in the economic dimension; however, its environmental performance was substandard, suggesting a potential trade-off between productivity and sustainability. In the second cluster, the economy-environment-sustainability triad is significantly constrained, although economic indicators are lower. In the third cluster, the traditional environmental and economic pillars perform below standard.
Figure 3. Results of k-means clustering. Source: Own elaboration based on questionnaire data.
ANOVA F-tests were used to ascertain the statistically significant differences in the values of the groups (Table 6), thereby providing insight into the relative contributions of the factors to the clustering.
Table 6. K-means clustering results.
The results indicate that all three factors have a significant effect on group composition and are therefore suitable for measuring the agricultural business sustainability measure that is relevant to the sustainability aspect of this study. The environment and economy factors have almost equal weights, while the mixed factor has a significantly lower weight. To facilitate comprehension, the final weights were determined by proportionalising the sum of the F-values as follows: EN (0.477), EC (0.479), EES (0.044). The item and indicator group weights are collectively presented in Table 7.
Table 7. Item and indicator group weights.
The indicator weights (F-values are derived from Table 6) were calculated as follows:
• F_EN/(F_EN + F_EC + F_EES) = 0.477
• F_EC/(F_EN + F_EC + F_EES) = 0.479
• F_EES/(F_EN + F_EC + F_EES) = 0.044
Consequently, the initial phase of the iterative process entails determining the indicator values by applying a weighting system to the items in question. This is followed by the calculation of the sustainability indicator, which is achieved by applying the aforementioned indicator weights. In the calculations, it is, of course, particularly important to take into account the positive/negative signs identified in the literature, as they affect the value of the sustainability indicator.
To use a potential final indicator, it may be prudent to use normalised values in the calculations, which are more convenient to implement given minimum and maximum values. Nevertheless, the objective of the present research was merely to ascertain the possible indicator groups, items, and their respective weights for the test indicator.
The relatively higher weight of the environmental dimension (0.477) reflects the importance of these factors for sustainability. In this dimension, factors related to biodiversity enhancement (e.g., intensive crop rotation or proportion of natural grassland) and waste management (e.g., organic manure use) are positively skewed, while indicators related to waste generation and inappropriate management, as well as inefficient use of resources (e.g., fragmented land) are negatively skewed. The other important pillar of agricultural sustainability is the economic dimension, which accounts for 0.479 in this model. Within this group of indicators, increasing profitability through sustainable methods, such as improving resource efficiency (e.g., own territory, low indebtedness) or reducing market risks (e.g., diversification of buyers and suppliers, price stability), contributes positively to sustainability. However, unsustainable economic practices (inefficient resource allocation, inadequate market risk management) undermine long-term economic viability and competitiveness. Mixed indicators, which are a common intersection of environmental, economic, and social dimensions, make a positive contribution to sustainability when environmental protection, social equity, and human health are also considered alongside economic viability and profitability. This could include minimising the use of harmful chemicals to both the environment and human health, or adopting a local sales approach on both the supplier and customer sides. However, the study assigns a significantly lower weight (0.044) to this category, suggesting that it plays a smaller role in sustainability assessment, whether positive or negative, than the environmental and economic factors. This suggests that integrating them into sustainability assessment is methodologically challenging, as the interactions among environmental, economic, and social dimensions are complex and context-specific. This may reflect the difficulty of quantifying synergies or trade-offs between dimensions, as well as a tendency for assessment frameworks to focus on individual pillars rather than their integration. As a result, although composite indicators can provide valuable insights, their practical impact on overall sustainability assessment may be limited compared to dominant environmental and economic factors.
3 Discussion
A comparison of the indicator framework of the present study with the literature reveals similarities and specific features tailored to the local context. Several indicator-based methods focus on environmental factors due to their critical role in sustainability (Bausch et al., 2014; Dabkiene et al., 2021), while others focus primarily on social or economic dimensions (Fernandes and Woodhouse, 2008; Pannell and Glenn, 2000). The balanced approach to environmental, social, and economic factors in this study reflects the prominent role of agriculture in the national economy, where both resource conservation and management, as well as economic resilience, are essential for sustainable development. Furthermore, unlike global frameworks such as SAFA (FAO, 2012), which require context-specific indicators due to incomplete assessment scales, this study has developed a region-specific assessment tool using locally relevant data. The sustainability assessment tool developed in this study, with its complex indicator system based on 61 indicators and its unique integration of three dimensions—environment, economy, and society—offers advantages over previous methods such as the SMART, SAFA, or RISE systems (FAO, 2012; Häni et al., 2003). While these systems often evaluate only partial dimensions or require overly complex basic data, the present approach ensures both ease of use and multidimensional assessment. This makes the method more applicable to domestic economic and agri-environmental conditions and supports the development of targeted agricultural policy interventions.
The composition and weighting of the indicators developed in the study provide insights into their contribution to sustainable agricultural production and the relative importance of the environmental, economic, and mixed dimensions in assessing agricultural sustainability. The nearly equal weights assigned to the environmental (0.477) and economic (0.479) dimensions highlight their equal importance in achieving sustainability goals and the dual need to conserve natural resources and maintain economic resilience in agriculture. This finding aligns with the literature, which emphasizes the interdependence of these dimensions (Tanguay et al., 2010). For instance, Sikdar (2003) highlights that sustainable development requires a simultaneous focus on environmental health and economic growth, while Talukder et al. (2017) argue that agricultural sustainability must balance ecological conservation with economic profitability to ensure long-term viability. Although this study effectively balances environmental and economic dimensions, future versions of the assessment tool should also incorporate social indicators, such as workers’ rights, working conditions, and worker wellbeing, to fully assess the social sustainability of agricultural systems.
