Assessment of Organization for Economic Co-Operation and Development Countries Based on Agricultural Performance Using Multi-Criteria Decision-Making Methods

Güler, Ezgi; Yerel Kandemir, Süheyla

doi:10.3390/su17188291

Open AccessArticle

Assessment of Organization for Economic Co-Operation and Development Countries Based on Agricultural Performance Using Multi-Criteria Decision-Making Methods

by

Ezgi Güler

and

Süheyla Yerel Kandemir

^*

Industrial Engineering Department, Faculty of Engineering, Bilecik Şeyh Edebali University, Bilecik 11230, Türkiye

^*

Author to whom correspondence should be addressed.

Sustainability 2025, 17(18), 8291; https://doi.org/10.3390/su17188291

Submission received: 8 July 2025 / Revised: 1 September 2025 / Accepted: 12 September 2025 / Published: 15 September 2025

Download

Browse Figures

Versions Notes

Abstract

This study presents a comprehensive evaluation of agricultural performance across 38 Organization for Economic Co-Operation and Development countries using an integrated Multi-Criteria Decision-Making framework that combines Technique for Order Preference by Similarity to Ideal Solution, VlseKriterijumska Optimizacija I Kompromisno Resenje, Analytical Hierarchy Process-based weighting, and equal-weighting strategies. The analysis reveals that the VlseKriterijumska Optimizacija I Kompromisno Resenje method exhibited greater sensitivity to changes in criterion weights, as confirmed by Spearman’s rank correlation (

P_{v} = 0.507

<

P_{t} = 0.938

), while Technique for Order Preference by Similarity to Ideal Solution produced more stable rankings. To confirm the differing outcomes, the Borda count technique is applied, yielding a highly consistent final ranking (

P_{r a n k} = 0.819

). Remarkably, according to the integrated ranking results, Norway (total Borda score: 73) emerges as the top-performing country in terms of agricultural sustainability, whereas Ireland (total Borda score: 0) is positioned at the bottom. These findings offer a critical reference point for policymakers and stakeholders, highlighting both methodological rigor and practical relevance. By combining subjective and neutral weighting approaches, this study provides a balanced decision-support model and also underscores the potential of hybrid Multi-Criteria Decision-Making structures in generating nuanced and actionable insights in agricultural strategy development.

Keywords:

agricultural assessment; Borda count technique; Multi-Criteria Decision-Making methods; Spearman’s rank correlation

1. Introduction

Agriculture is a fundamental component of the global economic system as it sustains the livelihoods of approximately 86% of the rural population. It stands as one of the most significant economic sectors, with a notable contribution to national income. For instance, in 2018, the agricultural sector represented around 4% of the worldwide Gross Domestic Product (GDP), and in several developing nations, this figure exceeded 25% [1]. As research on the adoption of novel practices and strategies in agriculture continues to expand, the connection between these developments and agricultural policy becomes increasingly significant. Among the emerging policy opportunities are initiatives aimed at empowering women farmers in developing countries to adopt beneficial innovative techniques, as well as the integration of marketing tools and strategies into public agricultural extension programs [2]. Although many countries that once had agriculture-based economies have gradually shifted toward industrial and, subsequently, service-oriented structures, the agricultural sector continues to play a vital role in national development. It does so by supplying raw materials to other industries, generating employment opportunities, and maintaining its central function in food production, which remains a critical activity in numerous economies [3].

Agricultural commodities have consistently held a significant place in global trade, reinforcing the importance of agricultural markets. One of the most notable features of economic transformation is the relative decline of agriculture in developing economies. Moreover, growth in exports from other sectors can sometimes undermine a nation’s agricultural base; nevertheless, due to rising global population and income levels, the demand for agricultural products is projected to increase. As such, the efficiency of a country’s agricultural trade practices will be a key indicator of its capacity to respond to growing demand [4]. Understanding the environmental impacts of agriculture is an essential requirement for all nations in order to ensure sustainable agricultural production. It is crucial to develop agriculture–environment indicators at the national level and to establish monitoring systems capable of tracking changes in these indicators over time. Agricultural performance indicators support evidence-based decision-making by facilitating the monitoring of environmental outcomes in agricultural activities and identifying critical trends in resource use and production practices [5].

Achieving sustainability in agriculture requires the fulfillment of specific economic, environmental, and social criteria. In addition, the adoption of a robust and well-structured decision-making framework is essential for guiding complex strategic choices in the agricultural sector. Such a framework plays a crucial role in identifying the most appropriate and effective partnership models that can facilitate a successful transition toward sustainable agricultural practices, particularly in the face of evolving environmental and socio-economic challenges [6]. The overall goal of this study is to develop a comprehensive and integrated framework to evaluate the agricultural performance of 38 Organization for Economic Co-Operation and Development (OECD) countries through Multi-Criteria Decision-Making (MCDM) approaches in the context of sustainability and policy relevance.

There are many studies in the literature on agricultural performance evaluation using MCDM approaches; for example, Alphonce (1997) [7] demonstrated the applicability of the Analytical Hierarchy Process (AHP) in agricultural decision-making within developing countries, particularly for subsistence farming contexts. Through a case study, AHP was employed to support decisions on field allocation, crop production methods, and the choice between subsistence and cash crops, incorporating resource-based criteria and sub-criteria into the analysis. Rezaei-Moghaddam and Karami (2008) [8] assessed suitable models for sustainable agricultural development in Iran, comparing ecological modernization and de-modernization; using AHP with input from multiple stakeholder groups, they identified nine evaluation criteria. Their results suggested ecological considerations as the most critical factor, leading to the conclusion that the ecological model holds the highest priority for Iran’s sustainable agriculture.

Aktan and Samut (2013) [9] analyzed the agricultural performance of Turkish provinces from the year 2009 using a two-stage Multi-Criteria Decision-Making approach, with Fuzzy AHP being employed to determine the weights of agricultural performance criteria, followed by the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for provincial ranking. The integration of linguistic variables enhanced the model’s realism and reliability. Additionally, a sensitivity analysis was conducted to evaluate the robustness of the results against changes in weight parameters. Talukder et al. (2017) [10] explored the use of the Elimination method, a type of MCDM, for assessing agricultural sustainability. Their study applied the method to a case study in coastal Bangladesh, highlighting its simplicity, speed, and applicability for ranking agricultural systems. While the approach demonstrated practical benefits, the authors also noted the importance of addressing certain limitations to ensure its effective implementation.

Cicciù et al. (2022) [11] reviewed studies employing MCDM methods to assess agricultural sustainability, and based on 41 articles from the Web of Science, they found a rise in related publications after 2016, with France and China as leading contributors. AHP was the most commonly used method, while the Triple Bottom Line framework and farming systems were frequently considered. Their study highlighted that MCDM applications in this field remain limited and predominantly compensatory. Kumar and Path (2023) [12] emphasized the relevance of United Nations Sustainable Development Goal 2, which targets ending hunger and ensuring global food security by 2030. They argued that achieving this goal requires integrating scientific disciplines, including mathematics and statistics, into agricultural practices. Highlighting agriculture’s central role in both developing and developed economies, they noted the complexity of decision-making due to conflicting factors. The authors reviewed the application of AHP in addressing agricultural decision-making problems, discussing models, data sources, and the effectiveness of AHP in improving decision precision.

Atlı (2024) [13] examined the importance of implementing agricultural policies within a sustainability-oriented framework in competitive economies, noting that climate change, evolving market dynamics, and shifts in national and international agricultural policies had increasingly influenced the sector. They aimed to identify and rank the criteria affecting agricultural policy for sustainable marketing using the Best Worst Method, the results of which indicated that project cost was the most influential criterion, followed by social benefits and employment opportunities. The authors emphasized that economic, social, and environmental dimensions should be considered collectively in policy development to enhance sustainability in agricultural marketing. Headquartered in Paris, the OECD supports governments by offering policy recommendations aimed at reducing poverty and enhancing economic growth, stability, and cooperation in areas such as trade, agriculture, investment, entrepreneurship, technology, and development. Given its active role in promoting collaboration in the agricultural sector, the productivity of member countries remains a central concern for the organization [14].

The specific objectives of this study are to (1) apply two MCDM methods—Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)—under both Analytical Hierarchy Process (AHP)-based and equal-weighting strategies; (2) generate four separate country rankings based on these combinations; (3) integrate the rankings using the Borda count technique to produce a unified and robust performance order; (4) evaluate the consistency and sensitivity of the individual methods by employing Spearman’s rank correlation coefficients; (5) offer policy-relevant insights into agricultural sustainability and performance differences among OECD countries. The main contribution and motivation of the study lies in the integration of different MCDM techniques through the Borda count technique to generate a unified ranking, followed by an analysis of the effectiveness of these methods using Spearman’s rank correlation coefficients. To the best of the authors’ knowledge, this represents the first attempt in the literature to assess the agricultural performance of OECD countries using this approach.

2. Materials and Methods

This study aims to support the evaluation of the agricultural performance of OECD countries with Multi-Criteria Decision-Making (MCDM) methods. The general workflow plan is given in Figure 1, with an explanation of the agricultural performance evaluation criteria determined for the OECD countries and the results of the Analytical Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), Borda count technique, and Spearman’s rank correlation coefficient detailed in the following sections. This workflow provides a structured and transparent process, ensuring methodological clarity and replicability for future research in similar contexts.

