Multi-criteria decision-making (MCDM) methods have been refined and enhanced over time to become powerful tools for addressing complex and multidimensional problems. These methods assist decision-makers in selecting the best option in situations where multiple and sometimes conflicting, objectives must be considered. By evaluating various criteria, MCDM ensures a comprehensive and balanced assessment, enhancing both the accuracy and credibility of the decision-making process (Aycin et al., 2024; Nagy et al., 2024). In real-world applications, decision-making is often challenged by uncertainty and ambiguity, where fuzzy MCDM proves highly effective. By modelling these uncertainties, fuzzy approaches allow decision-makers to integrate qualitative and imprecise information, making the process more adaptable and realistic, especially when precise quantitative data is unavailable or subjective judgements are essential (Bechar et al., 2024; Lefranc et al., 2025). This flexibility is particularly valuable in domains such as sustainable agriculture, where evaluating diverse solutions requires balancing economic, environmental, and social considerations under uncertain conditions.

Sustainable agriculture is a holistic approach to farming that balances meeting present-day food and fiber needs with conserving the planet’s resources for future generations. By minimizing reliance on nonrenewable inputs and incorporating biological sources, it ensures productivity while protecting the environment (Rao and Rogers, 2006; Thompson, 2007). This system, rooted in an understanding of ecosystem services, aligns with principles from organic farming, regenerative agriculture, permaculture, and agroforestry to promote environmental resilience and long-term sustainability (Hayati et al., 2011; Pretty, 2008). Sustainable agriculture encourages multifunctionality by striking a balance between environmental health and productivity, producing food while contributing to public goods such as water conservation and biodiversity, and adapting to local conditions and social dynamics (Arora, 2018; Zhen and Routray, 2003). In order to fulfill the growing demands for food, feed, and biofuels, there is a greater need for agricultural products. Economically speaking, price volatility brought on by the growing demand for agricultural products can affect both the affordability of goods for consumers and the livelihoods of farmers (Bathaei and Štreimikienė, 2023; Martos et al., 2021). In terms of society, this demand intensifies competition for limited resources, such as water and land, which can lead to conflicts and uneven allocation of resources. In terms of the environment, the drive for increased production frequently leads to soil deterioration, deforestation, excessive chemical input use, and water resource depletion—all of which exacerbate climate change and biodiversity loss (Janker and Mann, 2020; Janker et al., 2019; Montgomery, 2007).

To achieve sustainable agriculture, it is essential to adopt a variety of solutions that are prioritized according to factors such as geographical, economic, social, and political conditions. Deciding which solution should take precedence requires a thorough evaluation from multiple angles, as sustainable agriculture is crucial not only for environmental protection but also for fostering economic growth and social development (Godfray and Garnett, 2014; Rockström et al., 2017). Despite the numerous challenges impeding the broader adoption of sustainable agriculture, the variety of solutions emphasizes that assessing the efficacy of solutions to achieve sustainable agriculture can be approached as an MCDM problem (Keshavarz-Ghorabaee, 2023b; Pretty et al., 2018). In this structure, experts establish criteria specific to the conditions and challenges of sustainable agriculture, providing a comprehensive framework for evaluation. Decision-makers can then evaluate the effectiveness of different solutions based on these criteria and, through MCDM techniques, identify near-optimal solutions that align with the existing conditions (Cicciù et al., 2022; Keshavarz-Ghorabaee, 2023a).

This study introduces a fuzzy multi-criteria group decision-making approach that integrates the Objective Pairwise Adjusted Ratio Analysis (OPARA) method, the Method Based on the Removal Effects of Criteria (MEREC), and the Relative Preference Relation (RPR). OPARA is a new MCDM method, and its efficiency and robustness have been rigorously validated through various analyses (Keshavarz-Ghorabaee et al., 2024). MEREC, which has been extensively applied in empirical studies, has been shown to be effective in determining objective weights in MCDM problems (Keshavarz-Ghorabaee et al., 2021). The proposed approach is employed to assess sustainable agriculture solutions by integrating subjective weights—representing expert-assigned weights of evaluation criteria—with objective weights derived from MEREC, ensuring a balanced and realistic weighting scheme. The fuzzy OPARA method is utilized for the evaluation process, leveraging comprehensive decision-making data while incorporating parameters that adjust evaluations based on the range and linearity of criteria. To rank fuzzy numbers, the RPR approach is applied, enabling a relative assessment within a set rather than in isolation (Wang, 2015). The proposed approach provides a robust framework for addressing a wide range of decision-making problems in fuzzy environments.