The mixed factor, which represents the common intersection of environmental, economic, and social dimensions, has a significantly lower weight (0.044), suggesting it plays a minor role in this context. This may be due to methodological reasons or to the complexity of integrating all three dimensions simultaneously. Although mixed indicators can be valuable for identifying synergies between sustainability dimensions (Sikdar, 2003; Tanguay et al., 2010), their lower weight in this study suggests that their impact on sustainability achievements is less specific, or that they are more complex to quantify than individual environmental, social, and economic indicators. The lower weighting of the mixed dimension can be attributed to the methodological challenges of quantifying mixed indicators. Integrating environmental, economic, and social dimensions into a single composite indicator is inherently complex, as these dimensions often interact in non-linear and context-specific ways. In addition, the aggregation of composite indicators typically requires sophisticated statistical methods, such as factor analysis or principal component analysis, which can be sensitive to the selection and correlation of the underlying variables. The lack of standardized approaches to measuring synergies across dimensions, as well as the risk of double-counting or spurious correlations, may also result in lower weightings for composite indicators in composite sustainability indices (Gan et al., 2017). Equal weighting is often used when all indicators are considered equally important or when insufficient data preclude alternative approaches (Gan et al., 2017). However, this approach has limitations: it may oversimplify the complex relationships between dimensions and fail to capture the varying importance of dimensions across contexts. For example, Rodrigues et al. (2010) highlight that consumer preferences directly influence corporate sustainability indicators, suggesting that consumer-driven metrics may be more relevant in certain contexts.
The results of this methodological experiment provide preliminary evidence of the approach’s feasibility but also indicate that further validation with larger datasets is needed.
The limitations of the study include a relatively small sample size and a focus on specific regions and economic types, which limit the generalizability of the results to other environments. The methodology used in factor analysis also makes certain assumptions that may influence the arrangement of indicators into dimensions and thus the structure of the complex sustainability index. Furthermore, the self-reported questionnaires used in data collection may introduce bias, potentially affecting the reliability of the analyses. Aware of these limitations, further research should expand the sample to include more regions and economic types, refine the indicator weighting system, and externally validate the method using other sustainability assessment frameworks. In addition, future studies could develop standardized approaches to quantify synergies across dimensions and integrate social indicators such as workers’ rights and wellbeing. Exploring new statistical models and software solutions for integrating mixed indicators could also improve the robustness and applicability of sustainability assessment tools.
The tool developed in the study can be used in agricultural policy to support the development of targeted support programmes and regulations that promote sustainable development. For farm managers, the tool enables them to identify their strengths and weaknesses in sustainability, thereby facilitating targeted improvements in environmental, economic, and social performance. In addition, the assessment framework enables agricultural businesses to objectively measure and compare their sustainability performance, thereby improving their competitiveness and supporting evidence-based decision-making in farm management.
4 Conclusion
This study developed a farm-level assessment tool for estimating agricultural sustainability using a multidimensional approach. It includes environmental, economic, and social dimensions, as well as their intersections. The methodology used showed the relevance of three main sets of indicators - environmental, economic, and mixed - with environmental and economic factors being assigned almost equal weight (0.477 and 0.479). In contrast, the mixed factor was ranked with a significantly lower weight (0.044). These results highlight the importance of balancing environmental and economic viability, while highlighting the challenges of integrating multiple dimensions simultaneously.
The study’s methodological strengths, such as the use of factor and cluster analysis, reinforce the reliability of the results. Factor analysis identified the most important indicator groups, while cluster analysis grouped farms based on their sustainability profiles, providing a clear and reliable structure for the assessment framework.
The model’s practical applicability lies in its ability to support farmers and policymakers in making informed decisions to ensure long-term sustainability. It develops easily measurable metrics at the farm level to assess environmental protection, economic viability, and social equity, which can help farmers to develop production systems and management practices that optimise resource use, improve profitability, and address environmental and social challenges. It also provides policymakers with valuable insights to design targeted interventions and agri-environment schemes that are consistent with sustainability objectives. The tool developed in the study is particularly important for Hungarian agriculture and similar regions because it integrates locally relevant indicators and balances environmental, economic, and social dimensions in line with the specific challenges and opportunities of small and medium-sized farms. Unlike global frameworks such as SAFA, which often require significant customization, this tool is designed for practical application at the farm level, making it more accessible and applicable to farmers and local policymakers. The emphasis on measurable, region-specific indicators ensures the tool supports targeted interventions and sustainable development strategies directly applicable to the local context. This study was designed as a methodological pilot study to provide a basis for future research. The results are promising, but further validation with larger samples and broader geographical coverage is needed to ensure reliability and generalizability.
Despite these strengths, the study has limitations. In sustainability assessment, the low weighting of the mixed dimension means that the tool favors environmental and economic factors over the integration of the three dimensions. This can lead to a less comprehensive assessment of sustainability, as synergies and trade-offs among environmental, economic, and social aspects are not fully accounted for. Therefore, the assessment may underestimate the importance of integrated approaches and highlight the need for methodological development to better reflect the complexity of agricultural sustainability. Furthermore, the small sample size (n = 30) falls below the minimum recommended for exploratory factor analysis (Costello and Osborne, 2005), which may affect the reliability and generalizability of the results, because the findings cannot be generalized to a larger region or to all types of farms. The lower weight assigned to mixed indicators (0.044) may neglect important interactions between sustainability dimensions. It is likely due to weak correlations among its indicators, a common problem with small samples, and a challenge for integrating multidimensional sustainability indicators. In addition, data based on farmers’ self-reports may introduce biases that compromise robustness. The absence of spatial and temporal variables also limits the model’s ability to account for broader trends in agricultural sustainability.