The selection of evaluation criteria and Decision-Makers (DMs) represents a critical foundation for ensuring the reliability and relevance of the agricultural performance assessment framework. In this study, the criteria are identified through a comprehensive review of the literature on sustainable agriculture and performance measurement, with particular emphasis on economic, environmental, and structural dimensions relevant to OECD countries. The aim is to capture a holistic view of agricultural sustainability while reflecting indicators that are both measurable and policy-relevant. To ensure expert-driven weighting of the criteria, a group of knowledgeable DMs with academic and practical expertise in agricultural economics, rural development, and sustainability are consulted. Their inputs are used in the AHP to derive subjective weights based on informed judgments. This approach allows the integration of expert insight into the evaluation while the inclusion of an equal-weighting strategy ensures methodological balance and neutrality. The Borda count technique serves as the core integration mechanism for systematically comparing and reconciling the ranking outcomes of the applied MCDM methods. Together, these steps contribute to the transparency and robustness of the overall MCDM workflow illustrated in Figure 1.

2.1. Data Set

The World Development Indicators (WDI) database, developed and maintained by the World Bank, is one of the most comprehensive sources of cross-country development data available for researchers and policymakers. It offers a wide array of statistical indicators covering economic, social, and environmental dimensions of development across more than 200 countries and regions [15] and includes key metrics such as Gross Domestic Product (GDP), agricultural value added, land use, employment, poverty rates, and CO₂ emissions, enabling comparative and longitudinal analyses [16,17].

Due to its extensive coverage and standardized data collection methodology, WDI has been frequently utilized in empirical research addressing sustainability, agricultural productivity, and socio-economic performance [18,19]. The reliability and accessibility of WDI make it a fundamental resource for evaluating progress toward global goals, including the Sustainable Development Goals.

In this study, explanations regarding evaluation criteria obtained from WDI for the agricultural performance of OECD countries determined the following six criterion codes. The first criterion code (C1) pertains to grain yields (kg per hectare) of wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and other mixed grains, which are reported as harvested and not adjusted for dry matter. While most grains are typically harvested within a relatively narrow moisture range, this may introduce minor variations in yield comparisons due to differences in the moisture content across grain types. The second criterion code (C2) is the application of mineral fertilizer (kg/ha of arable land) versus organic manures, which is reported by countries as either full-calendar-year or split-year applications. Here, arable land includes land used for temporary crops, meadows, market gardens, and fallow, but excludes shifting cultivation (i.e., swidden). The code C3 is the value added by agriculture, forestry, and fishing (% of Gross Domestic Product) which represents all sectors’ net outputs, which are calculated as the sum of outputs with intermediate inputs subtracted. Agricultural land (km²) (C4) is measured as the proportion of total land area composed of arable land as well as permanent crops and pastures. Permanent crops are not replanted after every harvest (e.g., coffee, cocoa, rubber, fruit trees, vines, and flowering shrubs) and do not include timber plantations; permanent pastures are used for five or more years and are either naturally occurring or cultivated. The fifth criterion code (C5) is employment in farming, hunting, forestry, and fishing (% of total national employment) involving working-age people producing goods and services for income/profit, including those temporarily absent from work. It refers to the percentage of the working-age population engaged specifically in agricultural activities such as crop cultivation, livestock production, and agricultural support services. According to the World Bank classification, this excludes sectors like forestry, fishing (unless related to aquaculture), and hunting, which are considered distinct from agriculture; therefore, this criterion reflects strictly farming-related employment. Finally, C6 is agricultural exports of raw materials (% of merchandise exports) that are unprocessed natural agricultural products used as inputs for manufacturing and industrial processes.

The decision matrix in Table 1 for OECD countries is obtained from WDI, provided by the World Bank Group [20]. The data in Table 1 are cross-sectional; no outliers are excluded, and no correction is needed for the data. The MCDM techniques noted in the flowchart in Figure 1 are detailed in the following sections.

2.2. Analytical Hierarchy Process

AHP is an MCDM technique developed by Thomas Saaty in the 1970s to produce solutions to complex problems [21,22], incorporating both quantitative and qualitative criteria. It enables the inclusion of individual or group preferences, expert opinions, experiences, judgments, and perspectives into the decision-making process, and addresses complex problems by structuring them within a hierarchical framework [23,24,25]. The steps of the AHP method are given in Appendix A.1.

2.3. Technique for Order Preference by Similarity to Ideal Solution

TOPSIS, an important MCDM method, was developed by Hwang and Yoon in 1981 [26], based on the logic that the solution alternative is closest to the positive-ideal solution and farthest from the negative-ideal solution. In this method, the distances of all alternatives to the positive and negative ideal solutions are calculated, and the method has the feature of being directly applicable to the data. Alternatives can be ranked according to their distances to the ideal solution among the maximum and minimum values that the criteria can take; there must be more than one decision option for the method to be applicable. The TOPSIS method is based on the assumption that each criterion has a systematically increasing or decreasing benefit trend [27]. The steps of the TOPSIS method are given in Appendix A.2.

2.4. VlseKriterijumska Optimizacija I Kompromisno Resenje

The VIKOR method was developed by Opricovic in 1998 for the optimization of multi-criteria complex problems. This MCDM technique takes into account the special measure of ‘closeness’ to the ‘ideal’ solution, based on ranking and selection among alternatives under conflicting criteria [28]. With its application, maximum group benefit and minimum regret are provided for a multi-criteria optimal compromise solution; it searches for the best alternative among the criteria and calculates the regret situation in case other alternatives are selected by considering the best alternative for the criteria. After calculating the regrets of the criteria, they are multiplied by the weight values of the criteria and, finally, a compromise solution is created between the maximum group benefit and minimum regret [29,30]. The steps of the VIKOR method are given in Appendix A.3.

2.5. Borda Count Technique

The Borda count technique was originally introduced in 1784 by Jean-Charles de Borda as a voting technique [31]. In the context of social choice problems, where deriving precise numerical evaluations from DMs can be challenging, the Borda count assigns scores to alternatives based on their relative rankings rather than absolute values [32]. It assumes equal importance for each group in classification performance, and is also considered straightforward in terms of its implementation [33].

In the Borda count scoring process, each of the alternatives within the evaluated group is assigned a rank-based score. The top-ranked alternative receives a score of

m - 1

, the second-best receives

m - 2

, and so on, with the lowest-ranked alternative receiving a score of

0

. The total Borda score for each alternative is calculated by summing these assigned values across all ranking classes. The final ranking of alternatives is then established based on their aggregated Borda scores. The mathematical representation of this procedure is provided in Equation (1) [34,35], where

r_{i k}

is the ranking of alternative

i

with respect to criterion

k

and

M

is the total alternative number.

b_{i} = \sum_{k = 1}^{n} (M - r_{i k})

(1)

2.6. Spearman’s Rank Correlation Coefficient

Spearman’s rank correlation coefficient quantifies the degree of statistical association between two distinct stochastic sequences by evaluating the strength and direction of their monotonic relationship [36,37]. For example, assume two random variables,

X

and

Y

, each consisting of

N

elements. Let

X_{i}

and

Y_{i}

represent the

i - t h

observation in

X

and

Y

, respectively, where

1 \leq i \leq N

. The values in both variables are ordered (either in ascending or descending order), resulting in two ranked sets denoted by

x

and

y

. Here,

x_{i}

corresponds to the rank of

X_{i}

and

y_{i}

corresponds to the rank of

Y_{i}

.

Spearman’s rank correlation coefficient, which measures the strength and direction of the monotonic relationship between the two variables, can be computed using Equation (2) [38]. However, in many practical applications, the precise values of the variables are less critical than their ranks; therefore, a simplified form of Spearman’s rank coefficient can be applied by calculating the rank differences

d_{i} = x_{i} - y_{i}

. The coefficient is then given by Equation (3):

P = \frac{\sum_{i = 1}^{N} (x_{i} - \bar{x}) (y_{i} - \bar{y})}{\sqrt{\sum_{i = 1}^{N} {(x_{i} - \bar{x})}^{2} \sum_{i = 1}^{N} {(y_{i} - \bar{y})}^{2}}}

(2)

P = 1 - \frac{6 \sum_{i = 1}^{N} d_{i}^{2}}{N (N^{2} - 1)}

(3)

3. Results

3.1. Weighting of Agricultural Performance Criteria

In this study, the agricultural performance criteria in Table 1 are scored by Decision-Makers (DMs). The DMs who were involved in the decision process had the following areas of expertise: DM1: environmental engineer, DM2: agricultural economist, DM3: economist. The pairwise comparison matrices of the DMs are given in Table 2, and the matrix values are prepared according to the scale used in the Analytical Hierarchy Process (AHP) method.

Using Equation (A5) and Equation (A6), the CR values for the evaluation of the three DMs are obtained as 0.097, 0.095, and 0.082, respectively. In this context, a consistent approach is taken for all DMs and there is no need to prepare the pairwise comparison matrices again. For the matrices in Table 2, the aggregated pairwise comparison matrix in Table 3 is obtained by taking the geometric mean of the values as specified in the AHP method. The agricultural performance evaluation criteria weights obtained by applying the AHP method steps in order are given in Table 4.