The remainder of the paper is structured as follows. Section 2 provides a review of the literature, focusing on recent research in the field. Section 3 details the methodology employed in this study. Section 4 presents the results and discussion, where the practical application of the proposed approach and the analysis of its results are examined. Finally, the conclusion summarizes the key findings of the study.

This section reviews several studies on the application of MCDM methods to agricultural research from recent years. At the end, a table provides a summary of the key features of the reviewed studies.

Zamani et al. (2020) investigated farmers’ perceptions and stakeholders’ views on climate change adaptation strategies in the Jarreh agricultural water resources system in southwest Iran. Six adaptation scenarios were developed, and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and PROMETHEE II (Preference Ranking Organization METHod for Enrichment of Evaluations) methods were used for evaluation. Results favoured enhancing irrigation efficiency and reducing cultivated area as the most effective strategies to mitigate climate change impacts. Ebad Ardestani et al. (2020) addressed the issue of water resource management in Iranian drainage basins. They utilized a framework to evaluate agricultural water supply systems, emphasizing cooperation among all stakeholders. They compared different water supply alternatives across social, economic, bioenvironmental, and technical criteria. Through MCDM techniques, the research showcased the effectiveness of evaluating projects for sustainable development. De Marinis and Sali (2020) investigated the use of participatory Analytic Hierarchy Process (AHP) in agricultural development projects. The method was utilized for choice criteria elicitation and resource allocation. The methodology facilitated team training and established a shared resource allocation pattern aligned with international guidelines for sustainable agricultural development in the region. Manafi Mollayosefi et al. (2020) evaluated agricultural sustainability in East Azerbaijan Province using Entropy and fuzzy AHP techniques. Key indicators included water consumption efficiency, conservation tillage, health, agricultural employment, and greenhouse land. The study emphasized the importance of considering multiple evaluation methods to avoid misleading results.

Rani et al. (2021) aimed to address the uncertainty in assessing agricultural residues-to-energy conversion technologies using Pythagorean fuzzy sets (PFSs). Through a combined approach incorporating weighted discrimination-based approximation (WDBA), they improved the assessment process by developing a new ranking method and a score function-based linear programming model to estimate unknown attribute weights. Mishra et al. (2021) addressed the uncertainty of season crop assessment as a complex MCDM problem. They proposed a new decision-making framework, combining CRITIC (Criteria Importance Through Intercriteria Correlation) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) approaches under PFSs environment. The CRITIC model estimated objective criteria weights, while VIKOR provided simple and accurate results for crop assessment. Yazdani et al. (2021) studied the impacts of flooding on agricultural supply chains within a circular economy framework. They utilized the SWARA (Stepwise Weight Assessment Ratio Analysis) and EDAS (Evaluation Based on Distance from Average Solution) methods, to identify and rank flood risk drivers. Tork et al. (2021) investigated the modernization of surface water distribution. They employed a combination of structural and non-structural methods alongside automation, evaluating scenarios based on technical, social, economic, and environmental criteria. They applied the AHP and COPRAS (Complex Proportional Assessment) for prioritization.

Kumar et al. (2021) employed frequency ratio and AHP methods, integrating remote sensing and geographic information system (GIS) to identify suitable land for agriculture. The results indicated that a significant portion of the area is unsuitable for agriculture, with only a small fraction being highly suitable. Hoose et al. (2021) addressed the challenge of optimizing product mixes in the agricultural machinery industry while considering sustainability. Using AHP and data envelopment analysis (DEA), they identified seven alternatives for grain trailer production. The AHP results highlighted one optimal alternative, while the DEA revealed two efficient options, allowing managers to choose between lower environmental impact and higher profitability. Puška et al. (2021) focused on the selection of sustainable suppliers for agricultural production, addressing the challenge of asymmetric information in supplier evaluation. They applied MCDM methods, specifically interval fuzzy logic, using PIPRECIA (PIvot Pair-wise RElative Criteria Importance Assessment) to determine criteria weights and MABAC (Multi-Attributive Border Approximation Area Comparison) for supplier ranking. This approach aids agricultural producers in making informed decisions despite information asymmetry. Erdoğan (2022) addressed the challenge of adopting Agriculture 4.0 technologies for sustainable resource use in agriculture. She proposed a decision-making framework utilizing MCDM that incorporates interval-valued spherical fuzzy numbers to manage uncertainty. The study extended the SWARA and MAIRCA (MultiAtributive Ideal-Real Comparative Analysis) methods, demonstrating their effectiveness in prioritizing farmers’ perceptions of these technologies.