Future research should address these limitations. It is advisable to increase the sample size at both the regional and agricultural production system type levels. The inclusion of additional indicators that account for spatial and temporal dynamics could enhance the robustness of the evaluation framework. Consulting stakeholders on indicators could further increase accuracy. Comparative studies with other sustainability assessment tools, such as SMART or SAFA, could also provide valuable insights for methodological improvements and wider applicability.
The developed farm-level sustainability assessment tool has been designed to be applicable and scalable to other agricultural systems and geographical conditions. By integrating locally relevant indicators and balancing environmental, economic, and social dimensions, the framework can be adapted to the specific needs and challenges of different farming systems. This flexibility makes the tool valuable not only in Hungarian agriculture but also in similar regional contexts worldwide, supporting informed decision-making and sustainable development strategies at multiple levels.
The key lesson of this study is that a balanced approach to assessing agricultural sustainability, tailored to regional needs and integrating environmental, economic, and social dimensions, provides a solid basis for informed decision-making and targeted policy development in different agricultural contexts.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the [patients/participants OR patients/participants legal guardian/next of kin] was not required to participate in this study in accordance with the national legislation and the institutional requirements.
Author contributions
NG: Conceptualization, Formal Analysis, Investigation, Project administration, Supervision, Validation, Writing – original draft, Writing – review and editing. TV: Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review and editing. JK: Conceptualization, Formal Analysis, Investigation, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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References
Abdar, Z. K., Amirtaimoori, S., Mehrjerdi, M. R. Z., and Boshrabadi, H. M. (2022). A composite index for assessment of agricultural sustainability: the case of Iran. Environ. Sci. Pollut. Res. 29, 47337–47349. doi:10.1007/s11356-022-19154-6
Alary, V., Messad, S., Aboul-Naga, A. M., Osman, M. A., H. Abdelsabour, T., Salah, A. E., et al. (2020). Multi-criteria assessment of the sustainability of farming systems in the reclaimed desert lands of Egypt. Agric. Syst. 183, 102863. doi:10.1016/j.agsy.2020.10286
Alary, V., Lasseur, J., Frija, A., and Gautier, D. (2022). Assessing the sustainability of livestock socio-ecosystems in the drylands through a set of indicators. Agric. Syst. 198, 103389. doi:10.1016/j.agsy.2022.103389
Arcury, T. A., and Quandt, S. A. (1998). Occupational and environmental health risks in farm labor. Hum. Organ. 57 (3), 331–334. doi:10.17730/humo.57.3.m77667m3j2136178
Areal, F. J., Jones, P. J., Mortimer, S. R., and Wilson, P. (2018). Measuring sustainable intensification: combining composite indicators and efficiency analysis to account for positive externalities in cereal production. Land Use Policy 75, 314–326. doi:10.1016/j.landusepol.2018.04.001
Bakucs, L. Z., and Fertő, I. (2009). The growth of family farms in Hungary. Agric. Econ. 40, 789–795. doi:10.1111/j.1574-0862.2009.00415.x
Barna, I., and Székelyi, M. (2008). Túlélőkészlet az SPSS-Hez - többváltozós elemzési technikákról társadalomkutatók számára. Budapest: Typotex Kiadó.
Bausch, J. C., Bojórquez-Tapia, L., and Eakin, H. (2014). Agro-environmental sustainability assessment using multicriteria decision analysis and system analysis. Sustain. Sci. 9, 303–319. doi:10.1007/s11625-014-0243-y
Beavers, A. S., Lounsbury, J. W., Richards, S. W., Huck, G., Skolits, J., and Esquivel, S. L. (2013). Practical considerations for using exploratory factor analysis in educational research. Pract. Assess. Res. Eval. 18 (1), 6. doi:10.7275/qv2q-rk76
Bellon, S., and Lamine, C. (2009). “Conversion to organic farming: a multidimensional research object at the crossroads of agricultural and social sciences - a review,” in Sustainable agriculture. Editors E. Lichtfouse, M. Navarrete, P. Debaeke, S. Véronique, and C. Alberola (Dordrecht: Springer). doi:10.1007/978-90-481-2666-8_40
Berti, G., and Mulligan, C. (2016). Competitiveness of small farms and innovative food supply chains: the role of food hubs in creating sustainable regional and local food systems. Sustainability 8 (7), 616. doi:10.3390/su8070616
Blume, H.-P., Brümmer, G. W., Fleige, H., Horn, R., Kandeler, E., and Kögel-Knabner, I. (2015). Soil science. New York, NY: Springer.
Boyabatlı, O., Nasiry, J., and Zhou, Y. (2019). Crop planning in sustainable agriculture: dynamic farmland allocation in the presence of crop rotation benefits. Manag. Sci. 65 (5), 2060–2076. doi:10.1287/mnsc.2018.3044
Burton, R. J. F. (2014). The influence of farmer demographic characteristics on environmental behaviour: a review. J. Environ. Manage. 135, 19–26. doi:10.1016/j.jenvman.2013.12.005
Chel, A., and Kaushik, G. (2011). Renewable energy for sustainable agriculture. Agron. Sustain. Dev. 31, 91–118. doi:10.1051/agro/2010029
Chen, C.-C., and Liang, C. (2020). Evoking agriculture entrepreneurship: how younger and older farmers differ. Sustainability 12, 7005. doi:10.3390/su12177005
Chopin, P., Blazy, J. M., Guindé, L., Tournebize, R., and Doré, T. (2017). A novel approach for assessing the contribution of Agric. Syst. to the sustainable development of regions with multi-scale indicators: application to Guadeloupe. Land Use Policy 62, 132–142. doi:10.1016/j.landusepol.2016.12.021
Chyung, S. Y., Swanson, I., Roberts, K., and Hankinson, A. (2018). Evidence-based survey design: the use of continuous rating scales in surveys. Perform. Imprevement 57 (5), 38–48. doi:10.1002/pfi.21763
Coleman, D. C., Callaham, M. A., and Crossley, D. A. (2020). Fundamentals of soil ecology. Academic Press, Elsevier.