When Table 4 is examined, the most important criterion in the context of agricultural performance is the grain yield (kg per hectare) criterion, with a weight value of 0.2993, while the least important criterion is the agricultural raw material exports (% of merchandise exports) criterion, with a value of 0.0766. Within the scope of this study, the criterion weights obtained with AHP are used as method parameters for both the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods. In some studies using the TOPSIS method, the criterion weights are considered equally, and the algorithm steps are run [39,40,41,42]. Similarly, in some studies employing the VIKOR method, the equal weight method is used for criterion weighting [43,44,45]. As is known in Multi-Criteria Decision-Making (MCDM) processes, the evaluation of criterion weights with more than one DM contributes to the process [46]. In this study, the results obtained using the AHP-based weights in Table 4 are compared with those under the assumption of equal weighting. This comparison aims to evaluate the impact of weighting schemes on the final rankings produced by relevant MCDM techniques.

3.2. Technique for Order Preference by Similarity to Ideal Solution Analysis

The decision matrix for the Organization for Economic Co-Operation and Development (OECD) countries in Table 1 is used in the TOPSIS method. Criteria aspects (cost or benefit) are important for the TOPSIS method: In this study, grain yield (kg per hectare) is identified as a “benefit” criterion, as higher yields reflect greater agricultural productivity and technological efficiency. Fertilizer consumption (kg per hectare of arable land) is classified as a “cost” criterion. Excessive fertilizer use may lead to negative environmental consequences, such as soil degradation, and thus, lower usage is considered more sustainable. Value added by agriculture, forestry, and fishing (% of GDP) is considered a “benefit” criterion. A higher contribution of the agricultural sector to Gross Domestic Product (GDP) signifies a stronger and more influential sector within the national economy. Agricultural land (in square kilometers) is also treated as a “benefit” criterion, since a larger area of agricultural land implies greater production potential and reflects the country’s overall agricultural capacity. Employment in agriculture (% of total employment) is defined as a “cost” criterion. In more developed economies, lower agricultural employment often indicates structural transformation and higher sectoral efficiency, whereas higher employment rates may reflect underdevelopment. Agricultural raw material exports (% of merchandise exports) is regarded as a “benefit” criterion, as a greater share of agricultural exports in total merchandise trade highlights the strategic importance of agriculture in external trade.

In this study, two different algorithms are used in the TOPSIS method, as shown in Figure 1. The criteria weights obtained using the AHP method for

W_{i}

(weight for each agricultural performance evaluation criteria) in the fourth step of the TOPSIS method are given in Table 4. In this context, the weighted normalized decision matrix (using AHP) and ranking results obtained for the TOPSIS method are given in Table A3. According to Table A3, when AHP is used in the criterion weighting process for the TOPSIS method, the first country among the OECD countries in terms of agricultural performance is obtained as Mexico, while the last country in terms of agricultural performance is obtained as Ireland. Equal weights for

W_{i}

(weight for each agricultural performance evaluation criteria) are used in the fourth step of the TOPSIS method. In this context, the weighted normalized decision matrix (using equal weights =

W_{i} = \frac{1}{6} = 0.167

) and ranking results obtained for the TOPSIS method are given in Table A4. According to Table A4, when equal weights are used in the criterion weighting process for the TOPSIS method, the highest-ranking country among the OECD countries in terms of agricultural performance is found to be Mexico, while the lowest-ranking country in terms of agricultural performance is found to be Ireland. Although the highest- and lowest-ranked countries are the same as those listed in Table A3, there are significant differences in the rankings of the remaining OECD countries.

3.3. VlseKriterijumska Optimizacija I Kompromisno Resenje Analysis

The decision matrix for the OECD countries in Table 1 is used in the VIKOR method. The criterion aspects for the VIKOR method in this study are the same as those used in the TOPSIS method discussed in the previous section. Here, as in the TOPSIS method, two different algorithms are used in the VIKOR method, as shown in Figure 1. The criteria weights obtained using the AHP method for

W_{i}

(weight for each agricultural performance evaluation criteria) in the fourth step of the VIKOR method are given in Table 4. In this context, the weighted normalized decision matrix (using AHP) and ranking results obtained for the VIKOR method are given in Table A5. According to Table A5, when AHP is used in the criterion weighting process for the VIKOR method, the highest-ranking country among the OECD countries in terms of agricultural performance is found to be Canada, while the lowest-ranking country in terms of agricultural performance is obtained as New Zealand. Equal weights for

W_{i}

(weight for each agricultural performance evaluation criteria) are used in the fourth step of the VIKOR method. The weighted normalized decision matrix (using equal weights =

W_{i} = \frac{1}{6} = 0.167

) and ranking results obtained for the VIKOR method are given in Table A6, and according to this, when equal weights are used in the criterion weighting process for the VIKOR method, the highest-ranking country among the OECD countries in terms of agricultural performance is found to be Israel. In the VIKOR method, when the criterion weights (

W_{i}

) are assumed to be equal, a notably different and unexpected outcome emerges: as presented in Table A6, six OECD countries—the United States, Ireland, Colombia, Latvia, Türkiye, and New Zealand—share the lowest rank in terms of agricultural performance.

3.4. Comparison of Results

The TOPSIS and VIKOR methods are among the most prominent and extensively applied techniques within the field of MCDM. Both approaches have gained widespread recognition due to their ability to effectively evaluate and rank alternatives in the presence of multiple, often conflicting, criteria. Despite their common goal of supporting rational decision-making, they differ significantly in their computational procedures and underlying assumptions [47]. Some studies in the literature show that the VIKOR and TOPSIS methods may produce different results and therefore, DMs should carefully decide which method to use. Both methods have advantages and limitations, and the choice should be made according to their intended use [48,49,50,51]. Several studies confirm that the TOPSIS and VIKOR methods may produce differing ranking results, with VIKOR often yielding more sensitive and discriminative findings in MCDM contexts [52,53]. The integration of different rankings that can be obtained as a result of the application of TOPSIS and VIKOR analyses into a single ranking has also been encountered in studies in the literature [54,55].

In this study, the TOPSIS and VIKOR methods are used with two different criteria weighting strategies (AHP and equal weight) for the agricultural performance of OECD countries. When the Spearman rank correlation coefficients in Section 2.6 are calculated for the TOPSIS and VIKOR methods,

P_{t}

= 0.938 between the TOPSIS-AHP and TOPSIS-equal weight rankings, while

P_{v}

= 0.507 between the VIKOR-AHP and VIKOR-equal weight rankings. The Borda count technique is used to compare the final ranking under the different algorithms of the two methods to the ranking results obtained using the TOPSIS and VIKOR methods. In this context, the ranking results are given in Table 5. Based on the workflow chart in Figure 1, the Spearman rank correlation coefficient between the final ranking results obtained for the two different weighting methods in Table 5 is obtained as

P_{r a n k}

= 0.819. For the agricultural performance evaluation of the OECD countries, the two different final ranking results in Table 5 are combined with those of the Borda count technique to obtain the ranking shown in Figure 2. According to the combined final ranking results, the highest-ranking country in terms of agricultural performance among the OECD countries is found to be Norway, while the lowest-ranking country is obtained as Ireland.

4. Discussion

4.1. Comparison to Prior Research

Previous studies on agricultural performance evaluation have largely focused on either individual Multi-Criteria Decision-Making (MCDM) methods or limited geographic scopes. For example, Alphonce [7] and Rezaei-Moghaddam & Karami [8] utilized the Analytical Hierarchy Process (AHP) approach to evaluate local or national agricultural priorities, while Aktan and Samut [9] applied Fuzzy AHP and VIKOR in a provincial context. However, these studies typically relied on a single weighting strategy and did not examine the comparative behavior of multiple MCDM models under different weighting schemes.

In contrast, the present study provides a broader and more systematic analysis by simultaneously applying both Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods to a comprehensive set of Organization for Economic Co-Operation and Development (OECD) countries. Furthermore, by incorporating two distinct weighting strategies—subjective (AHP-based) and neutral (equal weighting)—this research offers a multidimensional view of agricultural performance assessment. Most notably, while the earlier literature seldom reconciled differences between methods, our study employs Borda count aggregation to integrate rankings and mitigate methodological inconsistencies. This layered approach to performance evaluation, including sensitivity analysis via Spearman’s correlation, has not been widely explored in previous studies and thus represents a methodological advancement. Additionally, while Cicciù et al. [11] noted that the literature on MCDM in agricultural sustainability remains fragmented and often compensatory in nature, our study presents a holistic hybrid framework that integrates multiple tools and decision perspectives. By doing so, it aligns more closely with the real-world complexity of agricultural systems and the multidimensional trade-offs faced by policymakers.

4.2. Research Implications

In this study, the agricultural performance of OECD countries was assessed using TOPSIS and VIKOR methods under two different weighting strategies: the AHP and equal weighting. The findings revealed that the VIKOR method produced more sensitive ranking outcomes compared to those obtained using TOPSIS, particularly when the weighting scheme changed. This observation was empirically supported by the Spearman rank correlation coefficients (

P_{v}

= 0.507 <

P_{t}

= 0.938), indicating greater variability in the VIKOR rankings due to changes in the weight structure. These results show that VIKOR methods are more sensitive under changing criteria for weighting conditions and have the potential to obtain different results. Furthermore, the individual rankings derived from the TOPSIS and VIKOR algorithms were aggregated using the Borda count technique under both weighting strategies. As presented in Table 5, the final combined ranking achieved a high consistency score (

P_{r a n k}

= 0.819), reinforcing the reliability of the integrated approach. This hybrid methodology, which combines Decision-Maker (DM)-based and neutral weighting schemes, offers a balanced and robust decision-support tool for evaluating agricultural performance across countries.