Namiotko et al. (2022) examined the agri-environmental situation of selected European Union countries. They utilized SAW (Simple Additive Weighting), TOPSIS, and EDAS to identify strategies for improving agricultural practices and environmental conditions. The study revealed consistent trends across all methods, with Finland, Ireland, and Sweden showing the best agri-environmental performance, while the Netherlands, Denmark, and Germany ranked the lowest. Roy et al. (2022) focused on assessing land suitability for rain-fed cultivation in the red and lateritic zones of West Bengal, India. They employed a GIS-based MCDM approach, analysing factors such as slope, soil moisture, and geology. The AHP was used to assign weights and rank the sub-criteria. The study identified four land suitability categories, achieving 85% accuracy in mapping, which supports optimized agricultural practices and drought risk reduction. Çürük and Alptekin (2022) examined the Turkish floriculture industry to propose sustainable strategies. They conducted a SWOT (strengths, weaknesses, opportunities, and threats) analysis to assess the current state of the industry and utilized an MCDM approach with an ANP (Analytical Network Process) model to evaluate economic, environmental, and socio-political dimensions of sustainability. Puška et al. (2022) investigated the selection of green suppliers for agricultural producers. They employed Z-numbers alongside fuzzy LMAW (Logarithm Methodology of Additive Weights) and fuzzy CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) methods to account for uncertainty in expert decision-making. The study confirmed the model’s stability through validation and sensitivity analysis.

Abualkishik et al. (2022) addressed the challenge of improving agricultural production efficiency and sustainability through smart agriculture. They developed a fuzzy MARCOS (Measurement Alternatives and Ranking according to Compromise Solution) to evaluate agricultural solutions. The results indicated that specific aspects of agriculture are significantly influenced by the findings, emphasizing the importance of selecting effective smart agriculture solutions. Rouyendegh and Savalan (2022) developed a hybrid model based on AHP and TOPSIS to aid in selecting sustainable agricultural solutions. The case study highlighted the complexity of agricultural production techniques, emphasizing the need for a holistic assessment approach. The model effectively integrated qualitative and quantitative criteria, demonstrating its validity in evaluating technology suitability. Barbosa Junior et al. (2022) investigated the barriers to adopting sustainable agriculture. They identified these barriers through a literature review and analysed their influence using the fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) method. The study revealed eleven barriers, with “technical knowledge and qualified workforce” being the most significant. Radmehr et al. (2022) focused on sustainable water resources management to enhance water efficiency and food security. They proposed a novel spatial fuzzy strategic planning framework combined with MCDM and a conceptual agricultural water use model, which was applied to an irrigation network in Iran. Using SWOT analysis, AHP and TOPSIS, they identified and ranked barriers to sustainable practices.

Anusha et al. (2023) addressed the issue of land suitability for agriculture to enhance sustainable land use strategies. They developed a conceptual process for land suitability analysis using thematic layers derived from satellite images and collateral data. The study applied AHP, providing valuable insights for identifying viable agricultural lands. Abrams et al. (2023) employed triangular fuzzy numbers, Monte Carlo simulations and TOPSIS to rank five countermeasures for remediating contaminated agricultural land. Their findings indicated that incorporating uncertainties from linguistic scores, weights, and decision-maker disagreements significantly challenged deterministic rankings, resulting in a fuzzy ranking that encouraged decision-makers to reconsider the perceived superiority of alternatives. Tuan and Canh (2023) focused on developing priority strategies for agricultural development, amid drought challenges. They utilized the SWOT analysis to identify 15 strategies and employed the fuzzy ANP to rank these factors. The study highlighted effective strategies for agricultural production, emphasizing drought evaluation and market development for key crops. Mokarram et al. (2023) investigated the spatial distribution of fig trees infected by the invasive pest to identify areas prone to fig cultivation in Iran. They employed a multi-step approach, which included land suitability mapping through AHP and GIS. The results indicated that central and parts of the east and northwest regions were most suitable for fig cultivation.

Zkik et al. (2023) examined the barriers and enablers to adopting blockchain technology for sustainable supply chain performance in e-enabled agricultural supply chains. They utilized a novel hybrid method combining PFS, cumulative prospect theory, and VIKOR. The results provided strategies for blockchain technology implementation. Zhai et al. (2023) addressed the challenge of encouraging continued investment in agri-food supply chains. They developed an integrated decision-making framework based on MEREC and PFSs alongside other methods to evaluate and prioritize risks. An empirical case study demonstrated the framework’s effectiveness in assessing risks. Keskes et al. (2024) developed an MCDM framework to assist decision-makers in selecting sustainable agricultural practices and waste recovery methods. This framework integrated the 2-Tuple model for uncertainty and VIKOR for ranking sustainability scenarios. A case study on the olive oil life cycle in Sfax, Tunisia, demonstrated the framework’s effectiveness. Nguyen et al. (2024) focused on the barriers to digitalization and innovation in the agricultural supply chain. They developed a model combining DEMATEL and ANP with Spherical Fuzzy Sets (SFSs) to identify and quantify these barriers. The SFS-TOPSIS method was then applied to rank potential solutions. The results identified technology and institutional barriers as the most significant.