Committee on Sustainability Assessment (2014). Cosa methods - Concise introduction to the methodological principles. Available online at: https://thecosa.org/wp-content/uploads/2014/03/COSA-Methods-20141031.pdf (Accessed May 24, 2020).
Costello, A. B., and Osborne, J. (2005). Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis. Practi. Assess. Res. Eval. 10 (1), 7. doi:10.7275/jyj1-4868
Coteur, I., Marchand, F., Debruyne, L., Dalemans, F., and Lauwers, L. (2018). Participatory tuning agricultural sustainability assessment tools to Flemish farmer and sector needs. Environ. Impact Assess. Rev. 69, 70–81. doi:10.1016/j.eiar.2017.12.003
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika 16 (3), 297–334. doi:10.1007/BF02310555
Cronin, D. (2020). Soil science: natural resource management. New York, NY: Syrawood Publishing House.
Dabkiene, V., Balezentis, T., and Streimikiene, D. (2021). Development of agri-environmental footprint indicator using the FADN data: tracking development of sustainable agricultural development in Eastern Europe. Sustain. Prod. Consum. 27, 2121–2133. doi:10.1016/j.spc.2021.05.017
de Olde, E. M., Oudshoorn, F. W., Sørensen, C. A. G., Bokkers, E. A. M., and de Boer, I. J. M. (2016). Assessing sustainability at farm-level: lessons learned from a comparison of tools in practice. Ecol. Indic. 66, 391–404. doi:10.1016/j.ecolind.2016.01.047
de Winter, J. C. F., and Dodou, D. (2011). Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size. J. Appl. Stat. 39 (4), 695–710. doi:10.1080/02664763.2011.610445
Duval, J., Cournut, S., and Hostiou, N. (2021). Livestock farmers’ working conditions in agroecological farming systems. A review. Agron. Sustain. Dev. 41, 22. doi:10.1007/s13593-021-00679-y
Ewert, F., Rounsevell, M. D. A., Reginster, I., Metzger, M. J., and Leemans, R. (2005). Future scenarios of European agricultural land use: I. Estimating changes in crop productivity. Agric. Ecosyst. Environ. 107 (2–3), 101–116. doi:10.1016/j.agee.2004.12.003
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., and Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychol. Methods 4 (3), 272–299. doi:10.1037//1082-989X.4.3.272
Fennel, R. (1981). Farm succession in the European community. Sociol. Rural. 21, 19–42. doi:10.1111/j.1467-9523.1981.tb00290.x
Fernandes, L. A. D. O., and Woodhouse, P. J. (2008). Family farm sustainability in southern Brazil: an application of agri-environmental indicators. Ecol. Econ. 66 (2-3), 243–257. doi:10.1016/j.ecolecon.2008.01.027
Fischer, J. R., Finnell, J. A., and Lavoie, B. D. (2006). Renewable energy in agriculture: back to the future? Choices 21 (1), 27–31.
Fleischer, T. (2007). “Fenntartható fejlődés: Környezeti, társadalmi és gazdasági tényezők,” in Magyarország globális környezete 2020-ig. Háttértanulmányok a magyar külstratégiához (1) (Budapest: MTA Világgazdasági Kutatóintézet – CEU Center for EU Enlargement Studies), 192–202. Available online at: https://vgi.krtk.hu/∼tfleisch/PDF/pdf07/fleischer_fe-fejl-kor-tar-gaz-tenyezok_kum07.pdf.
Food and Agriculture Organization (FAO) (2012). Sustainability assessment of food and agriculture systems (SAFA) guidelines. Rome. Available online at: http://www.fao.org/fileadmin/templates/nr/sustainability_pathways/docs/Reflections_SAFA_E_Forum_2012_final.pdf (Accessed April 20, 2023).
Food and Agriculture Organization (FAO) (2014). Building a common vision for sustainable food and agriculture. Principles and approaches. Rome. Available online at: https://www.fao.org/3/i3940e/i3940e.pdf (Accessed January 21, 2023).
Food and Agriculture Organization (FAO) (2022). World food and agriculture – statistical yearbook 2022. Rome. Available online at: https://openknowledge.fao.org/server/api/core/bitstreams/0c372c04-8b29-4093-bba6-8674b1d237c7/content (accessed on November 02, 2025).
Food and Agriuculture Organization (FAO) (2023). Global facts and figures. Rome. Available online at: https://www.fao.org/docs/foodlosswastelibraries/default-document-library/global-facts-and-figures_idaflw24_en.pdf (accessed on November 02, 2025).
Gaillard, G., and Nemecek, T. (2009). Swiss agricultural life cycle assessment (SALCA): an integrated environmental assessment concept for agriculture. Available online at: https://www.researchgate.net/publication/263239301_Swiss_Agricultural_Life_Cycle_Assessment_SALCA_An_integrated_environmental_assessment_concept_for_agriculture (Accessed January 18, 2022).