This study differs from the existing literature in terms of its contributions and general gains, as follows: (i) This study simultaneously employed both the TOPSIS and VIKOR methods, enabling a comparative analysis of the ranking results generated by each technique. A sensitivity analysis was conducted by applying two distinct weighting strategies—AHP and equal weighting—to observe the influence of weighting schemes on the final rankings. (ii) The empirical findings demonstrated that the VIKOR method is more responsive to changes in weight allocation, as evidenced by the Spearman rank correlation coefficients. (iii) To mitigate inconsistencies between methods, the ranking results obtained using TOPSIS and VIKOR were consolidated using the Borda count technique, yielding an integrated and more balanced final ranking. (iv) The aggregated ranking achieved a high degree of consistency, as reflected by the

P_{r a n k}

, underscoring the reliability of the combined decision-making framework. (v) This study presents a comprehensive and integrated decision-support approach—incorporating AHP, equal weighting, TOPSIS, VIKOR, and Borda count technique aggregation—which is relatively underexplored in the existing literature. (vi) The proposed model offers a comparative, methodologically robust, and policy-relevant ranking structure that can guide DMs and stakeholders involved in agricultural strategy development.

The findings of this study have several implications for research and practice. First, the demonstrated sensitivity of the VIKOR method to changes in weighting schemes highlights the importance of method selection and robustness testing in MCDM applications. Researchers should not assume stability in rankings without conducting sensitivity analyses, particularly when different weighting strategies are considered. Second, the integration of results through the Borda count offers a promising approach to synthesizing divergent rankings from multiple methods. This suggests that future studies should adopt aggregation techniques to enhance consistency and reduce uncertainty in multi-model evaluations. Third, the hybrid framework used in this study, which combines AHP, equal weighting, TOPSIS, VIKOR, and Borda count, serves as a scalable model for cross-national or regional performance assessments in agriculture and beyond. Researchers may extend this approach to evaluate sustainability in areas such as water resource management, energy efficiency, or rural development planning. Finally, the study underlines the value of comparative international assessments in guiding evidence-based agricultural policy. The ranking outcomes can help DMs identify performance gaps and design targeted interventions. Future research should build on this foundation by exploring additional MCDM techniques and incorporating fuzzy logic or gray theory-based weighting models to capture uncertainty and vagueness in expert judgments more effectively.

4.3. Policy Recommendations

The results of this study provide actionable insights for national policymakers, offering a clear basis for developing targeted strategies to improve agricultural performance. From a national policy perspective, countries that rank lower in the integrated performance index—such as Ireland in this study—are advised to undertake comprehensive evaluations of their agricultural systems, with particular attention to the criteria that have been identified as most influential in this analysis. The applied weighting schemes revealed that economic indicators (e.g., agricultural value added and export capacity), environmental sustainability (e.g., pesticide use and greenhouse gas emissions), and structural factors (e.g., land use efficiency and employment in agriculture) play critical roles in shaping the overall performance rankings. Accordingly, national policymakers should focus on refining sustainability strategies that directly target these high-weighted criteria. For instance, improvements in resource use efficiency, reduction in input intensity, and enhancement of agri-environmental programs could significantly contribute to better performance. Moreover, revisiting investment priorities to support technological modernization, sustainable production techniques, and farmer education—particularly in regions lagging behind—may offer tangible gains. The decision-support framework proposed in this study enables countries to benchmark their current standing against peers using a transparent, multidimensional structure. Moreover, the findings and suggested policy actions are broadly consistent with the strategic goals and thematic emphasis given by the World Bank and Organization for Economic Co-Operation and Development (OECD), particularly in terms of enhancing agricultural sustainability, improving resource efficiency, and fostering inclusive rural development [56]. By interpreting their relative position through the lens of weighted criteria, policymakers can not only identify key weaknesses but also prioritize corrective actions in a more targeted and evidence-based manner.

5. Conclusions

Assessing agricultural performance necessitates a multidimensional perspective encompassing economic, environmental, and structural factors. In this study, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods were applied under both Analytical Hierarchy Process (AHP)-based and equal-weighting schemes to evaluate 38 Organization for Economic Co-Operation and Development (OECD) countries. Overall, the proposed hybrid framework offers a balanced and reliable decision-support tool by integrating both subjective and objective weighting strategies. The principal contribution and motivation of this study are rooted in the integration of multiple Multi-Criteria Decision-Making (MCDM) techniques through the Borda count method to establish a unified and comprehensive ranking framework. This was further complemented by a comparative effectiveness analysis using Spearman’s rank correlation coefficients to evaluate the consistency and sensitivity of the applied methods. This study assesses the agricultural performance of OECD countries using this hybrid decision-making approach, offering a novel perspective for policy development and methodological advancement. This study has several limitations that should be considered. The AHP-based weighting process involves expert judgments, which may introduce subjectivity despite the balancing effect of the equal-weighting scheme. The analysis is limited to OECD countries, restricting the generalizability of the findings to other regions with different agricultural contexts. Additionally, the study assumes uniform policy and environmental conditions across countries, potentially overlooking national differences. While the Borda count provides a practical aggregation method, it does not account for potential interdependencies among criteria or methods. Lastly, the framework does not explicitly address uncertainty, which could be improved in future studies through fuzzy or probabilistic approaches.

Author Contributions

E.G. and S.Y.K.; methodology, E.G.; validation and formal analysis, E.G. and S.Y.K.; investigation, E.G.; resources, E.G.; data curation, E.G.; writing—original draft preparation, E.G. and S.Y.K.; writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study did not require ethical approval.

Informed Consent Statement

Informed consent was obtained from all decision-makers involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to thank the journal staff and referees who contributed to the development of this article.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:

GDP	Gross Domestic Product
OECD	Organization for Economic Co-Operation and Development
MCDM	Multi-Criteria Decision-Making
TOPSIS	Technique for Order Preference by Similarity to Ideal Solution
VIKOR	VlseKriterijumska Optimizacija I Kompromisno Resenje
AHP	Analytical Hierarchy Process
DM	Decision-Maker
WDI	World Development Indicators

Appendix A

Appendix A.1. Steps of Analytical Hierarchy Process

The initial step in the AHP involves structuring the decision problem into a clear and analyzable hierarchical format. This hierarchy is constructed by defining the overall goal, followed by the relevant criteria, sub-criteria, and decision alternatives. Since the decision problem for the AHP method in this study is the weighting of agricultural performance evaluation criteria, the hierarchical structure of the criteria is important. The decision criteria in Table A1 and the pairwise comparison of the decision criteria are evaluated using a scale between 1 and 9.

Table A1. Analytical Hierarchy Process scale.

Scale	Definition
1	Equal Importance
3	Moderately Importance
5	Very Strongly Importance
7	Exceptional Importance
9	Almost Always Importance
2, 4, 6, 8	Intermediate Values (Consensus Values)

The criteria comparison matrix is created. More than one DM can make the evaluation. In this case, the geometric mean is used in the Equation (A1) matrix for aggregation purposes. The diagonal values of the created matrix are always 1. In addition, the comparisons above the diagonal are taken directly as a result of the evaluations made (

n

), while the parts below the diagonal are taken as (

1 / n

).

A = [\begin{matrix} 1 & \dots & a_{i j} \\ ⋮ & ⋱ & ⋮ \\ \frac{1}{a_{i j}} & \dots & 1 \end{matrix}]

(A1)

After the comparison matrix between the criteria is created, the normalized matrix is calculated. Equation (A2) is used to obtain the normalized matrix. Considering Table 3, each criterion is divided separately by the column total. Then, each row is summed and divided by the total number of criteria. Using Equation (A3), the criterion weight (

W_{i}

) value of each criterion is calculated. In the calculation, the total value of each column should be 1.

{a_{i j}}^{'} = \frac{a_{i j}}{\sum_{i = 1}^{n} a_{i j}}, i, j = 1,2, \dots, n

(A2)

W_{i} = (\frac{1}{n}) \sum_{i = 1}^{1} {a_{i j}}^{'}, i, j = 1,2, \dots, n

(A3)

In order to calculate the consistency index (

C R

),

λ_{m a x}

Random Index (

R I

) and Concordance Index (

C I

) values must be known. Equation (A4) is used to calculate the

λ_{m a x}

value. The value in Table A2 corresponding to the criterion number is taken into account in determining the RI. Equation (A5) is used to calculate CI and CR is obtained with Equation (A6). In order for the study to be consistent,

0.1 > C R

. If this condition is not met, DMs are asked to repeat the pairwise comparison matrices.