Prabhjyot et al. (2024) employed Agro-Eco-Resource zonation using meteorological, soil, and groundwater data to classify areas based on crop suitability. The AHP assigned weights to various factors, while GIS facilitated the creation of thematic maps. The findings provided a roadmap for sustainable agricultural practices, recommending a reduction in resource-intensive crop cultivation. Yang and Solangi (2024) focused on promoting sustainable development in China by evaluating natural resource management, environmental protection, and agricultural economics. They utilized fuzzy AHP and fuzzy VIKOR methods to analyse uncertainties in decision-making. The study identified economic viability, policy and governance, and environmental impact as key criteria influencing sustainability. Tao et al. (2024) examined the critical success factors necessary for enhancing productivity and performance in Chinese agriculture systems. They identified and prioritized 12 factors from a pool of 18 using the Delphi method, validated by experts’ opinions. Employing SWARA and ARAS (Additive Ratio ASsessment), the study revealed that an entrepreneurial mindset, entrepreneurial awareness among farmers, and technology transfer significantly influence agricultural growth. Atlı (2024b) focused on the challenges of selecting target markets for agricultural products. He employed fuzzy AHP and fuzzy COPRAS techniques to rank and evaluate market alternatives. The fuzzy AHP method assessed the importance levels of criteria, revealing that economic factors carried the most weight. The fuzzy COPRAS method ranked six importing countries, with the European Union identified as the top target market for processed agricultural products. In another study, Atlı (2024a) investigated the selection of sustainable fertilizer suppliers within the agricultural supply chain. He utilized AHP and ARAS techniques to evaluate and rank supplier alternatives. The AHP method determined the importance of criteria. Subsequently, the ARAS method ranked the suppliers.

Puška et al. (2024) examined the increasing electricity demand in agricultural production and the potential of renewable energy sources. They utilized fuzzy MCDM methods to develop a decision-making model. Their findings highlighted that economic criteria were prioritized, with solar energy identified as the most promising option for sustainable agricultural practices. Ismail et al. (2024) addressed the challenge of selecting the best supplier among competitors in agriculture. They developed an MCDM approach based on MEREC and MABAC, incorporating triangular neutrosophic sets to manage uncertainties. Comparison with the WSM (Weighted Sum Model) further validated the results. Bozorgi et al. (2024) focused on the risks associated with agricultural water distribution due to various natural and human-caused hazards. They developed a framework using a fuzzy dynamic Bayesian network model for risk assessment. The study defined and implemented several risk management scenarios and ranked them using WASPAS (Weighted Aggregated Sum Product Assessment), TOPSIS, and MOORA (Multi Objective Optimization by Ratio Analysis) methods. Singh et al. (2024) examined the disparities in agricultural development across districts in Uttar Pradesh, India. They utilized a combination of factor analysis (FA) and TOPSIS to create a composite index system based on twenty-six agricultural indicators. Biswas et al. (2024) investigated the practices for wheat cultivation using conservation agriculture methods. They employed TOPSIS and AHP to evaluate alternative tillage approaches. The performance of these regimes was assessed based on various parameters, including energy use and economics. Tran et al. (2024) addressed the issue of post-harvest losses in the agricultural supply chain. They employed a two-stage cause-effect approach using SFSs to manage uncertainty. The Spherical fuzzy Delphi method identified critical criteria, while the Spherical fuzzy DEMATEL method analysed relationships among these factors. The study revealed that transportation was the most significant causal factor.

Table 1 summarizes and examines the studies reviewed above based on their consideration of sustainability, the use of a single or integrated approach, the incorporation of uncertainty, the MCDM methods used, and the research problem addressed.

In this section, the preliminaries of the proposed methodology are provided. Then a novel multi-criteria group decision-making approach in a fuzzy environment is presented.

In this section, the results of applying the proposed approach to evaluate solutions for achieving sustainable agriculture are first presented. Subsequently, a sensitivity analysis and a comparison of the results with other methods are conducted. Finally, a detailed discussion is provided.