Gan, X., Fernandez, I. C., Guo, J., Wilson, M., Zhao, Y., Zhou, B., et al. (2017). When to use what: methods for weighting and aggregating sustainability indicators. Ecol. Indic. 2017 (81), 491–502. doi:10.1016/j.ecolind.2017.05.068
Gao, J., Gai, Q., Liu, B., and Shi, Q. (2021). Farm size and pesticide use: evidence from agricultural production in China. China Agric. Econ. Rev. 13 (4), 912–929. doi:10.1108/CAER-11-2020-0279
Gerdessen, J. C., and Pascucci, S. (2013). Data envelopment analysis of sustainability indicators of European agricultural systems at regional level. Agric. Syst. 2013 (118), 78–90. doi:10.1016/j.agsy.2013.03.004
Gharaibeh, B., Al-Smadi, A. M., and Boyle, D. (2017). Psychometric properties and characteristics of the diabetes self management scale. Int. Journal Nursing Sciences 4 (3), 252–259. doi:10.1016/j.ijnss.2017.04.001
Girardin, P., Bockstaller, C., and Van der Werf, H. (2000). Assessment of potential impacts of agricultural practices on the environment: the AGRO*ECO method. Envron. Impact Assess. Rev. 20 (2), 227–239. doi:10.1016/S0195-9255(99)00036-0
Gómez-Limón, J. A., and Sanchez-Fernandez, G. (2010). Empirical evaluation of agricultural sustainability using composite indicators. Ecol. Econ. 69, 1062–1075. doi:10.1016/j.ecolecon.2009.11.027
Gruber, S., and Claupein, W. (2009). Effect of tillage intensity on weed infestation in organic farming. Soil Till. Res. 105 (1), 104–111. doi:10.1016/j.still.2009.06.001
Häni, F., Braga, F., Stämpfli, A., Keller, T., Fischer, M., and Porsche, H. (2003). RISE, a tool for holistic sustainability assessment at the farm level. Int. Food Agribus. Manag. Rev. 6, 78–90. doi:10.22004/ag.econ.34379
Hair, J. F., Black, W. C., Babin, B. J., and Anderson, R. E. (2010). Multivariate data analysis. 7th. edition. New Jersey: Pearson Prentice Hall.
Hayati, D. (2017). A systematic review on selection and comparison of holistic agricultural sustainability assessment frameworks. Front. Sustain. Food Syst. 5, 1559503. doi:10.3389/fsufs.2025.1559503
Henderson, K. A., Bauch, C. T., and Anand, M. (2016). Alternative stable states and the sustainability of forests, grasslands, and agriculture. Proc. Natl. Acad. Sci. U.S.A. 113 (51), 14552–14559. doi:10.1073/pnas.1604987113
Hennessy, D. A., and Marsh, T. L. (2021). Economics of animal health and livestock disease, Editor(s): Barrett, C. B., Just, D. R. Handb. Agric. Econ. 5, 4233–4330. doi:10.1016/bs.hesagr.2021.10.005
Hostiou, N., Vollet, D., Benoit, M., and Delfosse, C. (2020). Employment and farmers’ work in European ruminant livestock farms: a review. J. Rural. Stud. 74, 223–234. doi:10.1016/j.jrurstud.2020.01.008
Iakovidis, D., Gadanakis, Y., and Park, J. (2022). Farm-level sustainability assessment in mediterranean environments: enhancing decision-making to improve business sustainability. Environ. Sustain. Indic. 15, 100187. doi:10.1016/j.indic.2022.100187
Intergovernmental Panel on Climate Change (IPCC) (2023). Climate change 2023: synthesis report. Geneva, Switzerland: Intergovernmental Panel on Climate Change. Available online at: https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_SPM.pdf (Accessed November 02, 2025).
Janker, J., and Mann, S. (2020). Understanding the social dimension of sustainability in agriculture: a critical review of sustainability assessment tools. Environ. Dev. Sustain. 22, 1671–1691. doi:10.1007/s10668-018-0282-0
Keating, B. A., Carberry, P. S., Bindraban, P. S., Asseng, S., Meinke, H., and Dixon, J. (2010). Eco-efficient agriculture: concepts, challenges, and opportunities. Crop Sci. 50, 109–119. doi:10.2135/cropsci2009.10.0594
Kiełbasa, B., Pietrzak, P., and Ulén, B. (2018). Sustainable agriculture: the study on farmers’ perception and practices regarding nutrient management and limiting losses. J. Water Land Dev. 36 (1), 67–75. doi:10.2478/jwld-2018-0007
Klikocka, H., Zakrzewska, A., and Chojnacki, P. (2021). Characteristics of models of farms in the European union. Sustainability 13 (9), 4772. doi:10.3390/su13094772
Kumar, A., and Yadav, D. S. (2012). Use of organic manure and fertilizer in rice (Oryza sativa) wheat (Triticum aestivum) cropping system for sustainability. Indian J. Agric. Sci. 65 (10).