λ_{m a x} = (\frac{1}{n}) \sum_{i = 1}^{n} (\frac{\sum_{j = 1}^{n} a_{i j} \cdot w_{j}}{w_{i}})

(A4)

Table A2. Random Index values.

n: Number of Criteria	1–2	3	4	5	6	7	8	9
Random Index	0.0	0.58	0.90	1.12	1.24	1.32	1.41	1.45

C I = \frac{λ_{m a x} - n}{n - 1}

(A5)

C R = \frac{C I}{R I}

(A6)

Appendix A.2. Steps of Technique for Order Preference by Similarity to Ideal Solution

The purpose of the decision problem is determined, and evaluation criteria and alternatives are decided. The decision matrix (

A

) is created using alternatives and criteria. In the decision matrix (Equation (A7)), the alternatives are in the rows, the criteria are in the columns, and the elements of the matrix reflect the characteristics that the criteria show concerning different alternatives. Each element

x_{i j}

represents the observed or evaluated value of the

i - t h

criterion associated with the

j - t h

alternative, where

i = 1,2, \dots, n

;

j = 1,2, \dots, m

. The normalized decision matrix (

R

) is obtained using Equations (A8) and (A9). The normalized matrix values are multiplied by the

W_{i}

criteria weight values obtained by the weighting method (AHP method and equal weight method in this study) to obtain the weighted normalized matrix (

V

). The

V

matrix is given in Equation (A10).

A = [\begin{matrix} x_{11} & \dots & x_{1 n} \\ ⋮ & ⋱ & ⋮ \\ x_{m 1} & \dots & x_{m n} \end{matrix}]

(A7)

r_{i j} = \frac{x_{i j}}{\sqrt{\sum_{i = 1}^{n} x_{i j}^{2}}}, i = 1,2, \dots, n; j = 1,2, \dots, m

(A8)

R = [\begin{matrix} r_{11} & \dots & r_{1 n} \\ ⋮ & ⋱ & ⋮ \\ r_{m 1} & \dots & r_{m n} \end{matrix}]

(A9)

V = [\begin{matrix} W_{1} r_{11} & \dots & W_{n} r_{1 n} \\ ⋮ & ⋱ & ⋮ \\ W_{1} r_{m 1} & \dots & W_{n} r_{m n} \end{matrix}] = [\begin{matrix} v_{11} & \dots & v_{1 n} \\ ⋮ & ⋱ & ⋮ \\ v_{m 1} & \dots & v_{m n} \end{matrix}]

(A10)

The positive ideal solution (

A^{*}

) and the negative ideal solution (

A^{-}

) are identified. In these expressions,

I

refers to the set of benefit criteria to be maximized, whereas

I^{'}

represents the set of cost criteria intended for minimization. The separation metrics in Equations (A11) and (A12) are obtained by measuring the distance of each alternative from the positive ideal (

S_{j}^{+}

) and negative ideal (

S_{j}^{-}

) reference points. The relative closeness (RC) is calculated as indicated in Equation (A13) for each alternative. Alternatives are then ranked in descending order according to their RC values, with the highest value indicating the most preferred option.

S_{j}^{+} = \sqrt{\sum_{j = 1}^{m} {(V_{i j} - V_{j}^{+})}^{2}}

(A11)

S_{j}^{-} = \sqrt{\sum_{j = 1}^{m} {(V_{i j} - V_{j}^{-})}^{2}}

(A12)

R C = \frac{S_{j}^{-}}{S_{j}^{-} + S_{j}^{+}}

(A13)

Appendix A.3. Steps of VlseKriterijumska Optimizacija I Kompromisno Resenje

The purpose of the decision problem is determined; evaluation criteria and alternatives are decided. The decision matrix (

A

) is created using alternatives and criteria. In the decision matrix (Equation (A14)), the alternatives are in the rows, the criteria are in the columns, and the elements of the matrix reflect the characteristics that the criteria show concerning different alternatives. Each element

x_{i j}

represents the observed or evaluated value of the

i - t h

criterion associated with the

j - t h

alternative, where

i = 1,2, \dots, n

;

j = 1,2, \dots, m

.

A = [\begin{matrix} x_{11} & \dots & x_{1 n} \\ ⋮ & ⋱ & ⋮ \\ x_{m 1} & \dots & x_{m n} \end{matrix}]

(A14)

The best (

f_{i}^{*}

) and worst (

f_{i}^{-}

) values for the criteria are determined. In determining these values, the benefit or cost effect of the criteria on the created model is taken into account. The average group value (

S_{j}

) and the worst group value (

R_{j}

) of the alternatives are determined. The

S_{j}

and

R_{j}

values are calculated by Equation (A15) and Equation (A16), respectively.

W_{i}

criteria weight values are obtained by the weighting method (AHP method and equal weight method in this study). The maximum group benefit value (

Q_{j}

) of the alternatives is determined. The

Q_{j}

value is calculated with the following Equation (A17). The calculated values of

Q_{j}

,

S_{j}

, and

R_{j}

are ranked in ascending order. The alternative with the lowest

Q_{j}

value is considered the most favorable option in the decision-making process.

S_{j} = \sum \frac{w_{i} (f_{i}^{*} - f_{i j})}{(f_{i}^{*} - f_{i}^{-})} j = 1,2, \dots, m

(A15)

R_{j} = m a x \frac{w_{i} (f_{i}^{*} - f_{i j})}{(f_{i}^{*} - f_{i}^{-})} j = 1,2, \dots, m

(A16)

Q_{j} = \frac{v (S_{j} - S^{*})}{(S^{-} - S^{*})} + \frac{(1 - v) (R_{j} - R^{*})}{(R^{-} - R^{*})}

(A17)

where

$S^{*} = {m i n}_{j} S_{j}$ $S^{-} = {m a x}_{j} S_{j}$
$R^{*} = {m i n}_{j} R_{j}$ $R^{-} = {m a x}_{j} R_{j}$
$v : the weight of the strategy that provides maximum group benefit$ .
$1 - v : the weight of the strategy that provides regret for those with opposing views .$

The

v

value is generally evaluated as 0.5.

In the final stage, the validity of the proposed best alternative is evaluated using two conditions: the acceptable advantage and the acceptable stability. For a result to be accepted, at least one of these conditions must be satisfied. According to the acceptable advantage condition, there should be a significant difference between the best and second-best alternatives. This is expressed as

Q (P_{2}) - Q (P_{1}) \geq D (Q)

. Here,

P_{1}

represents the alternative with the minimum

Q_{j}

value (the best), and

P_{2}

denotes the second-best alternative.

D (Q)

is calculated as

\frac{1}{(m - 1)}

, where

m

is the total number of alternatives. The acceptable stability requires that the alternative with the best

Q_{j}

value must also be ranked first in at least one of the

S_{j}

or

R_{j}

rankings. If neither of these conditions are met, the result may not be reliable. In such cases, the DMs are advised to (i) consider

P_{1}

and

P_{2}

if the stability condition fails, or (ii) evaluate all alternatives from

P_{1}

to

P_{m}

, where the inequality Q(

P_{m}

) − Q(

P_{1}

) ≥

D (Q)

is considered, if the advantage condition is not satisfied.

Table A3. Technique for Order Preference by Similarity to Ideal Solution–Analytical Hierarchy Process results.