Ladu, L., and Morone, P. (2021). Holistic approach in the evaluation of the sustainability of bio-based products: an integrated assessment tool. Sustain. Prod. Consum. 28, 911–924. doi:10.1016/j.spc.2021.07.006
Leknoi, U., Rosset, P., and Likitlersuang, S. (2023). Multi-criteria social sustainability assessment of highland maize monoculture in Northern Thailand using the SAFA tool. Resour. Environ. Sustain. 13, 100115. doi:10.1016/j.resenv.2023.100115
Maier, S., Szerencsits, M., Narodoslawsky, M., Ismail, I. M. I., and Shahzad, K. (2017). Current potential of more sustainable biomass production using eco-efficient farming practices in Austria. J. Clean. Prod. 155 (1), 23–27. doi:10.1016/j.jclepro.2016.09.037
Marcis, J., Pinheiro de Lima, E., and Gouvea da Costa, S. E. (2019). Model for assessing sustainability performance of agricultural cooperatives. J. Clean. Prod. 234, 933–948. doi:10.1016/j.jclepro.2019.06.170
Matsunaga, M. (2010). How to factor-analyze your data right: do's don'ts, and how-to's. Int. J. Psychol. Res. 3 (1), 98–111. doi:10.21500/20112084.854
McNeish, D. M., and Harring, J. R. (2016). Clustered data with small sample sizes: comparing the performance of model-based and design-based approaches. Commun. Stat. Simul. Comput. 46 (2), 855–869. doi:10.1080/03610918.2014.983648
Meadows, D. (1998). Indicators and information systems for sustainable development. Hartland: The Sustainibility Institute.
Meeus, J. H. A., Wijermans, M. P., and Vroom, M. J. (1990). Agricultural landscapes in Europe and their transformation. Landsc. Urban Plan. 18 (3–4), 289–352. doi:10.1016/0169-2046(90)90016-U
Milford, A. B., Lien, G., and Reed, M. (2021). Different sales channels for different farmers: local and mainstream marketing of organic fruits and vegetables in Norway. J. Rural. Stud. 88, 279–288.doi:10.1016/j.jrurstud.2021.08.018
Mundfrom, D. J., Shaw, D. G., and Ke, T. L. (2005). Minimum sample size recommendations for conducting factor analyses. Int. J. Test. 5 (2), 159–168. doi:10.1207/s15327574ijt0502_4
Nambiar, K. K. M., Gupta, A. P., Fu, Q., and Li, S. (2001). Biophysical, chemical and socio-economic indicators for assessing agricultural sustainability in the Chinese coastal zone. Agric. Ecosyst. Environ. 87, 209–214. doi:10.1016/S0167-8809(01)00279-1
Ness, B., Urbel-Piirsalu, E., Anderberg, S., and Olsson, L. (2007). Categorising tools for sustainability assessment. Ecol. Econ. 60, 498–508. doi:10.1016/j.ecolecon.2006.07.023
Nolte, K., and Ostermeier, M. (2017). Labour market effects of large-scale agricultural investment: conceptual considerations and estimated employment effects. World Dev. 98, 430–446. doi:10.1016/j.worlddev.2017.05.012
Olsson, J. A., Bockstaller, C., Turpin, N., Therond, O., and Bezlepkina, I. (2009). Indicator framework, indicators, and up-scaling methods implemented in the final version of SEAMLESS-IF, SEAMLESS Report No. 41. Available online at: www.seamless-jp.org.
Ouda, S., Zohry, A., and Noreldin, T. (2018). “Crop rotation maintains soil sustainability,” in Crop rotation (Cham: Springer). doi:10.1007/978-3-030-05351-2_4
Pannell, D. J., and Glenn, N. A. (2000). A framework for the economic evaluation and selection of sustainability indicators in agriculture. Ecol. Econ. 33 (1), 135–149. doi:10.1016/S0921-8009(99)00134-2
Payraudeau, S., and van der Werf, H. M. G. (2005). Environmental impact assessment for a farming region: a review of methods. Agric. Ecosyst. Environ. 107, 1–19. doi:10.1016/j.agee.2004.12.012
Perry, B. D., Robinson, T. P., and Grace, D. C. (2018). Review: animal health and sustainable global livestock systems. Animal 12 (8), 1699–1708. doi:10.1017/S1751731118000630
Peterson, M. (1986). “The family farm”: a review of a central concept in western European agricultural politics. Scand. J. Hist. 11 (3), 265–282. doi:10.1080/03468758608579090
Phillis, Y. A., Kouikoglou, V. S., and Manousiouthakis, V. (2010). A review of sustainability assessment models as system of systems. IEEE Syst. J. 4, 15–25. doi:10.1109/jsyst.2009.2039734
Pretty, J. (2008). Agricultural sustainability: concepts, principles and evidence. Phil. Trans. R. Soc. 363 (1491), 447–465. doi:10.1098/rstb.2007.2163
Primdahl, J., Andersen, E., Swaffield, S., and Kristensen, L. (2013). Intersecting dynamics of agricultural structural change and urbanisation within European rural landscapes: change patterns and policy implications. Landsc. Res. 38 (6), 799–817. doi:10.1080/01426397.2013.772959
Prus, P. (2010). Funkcjonowanie indywidualnych gospodarstw rolniczych według zasad zrównoważonego rozwoju (the functioning of individual farms according to the principles of sustainable development). Bydgoszcz. Wydaw. Uczel. UTP, 185.
Purvis, B., Mao, Y., Robinson, D., Lovio, R., Temmes, A., Hildén, M., et al. (2019). Three pillars of sustainability: in search of conceptual clarity. Sustainability 11 (3), 603. doi:10.3390/su11030603
Quintero-Angel, M., and González-Acevedo, A. (2018). Tendencies and challenges for the assessment of agricultural sustainability. Agric. Ecosyst. Environ. 254, 273–281. doi:10.1016/j.agee.2017.11.030
Redfearn, D. D., and Bidwell, T. G. (2017). Bidwell stocking rate: the key to successful livestock production. Oklahoma Cooperative Extension Service. Available online at: http://osufacts.okstate.edu.
Renetzeder, C., Schindler, S., Peterseil, J., Prinz, M. A., Mücher, S., and Wrbka, T. (2010). Can we measure ecological sustainability? Landscape pattern as an indicator for naturalness and land use intensity at regional, national and European level. Ecol. Indic. 10 (1), 39–48. doi:10.1016/j.ecolind.2009.03.017
Rigby, D., Woodhouse, P., Young, T., and Burton, M. (2001). Constructing a farm level indicator of sustainable agricultural practice. Ecol. Econ. 39, 463–478. doi:10.1016/S0921-8009(01)00245-2
Ripoll-Bosch, R., Díez-Unquera, B., Ruiz, R., Villalba, D., Molina, E., Joy, M., et al. (2012). An integrated sustainability assessment of mediterranean sheep farms with different degrees of intensification. Agric. Syst. 105, 46–56. doi:10.1016/j.agsy.2011.10.003
Ripoll-Bosch, R., Joy, M., and Bernués, A. (2014). Role of self-sufficiency, productivity and diversification on the economic sustainability of farming systems with autochthonous sheep breeds in less favoured areas in Southern Europe. Animal 8 (8), 1229–1237. doi:10.1017/S1751731113000529
Robling, H., Abu Hatab, A., Säll, S., and Hansson, H. (2023). Measuring sustainability at farm level – a critical view on data and indicators. Environ. Sustain. Indic. 18, 100258. doi:10.1016/j.indic.2023.100258
Rodrigues, G. S., Rodrigues, I. A., de Almeida Buschinelli, C. C., and de Barros, I. (2010). Integrated farm sustainability assessment for the environmental management of rural activities. Environ. Imp. Assess. Rev. 30 (4), 229–239. doi:10.1016/j.eiar.2009.10.002
Roy, R., Chan, N. W., and Rainis, R. (2014). Rice farming sustainability assessment in Bangladesh. Sustain. Sci. 9, 31–44. doi:10.1007/s11625-013-0234-4
Sakshaug, J. W., Vicari, B., and Couper, M. P. (2019). Paper, E-mail, or both? Effects of contact mode on participation in a web survey of establishments. Soc. Sci. Comput. Rev. 37 (6), 750–765. doi:10.1177/0894439318805160
Sarkar, A., Azim, J. A., Al Asif, A., Qian, L., and Peau, A. K. (2021). Structural equation modeling for indicators of sustainable agriculture: prospective of a developing country’s agriculture. Land Use Policy 109, 105638. doi:10.1016/j.landusepol.2021.105638
Schader, C., Grenz, J., Meier, M. S., and Stolze, M. (2014). Scope and precision of sustainability assessment approaches to food systems. Ecol. Soc. 19, 42. doi:10.5751/es-06866-190342
Sharma, D., and Shardendu, S. (2011). Assessing farm-level agricultural sustainability over a 60-year period in rural eastern India. Environmentalist 31, 325–337. doi:10.1007/s10669-011-9341-x
Shivanna, M. (2022). An introduction to soil science. New Delhi: International Books and Periodical Supply Service.
Sikdar, S. K. (2003). Sustainable development and sustainability metrics. AIChE J. 49, 1928–1932. doi:10.1002/aic.690490802
Skalicky, R., Rogalska, E., Pietrzak, M. B., Zinecker, M., and Meluzinova, J. (2021). Optimal farm size and effectiveness of agriculture in the EU: the case of wheat yields. Transform. Bus. Econ. 20 (3C), 653–669.
Soulé, E., Michonneau, P., Michel, N., and Bockstaller, C. (2021). Environmental sustainability assessment in Agric. Syst.: a conceptual and methodological review. J. Clean. Prod. 325, 129291. doi:10.1016/j.jclepro.2021.129291
Stamm, I., Matthies, A.-L., Hirvilammi, T., and Närhi, K. (2020). Combining labour market and unemployment policies with environmental sustainability? A cross-national study on ecosocial innovations. J. Int. Comp. Soc. Policy. 36 (1), 42–56. doi:10.1017/ics.2020.4
Streich, J., Romero, J., Gazolla, J. G. F. M., Kainer, D., Cliff, A., Prates, E. T., et al. (2020). Can exascale computing and explainable artificial intelligence applied to plant biology deliver on the United Nations sustainable development goals? Curr. Opin. Biotechnol. 61, 217–225. doi:10.1016/j.copbio.2020.01.010
Strickler, D. (2021). The complete guide to restoring your soil. North Adams, MA: Storey Publishing.
Sürücü, L., Yıkılmaz, İ., and Maslakçı, A. (2022). Exploratory factor analysis (EFA) in quantitative researches and practical considerations. doi:10.31219/osf.io/fgd4e
Talukder, B., Blay-Palmer, A., Hipel, K. W., and van Loon, G. W. (2017). Elimination method of multi-criteria decision analysis (MCDA): a simple methodological approach for assessing agricultural sustainability. Sustainability 9, 287. doi:10.3390/su9020287
Talukder, B., Blay-Palmer, A., van Loon, G. W., and Hipel, K. W. (2020). Towards complexity of agricultural sustainability assessment: main issues and concerns. Environ. Sustain. Indic. 6, 100038. doi:10.1016/j.indic.2020.100038
Tanguay, G. A., Rajaonson, J., Lefebvre, J.-F., and Lanoie, P. (2010). Measuring the sustainability of cities: an analysis of the use of local indicators. Ecol. Indic. 10, 407–418. doi:10.1016/j.ecolind.2009.07.013
Tiwari, R., Sharma, M. C., Mishra, K. K., and Singh, B. P. (2013). Economic impacts of infectious diseases of livestock. Indian J. Anim. Sci. 83 (3).