Organization for Economic Co-Operation and Development Countries	Criterion Codes						$S_{j}^{+}$	$S_{j}^{-}$	$R C$	Rank
	C1	C2	C3	C4	C5	C6	$S_{j}^{+}$	$S_{j}^{-}$	$R C$	Rank
Germany	0.0593	0.0120	0.0054	0.0050	0.0030	0.0028	0.1349	0.1371	0.5042	17
United States	0.0699	0.0120	0.0069	0.1231	0.0040	0.0064	0.0603	0.1865	0.7556	11
Australia	0.0216	0.0111	0.0167	0.1103	0.0060	0.0052	0.0762	0.1725	0.6936	12
Austria	0.0603	0.0118	0.0088	0.0008	0.0090	0.0057	0.1367	0.1361	0.4990	18
Belgium	0.0670	0.0245	0.0048	0.0004	0.0023	0.0038	0.1391	0.1284	0.4801	31
United Kingdom	0.0590	0.0222	0.0051	0.0052	0.0025	0.0017	0.1357	0.1278	0.4850	29
Czechia	0.0518	0.0137	0.0128	0.0011	0.0061	0.0049	0.1364	0.1332	0.4941	24
Denmark	0.0539	0.0122	0.0072	0.0008	0.0049	0.0071	0.1376	0.1351	0.4954	22
Estonia	0.0297	0.0095	0.0140	0.0003	0.0065	0.0246	0.1384	0.1361	0.4957	21
Finland	0.0235	0.0090	0.0157	0.0007	0.0099	0.0263	0.1397	0.1359	0.4932	25
France	0.0608	0.0143	0.0106	0.0087	0.0061	0.0033	0.1299	0.1350	0.5103	16
South Korea	0.0579	0.0259	0.0135	0.0005	0.0129	0.0031	0.1376	0.1218	0.4694	33
Netherlands	0.0667	0.0252	0.0120	0.0005	0.0054	0.0095	0.1352	0.1274	0.4853	28
Ireland	0.0729	0.1379	0.0070	0.0013	0.0108	0.0014	0.1883	0.0600	0.2418	38
Spain	0.0358	0.0146	0.0201	0.0080	0.0098	0.0034	0.1324	0.1298	0.4950	23
Israel	0.0284	0.0244	0.0096	0.0002	0.0019	0.0023	0.1453	0.1206	0.4536	35
Sweden	0.0429	0.0104	0.0081	0.0009	0.0048	0.0149	0.1375	0.1353	0.4959	20
Switzerland	0.0467	0.0163	0.0045	0.0005	0.0057	0.0005	0.1420	0.1292	0.4765	32
Italy	0.0471	0.0104	0.0133	0.0038	0.0098	0.0025	0.1354	0.1344	0.4982	19
Iceland	0.0264	0.0129	0.0317	0.0006	0.0099	0.0019	0.1396	0.1326	0.4870	27
Japan	0.0575	0.0200	0.0074	0.0014	0.0074	0.0020	0.1382	0.1281	0.4808	30
Canada	0.0264	0.0116	0.0161	0.0173	0.0033	0.0151	0.1256	0.1348	0.5175	14
Colombia	0.0363	0.0485	0.0564	0.0130	0.0384	0.0142	0.1310	0.1077	0.4511	36
Costa Rica	0.0396	0.0671	0.0317	0.0005	0.0367	0.0053	0.1505	0.0794	0.3454	37
Latvia	0.0331	0.0103	0.0305	0.0006	0.0164	0.0403	0.1327	0.1396	0.5126	15
Lithuania	0.0334	0.0129	0.0246	0.0009	0.0128	0.0098	0.1368	0.1312	0.4897	26
Luxembourg	0.0474	0.0209	0.0014	0.0000	0.0028	0.0028	0.1430	0.1259	0.4682	34
Hungary	0.0502	0.0152	0.0251	0.0015	0.0106	0.0022	0.1339	0.1319	0.9836	4
Mexico	0.0329	0.0101	0.0275	0.0295	0.0321	0.0006	0.1176	0.1346	0.9955	1
Norway	0.0355	0.0194	0.0116	0.0003	0.0057	0.0018	0.1422	0.1250	0.9859	2
Poland	0.0387	0.0144	0.0163	0.0044	0.0203	0.0031	0.1369	0.1275	0.9761	6
Portugal	0.0456	0.0164	0.0157	0.0012	0.0065	0.0074	0.1361	0.1296	0.9461	8
Slovak Republic	0.0507	0.0124	0.0132	0.0006	0.0078	0.0031	0.1374	0.1337	0.9775	5
Slovenia	0.0582	0.0229	0.0110	0.0002	0.0096	0.0047	0.1377	0.1252	0.9637	7
Chile	0.0550	0.0265	0.0254	0.0032	0.0158	0.0161	0.1296	0.1226	0.8837	10
Türkiye	0.0247	0.0119	0.0402	0.0116	0.0414	0.0019	0.1350	0.1324	0.9859	3
New Zealand	0.0747	0.1331	0.0420	0.0031	0.0146	0.0374	0.1737	0.0811	0.6843	13
Greece	0.0356	0.0173	0.0256	0.0018	0.0274	0.0073	0.1378	0.1248	0.9450	9
$A^{*}$	0.0747	0.0090	0.0564	0.1231	0.0019	0.0403
$A^{-}$	0.0216	0.1379	0.0014	0.0000	0.0414	0.0005

Table A4. Technique for Order Preference by Similarity to Ideal Solution–equal weight results.

Organization for Economic Co-Operation and Development Countries	Criterion Codes						$S_{j}^{+}$	$S_{j}^{-}$	$R C$	Rank
	C1	C2	C3	C4	C5	C6	$S_{j}^{+}$	$S_{j}^{-}$	$R C$	Rank
Germany	0.0331	0.0087	0.0070	0.0049	0.0053	0.0060	0.1567	0.1157	0.4247	22
United States	0.0390	0.0087	0.0089	0.1206	0.0070	0.0139	0.0982	0.1677	0.6306	11
Australia	0.0120	0.0080	0.0218	0.1081	0.0105	0.0114	0.0980	0.1565	0.6150	12
Austria	0.0336	0.0086	0.0115	0.0008	0.0158	0.0123	0.1553	0.1108	0.4164	28
Belgium	0.0374	0.0178	0.0062	0.0004	0.0040	0.0084	0.1595	0.1104	0.4091	31
United Kingdom	0.0329	0.0161	0.0067	0.0051	0.0043	0.0037	0.1582	0.1104	0.4109	30
Czechia	0.0289	0.0099	0.0166	0.0010	0.0108	0.0108	0.1539	0.1120	0.4212	25
Denmark	0.0301	0.0089	0.0094	0.0008	0.0086	0.0155	0.1545	0.1140	0.4245	23
Estonia	0.0166	0.0069	0.0182	0.0003	0.0113	0.0537	0.1393	0.1244	0.4719	17
Finland	0.0131	0.0066	0.0204	0.0007	0.0174	0.0573	0.1384	0.1237	0.4720	16
France	0.0339	0.0104	0.0138	0.0085	0.0106	0.0072	0.1508	0.1123	0.4267	20
South Korea	0.0323	0.0188	0.0175	0.0005	0.0226	0.0068	0.1573	0.0989	0.3861	36
Netherlands	0.0372	0.0183	0.0156	0.0005	0.0095	0.0206	0.1499	0.1089	0.4209	26
Ireland	0.0407	0.1001	0.0091	0.0013	0.0190	0.0030	0.1859	0.0612	0.2477	38
Spain	0.0200	0.0106	0.0262	0.0078	0.0172	0.0075	0.1486	0.1088	0.4227	24
Israel	0.0159	0.0177	0.0125	0.0002	0.0034	0.0049	0.1609	0.1082	0.4021	32
Sweden	0.0240	0.0075	0.0105	0.0009	0.0083	0.0324	0.1473	0.1178	0.4444	18
Switzerland	0.0261	0.0118	0.0059	0.0004	0.0100	0.0010	0.1638	0.1091	0.3997	35
Italy	0.0263	0.0075	0.0173	0.0037	0.0171	0.0053	0.1551	0.1100	0.4151	29
Iceland	0.0148	0.0093	0.0413	0.0006	0.0173	0.0040	0.1529	0.1134	0.4257	21
Japan	0.0321	0.0145	0.0096	0.0014	0.0129	0.0043	0.1597	0.1065	0.4001	34
Canada	0.0148	0.0084	0.0210	0.0169	0.0058	0.0329	0.1313	0.1206	0.4786	15
Colombia	0.0202	0.0352	0.0735	0.0127	0.0673	0.0311	0.1422	0.1025	0.4187	27
Costa Rica	0.0221	0.0487	0.0413	0.0005	0.0642	0.0115	0.1647	0.0669	0.2889	37
Latvia	0.0184	0.0075	0.0398	0.0006	0.0287	0.0878	0.1293	0.1396	0.5192	14
Lithuania	0.0186	0.0093	0.0321	0.0009	0.0224	0.0215	0.1461	0.1101	0.4297	19
Luxembourg	0.0265	0.0152	0.0018	0.0000	0.0048	0.0060	0.1633	0.1096	0.4017	33
Hungary	0.0280	0.0111	0.0327	0.0015	0.0186	0.0048	0.1522	0.1098	0.9583	4
Mexico	0.0183	0.0073	0.0358	0.0289	0.0562	0.0013	0.1437	0.1044	0.9874	1
Norway	0.0198	0.0141	0.0151	0.0003	0.0099	0.0039	0.1597	0.1075	0.9649	2
Poland	0.0216	0.0104	0.0212	0.0043	0.0355	0.0068	0.1558	0.0996	0.9361	6
Portugal	0.0254	0.0119	0.0204	0.0012	0.0115	0.0161	0.1502	0.1107	0.8729	8
Slovak Republic	0.0283	0.0090	0.0173	0.0006	0.0136	0.0067	0.1563	0.1109	0.9423	5
Slovenia	0.0325	0.0166	0.0143	0.0002	0.0169	0.0103	0.1561	0.1035	0.9096	7
Chile	0.0307	0.0192	0.0331	0.0031	0.0277	0.0352	0.1381	0.1052	0.7493	10
Türkiye	0.0138	0.0087	0.0523	0.0113	0.0725	0.0041	0.1580	0.1051	0.9621	3
New Zealand	0.0417	0.0966	0.0547	0.0030	0.0256	0.0816	0.1511	0.1112	0.5769	13
Greece	0.0198	0.0126	0.0334	0.0017	0.0480	0.0158	0.1530	0.0977	0.8606	9
$A^{*}$	0.0417	0.0066	0.0735	0.1206	0.0034	0.0878
$A^{-}$	0.0120	0.1001	0.0018	0.0000	0.0725	0.0010

Table A5. VlseKriterijumska Optimizacija I Kompromisno Resenje–Analytical Hierarchy Process results.