Toader, M., and Roman, G. V. (2015). Family farming – examples for rural communities development. Agric. Agric. Sci. Proc. 6, 89–94. doi:10.1016/j.aaspro.2015.08.043
Trabelsi, M., Mandart, E., Le Grusse, P., and Bord, J. P. (2016). How to measure the agroecological performance of farming in order to assist with the transition process. Environ. Sci. Pollut. Res. 23, 139–156. doi:10.1007/s11356-015-5680-3
Trabelsi, M., Mandart, E., Le Grusse, P., and Bord, J. P. (2019). ESSIMAGE: a tool for the assessment of the agroecological performance of agricultural production systems. Environ. Sci. Pollut. Res. 26, 9257–9280. doi:10.1007/s11356-019-04387-9
United Nations Educational, Scientific and Cultural Organization (UNESCO) (2010). Culture: fourth pillar of sustainable development. Policy statement, United Cities and local governments (UCLG). Available online at: https://cultureactioneurope.org/wp-content/uploads/2011/02/UCLG-Culture-4th-Pillar-ENG1.pdf (accessed November 05, 2025).
Valizadeh, N., and Hayati, D. (2021). Development and validation of an index to measure agricultural sustainability. J. Clean. Prod. 280, 123797. doi:10.1016/j.jclepro.2020.123797
van Cauwenbergh, N., Biala, K., Bielders, C., Brouckaert, V., Cidad, V. G., Hermy, M., et al. (2007). SAFE—A hierarchical framework for assessing the sustainability of agricultural systems. Agric. Ecosyst. Environ. 120, 229–242. doi:10.1016/j.agee.2006.09.006
van der Werf, H. M., and Petit, J. (2002). Evaluation of the environmental impact of agriculture at the farm level: a comparison and analysis of 12 indicator-based methods. Agric. Ecosyst. Environ. 93, 131–145. doi:10.1016/s0167-8809(01)00354-1
van Passel, S., and Meul, M. (2012). Multilevel and multi-user sustainability assessment of farming systems. Environ. Impact. Assess. Rev. 32, 170–180. doi:10.1016/j.eiar.2011.08.005
Veldhuizen, L. J. L., Giller, K. E., Oosterveer, P., Brouwer, I. D., Janssen, S., van Zanten, H. H. E., et al. (2020). The missing middle: connected action on agriculture and nutrition across global, national and local levels to achieve sustainable development goal 2. Glob. Food Sec. 24, 100336. doi:10.1016/j.gfs.2019.100336
Wang, X., Yan, J., Zhang, X., Zhang, S., and Chen, Y. (2020). Organic manure input improves soil water and nutrients use for sustainable maize (Zea mays. L) productivity on the loess plateau. PLoS ONE 15 (8), e0238042. doi:10.1371/journal.pone.0238042
Wei, Z., Li-xia, Q. I., and Rui-mei, W. (2022). The relationship between farm size and fertilizer use efficiency: evidence from China. J. Integr. Agric. 21 (1), 273–281. doi:10.1016/S2095-3119(21)63724-3
Weltin, M., Zasada, I., Franke, C., Piorr, A., Raggi, M., and Viaggi, D. (2017). Analysing behavioural differences of farm households: an example of income diversification strategies based on European farm survey data. Land Use Policy 62, 172–184. doi:10.1016/j.landusepol.2016.11.041
White, B. (2012). Agriculture and the generation problem: rural youth, employment and the future of farming. IDS Bull. 43, 9–19. doi:10.1111/j.1759-5436.2012.00375.x
Xin, L., Yuancheng, X., and Hu, S. (2023). Green waste characteristics and sustainable recycling options. Resour. Environ. Sustain. 11, 100098. doi:10.1016/j.resenv.2022.100098
Yan, J., Liu, X., Oi, J., You, T., and Zhang, Z.-Y. (2023). The significance of kappa and F-score in clustering ensemble: a comprehensive analysis. doi:10.21203/rs.3.rs-3005071/v1
Yong, A. G., and Pearce, S. (2013). A beginner’s guide to factor analysis: focusing on exploratory factor analysis. Tutorials Quantitative Methods Psychology 9 (2), 79–94. doi:10.20982/tqmp.09.2.p079
Ze, H., Yi-Fan, L., Zeng, C., Yu, L., Dong, W., Fu-Ping, T., et al. (2019). Natural grasslands maintain soil water sustainability better than planted grasslands in arid areas. Agric. Ecosyst. Environ. 286, 106683. doi:10.1016/j.agee.2019.106683
Keywords: agricultural sustainability, farm-level evaluation, indicator-based assessment, multidimensional analysis, sustainability index
Citation: Gombkötő N, Vinkóczi T and Koltai JP (2026) Estimating agricultural sustainability: a multidimensional approach to a farm-level assessment tool. Front. Environ. Sci. 13:1704344. doi: 10.3389/fenvs.2025.1704344
Received: 12 September 2025; Accepted: 08 December 2025;
Published: 12 January 2026.
Edited by:
Vijay Singh Meena, ICAR - Mahatma Gandhi Integrated Farming Research Institute, IndiaReviewed by:
Amit Thakur, ICAR-Vivekananda Parvatiya Krishi Anusandhan Sansthan, IndiaPazhanisamy Selvaganesan, Borlaug Institute for South Asia, India
Copyright © 2026 Gombkötő, Vinkóczi and Koltai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Nóra Gombkötő, Z29tYmtvdG8ubm9yYUBzemUuaHU=