Organization for Economic Co-Operation and Development Countries	Criterion Codes						$S_{j}$	$R_{j}$	$Q_{j}$	Rank
	C1	C2	C3	C4	C5	C6	$S_{j}$	$R_{j}$	$Q_{j}$	Rank
Germany	0.2126	0.0053	0.0093	0.0069	0.0026	0.0044	0.2411	0.2126	0.4568	22
United States	0.2725	0.0053	0.0128	0.1704	0.0050	0.0114	0.4774	0.2725	0.8990	36
Australia	0.0000	0.0036	0.0357	0.1526	0.0099	0.0091	0.2110	0.1526	0.4464	21
Austria	0.2181	0.0050	0.0173	0.0010	0.0171	0.0100	0.2685	0.2181	0.6935	32
Belgium	0.2560	0.0276	0.0079	0.0005	0.0008	0.0065	0.2992	0.2560	0.8366	35
United Kingdom	0.2111	0.0236	0.0088	0.0072	0.0013	0.0023	0.2543	0.2111	0.6672	31
Czechia	0.1703	0.0083	0.0265	0.0014	0.0102	0.0086	0.2254	0.1703	0.5132	25
Denmark	0.1819	0.0057	0.0135	0.0010	0.0073	0.0128	0.2222	0.1819	0.5569	26
Estonia	0.0456	0.0009	0.0293	0.0004	0.0110	0.0465	0.1337	0.0465	0.0459	3
Finland	0.0110	0.0000	0.0333	0.0009	0.0193	0.0497	0.1142	0.0497	0.0579	4
France	0.2209	0.0095	0.0215	0.0119	0.0100	0.0055	0.2793	0.2209	0.7041	33
South Korea	0.2044	0.0302	0.0282	0.0006	0.0265	0.0051	0.2949	0.2044	0.6418	29
Netherlands	0.2544	0.0288	0.0248	0.0007	0.0085	0.0173	0.3345	0.2544	0.8305	34
Ireland	0.2895	0.2302	0.0131	0.0018	0.0215	0.0017	0.5578	0.2895	0.9629	37
Spain	0.0802	0.0099	0.0436	0.0110	0.0190	0.0057	0.1694	0.0802	0.1732	11
Israel	0.0385	0.0275	0.0191	0.0002	0.0000	0.0035	0.0888	0.0385	0.0156	2
Sweden	0.1202	0.0024	0.0156	0.0012	0.0069	0.0277	0.1741	0.1202	0.3242	15
Switzerland	0.1418	0.0130	0.0074	0.0006	0.0092	0.0000	0.1719	0.1418	0.4055	18
Italy	0.1440	0.0024	0.0278	0.0052	0.0190	0.0038	0.2022	0.1440	0.4140	19
Iceland	0.0274	0.0069	0.0706	0.0007	0.0192	0.0027	0.1274	0.0706	0.1367	6
Japan	0.2025	0.0196	0.0139	0.0019	0.0132	0.0029	0.2540	0.2025	0.6348	28
Canada	0.0273	0.0046	0.0344	0.0239	0.0033	0.0282	0.1217	0.0344	0.0000	1
Colombia	0.0828	0.0705	0.1282	0.0179	0.0882	0.0265	0.4141	0.1282	0.3542	16
Costa Rica	0.1015	0.1037	0.0706	0.0007	0.0839	0.0092	0.3697	0.1037	0.2617	14
Latvia	0.0646	0.0023	0.0679	0.0008	0.0349	0.0766	0.2471	0.0766	0.1594	8
Lithuania	0.0664	0.0068	0.0542	0.0012	0.0263	0.0180	0.1730	0.0664	0.1208	5
Luxembourg	0.1455	0.0212	0.0000	0.0000	0.0020	0.0044	0.1731	0.1455	0.4195	20
Hungary	0.1611	0.0111	0.0552	0.0021	0.0210	0.0033	0.2538	0.1611	0.4784	23
Mexico	0.0636	0.0018	0.0607	0.0407	0.0729	0.0003	0.2401	0.0729	0.1455	7
Norway	0.0782	0.0185	0.0238	0.0004	0.0090	0.0025	0.1324	0.0782	0.1653	9
Poland	0.0963	0.0095	0.0348	0.0060	0.0443	0.0051	0.1960	0.0963	0.2336	13
Portugal	0.1353	0.0132	0.0333	0.0016	0.0112	0.0133	0.2079	0.1353	0.3810	17
Slovak Republic	0.1638	0.0060	0.0276	0.0007	0.0141	0.0050	0.2173	0.1638	0.4887	24
Slovenia	0.2062	0.0248	0.0223	0.0002	0.0186	0.0082	0.2804	0.2062	0.6488	30
Chile	0.1884	0.0311	0.0560	0.0044	0.0335	0.0302	0.3436	0.1884	0.5814	27
Türkiye	0.0177	0.0052	0.0904	0.0159	0.0954	0.0027	0.2273	0.0954	0.2303	12
New Zealand	0.2993	0.2216	0.0945	0.0042	0.0307	0.0711	0.7215	0.2993	1.0000	38
Greece	0.0787	0.0148	0.0565	0.0024	0.0615	0.0131	0.2270	0.0787	0.1673	10
$v = 0.5$	$S^{*} = 0.0888$				$R^{*} = 0.0344$
$v = 0.5$	$S^{-} = 0.7215$				$R^{-} = 0.2993$

Table A6. VlseKriterijumska Optimizacija I Kompromisno Resenje–equal weight results.

Organization for Economic Co-Operation and Development Countries	Criterion Codes						$S_{j}$	$R_{j}$	$Q_{j}$	Rank
	C1	C2	C3	C4	C5	C6	$S_{j}$	$R_{j}$	$Q_{j}$	Rank
Germany	0.1186	0.0038	0.0122	0.0068	0.0046	0.0096	0.1555	0.1186	0.3989	12
United States	0.1521	0.0039	0.0166	0.1670	0.0088	0.0248	0.3732	0.1670	1.0000	33
Australia	0.0000	0.0026	0.0466	0.1496	0.0173	0.0199	0.2360	0.1496	0.8775	32
Austria	0.1217	0.0036	0.0226	0.0010	0.0300	0.0217	0.2006	0.1217	0.6812	26
Belgium	0.1429	0.0200	0.0102	0.0005	0.0014	0.0141	0.1891	0.1429	0.8301	30
United Kingdom	0.1178	0.0171	0.0114	0.0070	0.0023	0.0051	0.1608	0.1178	0.6538	25
Czechia	0.0950	0.0060	0.0345	0.0014	0.0179	0.0188	0.1737	0.0950	0.4937	16
Denmark	0.1015	0.0041	0.0176	0.0010	0.0127	0.0278	0.1648	0.1015	0.5391	18
Estonia	0.0255	0.0006	0.0381	0.0004	0.0193	0.1014	0.1853	0.1014	0.5387	17
Finland	0.0061	0.0000	0.0434	0.0009	0.0338	0.1084	0.1925	0.1084	0.5874	21
France	0.1233	0.0069	0.0280	0.0117	0.0175	0.0119	0.1994	0.1233	0.6922	27
South Korea	0.1141	0.0219	0.0367	0.0006	0.0464	0.0110	0.2307	0.1141	0.6275	23
Netherlands	0.1419	0.0209	0.0323	0.0007	0.0149	0.0377	0.2484	0.1419	0.8237	29
Ireland	0.1615	0.1670	0.0171	0.0017	0.0377	0.0037	0.3888	0.1670	1.0000	33
Spain	0.0448	0.0072	0.0568	0.0107	0.0333	0.0125	0.1652	0.0568	0.2244	3
Israel	0.0215	0.0200	0.0249	0.0002	0.0000	0.0075	0.0741	0.0249	0.0000	1
Sweden	0.0671	0.0018	0.0204	0.0012	0.0120	0.0604	0.1628	0.0671	0.2970	5
Switzerland	0.0791	0.0094	0.0096	0.0006	0.0161	0.0000	0.1148	0.0791	0.3816	9
Italy	0.0804	0.0018	0.0362	0.0051	0.0332	0.0083	0.1649	0.0804	0.3905	10
Iceland	0.0153	0.0050	0.0919	0.0007	0.0336	0.0058	0.1524	0.0919	0.4718	15
Japan	0.1130	0.0142	0.0181	0.0019	0.0231	0.0062	0.1765	0.1130	0.6202	22
Canada	0.0153	0.0033	0.0448	0.0234	0.0058	0.0614	0.1540	0.0614	0.2569	4
Colombia	0.0462	0.0512	0.1670	0.0175	0.1544	0.0578	0.4941	0.1670	1.0000	33
Costa Rica	0.0567	0.0752	0.0920	0.0007	0.1470	0.0201	0.3917	0.1470	0.8592	31
Latvia	0.0361	0.0016	0.0885	0.0008	0.0611	0.1670	0.3551	0.1670	1.0000	33
Lithuania	0.0370	0.0050	0.0706	0.0012	0.0461	0.0393	0.1992	0.0706	0.3217	6
Luxembourg	0.0812	0.0154	0.0000	0.0000	0.0036	0.0096	0.1097	0.0812	0.3962	11
Hungary	0.0899	0.0080	0.0719	0.0020	0.0367	0.0072	0.2158	0.0899	0.4575	13
Mexico	0.0355	0.0013	0.0791	0.0399	0.1277	0.0006	0.2841	0.1277	0.7233	28
Norway	0.0436	0.0134	0.0310	0.0004	0.0158	0.0055	0.1098	0.0436	0.1317	2
Poland	0.0537	0.0069	0.0453	0.0059	0.0776	0.0111	0.2005	0.0776	0.3706	8
Portugal	0.0755	0.0096	0.0434	0.0016	0.0196	0.0290	0.1787	0.0755	0.3561	7
Slovak Republic	0.0914	0.0043	0.0360	0.0007	0.0247	0.0109	0.1681	0.0914	0.4681	14
Slovenia	0.1151	0.0180	0.0291	0.0002	0.0327	0.0178	0.2129	0.1151	0.6347	24
Chile	0.1051	0.0226	0.0730	0.0043	0.0587	0.0657	0.3294	0.1051	0.5646	19
Türkiye	0.0099	0.0038	0.1177	0.0156	0.1670	0.0060	0.3199	0.1670	1.0000	33
New Zealand	0.1670	0.1608	0.1231	0.0041	0.0538	0.1551	0.6640	0.1670	1.0000	33
Greece	0.0439	0.0108	0.0736	0.0024	0.1077	0.0285	0.2668	0.1077	0.5830	20
$v = 0.5$	$S^{*} = 0.0741$				$R^{*} = 0.0249$
$v = 0.5$	$S^{-} = 0.6640$				$R^{-} = 0.1670$

References

The World Bank. Agriculture and Food. 2021. Available online: https://www.worldbank.org/en/topic/agriculture/overview (accessed on 19 September 2021).
Pannell, D.; Zilberman, D. Understanding adoption of innovations and behavior change to improve agricultural policy. Appl. Econ. Perspect. Policy 2020, 42, 3–7. [Google Scholar] [CrossRef]
Dirik, C.; Sahin, S.; Atıcı, K.B. Veri zarflama analizi ile etkinlik ölçümü ve parçalı elastiklik analizi: OECD ülkelerinin tarımsal performansları üzerine bir uygulama. Veriml. Derg. 2023, 57, 1–22. [Google Scholar] [CrossRef]
Anderson, K. Globalization’s effects on world agricultural trade, 1960–2050. Philos. Trans. R. Soc. B Biol. Sci. 2010, 365, 3007–3021. [Google Scholar] [CrossRef]
Beşen, T.; Olhan, E. Tarımsal Çevre Göstergelerinin AB, OECD VE FAO Kapsamında Değerlendirilmesi. Bahçe 2021, 50, 71–86. [Google Scholar]
Poursaeed, A.; Mirdamadi, M.; Malekmohammadi, I.; Hosseini, J.F. The partnership models of agricultural sustainable development based on Multiple Criteria Decision Making (MCDM) in Iran. Afr. J. Agric. Res. 2010, 5, 3185–3190. [Google Scholar]
Alphonce, C.B. Application of the analytic hierarchy process in agriculture in developing countries. Agric. Syst. 1997, 53, 97–112. [Google Scholar] [CrossRef]
Rezaei-Moghaddam, K.; Karami, E. A multiple criteria evaluation of sustainable agricultural development models using AHP. Environ. Dev. Sustain. 2008, 10, 407–426. [Google Scholar] [CrossRef]
Aktan, H.E.; Samut, P.K. Agricultural performance evaluation by integrating fuzzy AHP and VIKOR methods. Int. J. Appl. Decis. Sci. 2013, 6, 324–344. [Google Scholar] [CrossRef]
Talukder, B.; Blay-Palmer, A.; Hipel, K.W.; VanLoon, G.W. Elimination method of multi-criteria decision analysis (mcda): A simple methodological approach for assessing agricultural sustainability. Sustainability 2017, 9, 287. [Google Scholar] [CrossRef]
Cicciù, B.; Schramm, F.; Schramm, V.B. Multi-criteria decision making/aid methods for assessing agricultural sustainability: A literature review. Environ. Sci. Policy 2022, 138, 85–96. [Google Scholar] [CrossRef]
Kumar, A.; Pant, S. Analytical hierarchy process for sustainable agriculture: An overview. MethodsX 2023, 10, 101954. [Google Scholar] [CrossRef]
Atlı, H.F. Sürdürülebilir Tarımsal Pazarlama İçin Tarım Politikasına Etki Eden Kriterlerin Değerlendirilmesinde Bwm Çok Kriterli Karar Verme Yöntemi Uygulaması. Electron. J. Soc. Sci. 2024, 23, 1582–1603. [Google Scholar] [CrossRef]
Aydın, U.; Kaya, G.; Karadayı, M.A.; Ülengin, F.; Ülengin, B. OECD ülkelerinin tarimsal ticaret performansinin değerlendirilmesi: Dinamik bir model önerisi. Endüstri Müh. 2023, 34, 110–136. [Google Scholar] [CrossRef]
World Bank. 2025. Available online: https://www.worldbank.org/ext/en/home (accessed on 10 June 2025).
Santos-Paulino, A.; Thirlwall, A.P. The impact of trade liberalisation on exports, imports and the balance of payments of developing countries. Econ. J. 2004, 114, F50–F72. [Google Scholar] [CrossRef]
Alkire, B.C.; Shrime, M.G.; Dare, A.J.; Vincent, J.R.; Meara, J.G. Global economic consequences of selected surgical diseases: A modelling study. Lancet Glob. Health 2015, 3, S21–S27. [Google Scholar] [CrossRef]
Lozano, R.; Ceulemans, K.; Alonso-Almeida, M.; Huisingh, D.; Lozano, F.J.; Waas, T.; Lambrechts, W.; Lukman, R.; Hugé, J. A review of commitment and implementation of sustainable development in higher education: Results from a worldwide survey. J. Clean. Prod. 2015, 108, 1–18. [Google Scholar] [CrossRef]
Zaman, G.; Goschin, Z. A new multidimensional ranking of shadow economy for EU countries. Rev. Rom. Econ. 2016, 43, 14–33. [Google Scholar]
World Bank. World Development Indicators. 2025. Available online: https://databank.worldbank.org/source/world-development-indicators (accessed on 17 June 2025).
Saaty, T.L. The Analytic Hierarchy Process; Mc Graw-Hill: New York, NY, USA, 1980. [Google Scholar]
Saaty, T.L. Rank generation, preservation, and reversal in the analytic hierarchy decision process. Decis. Sci. 1987, 18, 157–177. [Google Scholar] [CrossRef]
Saaty, T.L. How to make a decision: The Analytic Hierarchy Process. Eur. J. Oper. Res. 1990, 48, 9–26. [Google Scholar] [CrossRef]
Saaty, T.L. Decision making with the Analytic Hierarchy Process. Int. J. Serv. Sci. 2008, 1, 83–98. [Google Scholar] [CrossRef]
Murat, Y.S.; Arslan, T.; Cakici, Z.; Akçam, C. Analytica Hierarchy Process (AHP) based decision support system for urban intersections in transportation planning. In Using Decision Support Systems for Transportation Efficiency; IGI Global: Hershey, PA, USA, 2016; pp. 203–222. [Google Scholar] [CrossRef]
Hwang, C.L.; Yoon, K. Methods for multiple attribute decision making. In Multiple Attribute Decision Making: Methods and Applications a State-of-the-Art Survey; Springer: Berlin/Heidelberg, Germany, 1981; pp. 58–191. [Google Scholar] [CrossRef]
Ekin, E.; Dolanbay, G. AHP temelli TOPSIS yöntemi ile yer seçim problemine ilişkin bir uygulama. İstanbul Gelişim Univ. Sos. Bilim. Derg. 2024, 11, 301–317. [Google Scholar] [CrossRef]
Opricovic, S. Multicriteria Optimization of Civil Engineering Systems. Ph.D. Thesis, University of Belgrade, Belgrade, Serbia, 1998. [Google Scholar]
Opricovic, S.; Tzeng, G.H. Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 2004, 156, 445–455. [Google Scholar] [CrossRef]
Gülcan, N. Application of the VIKOR method in financial performance evaluation of financial leasing and factoring companies. Ekon. Polit. Finans Araşt. Derg. 2022, 7, 235–247. [Google Scholar] [CrossRef]
Meşe, B.; Özdemir, L. Entropi temelli TOPSIS ve BORDA sayım yöntemleri ile gıda işletmelerinin performanslarının değerlendirilmesi. Alanya Akad. Bakış 2022, 6, 2809–2829. [Google Scholar] [CrossRef]
Lamboray, C. A comparison between the prudent order and the ranking obtained with Borda’s, Copeland’s, Slater’s and Kemeny’s rules. Math. Soc. Sci. 2007, 54, 1–16. [Google Scholar] [CrossRef]
Ho, T.K.; Hull, J.J.; Sriharı, S.N. On Multiple Classifier Systems for Pattern Recognition. In Proceedings of the 11th IAPR International Conference on Pattern Recognition (ICPR), The Hague, The Netherlands, 30 August–3 September 1992; pp. 1–5. [Google Scholar] [CrossRef]
Lansdowne, Z.F.; Woodward, B.S. Applying the Borda ranking method. Air Force J. Logist. 1996, 20, 27–29. [Google Scholar]
Ömürbek, N.; Dağ, O.; Eren, H. EM algoritmasına göre kümelenen havalimanlarının borda sayım yöntemi ile değerlendirilmesi. Atatürk Univ. Iktis. Idari Bilim. Derg. 2020, 34, 491–514. [Google Scholar] [CrossRef]
Liu, D.; Cho, S.Y.; Sun, D.M.; Qiu, Z.D. A Spearman correlation coefficient ranking for matching-score fusionon speaker recognition. In Proceedings of the TENCON 2010–2010 IEEE Region 10 Conference, Fukuoka, Japan, 21–24 November 2010. [Google Scholar] [CrossRef]
Güler, E.; Yerel Kandemir, S.; Acikkalp, E.; Ahmadi, M.H. Evaluation of sustainable energy performance for OECD countries. Energy Sources Part B Econ. Plan. Policy 2021, 16, 491–514. [Google Scholar] [CrossRef]
Meng, C.; Jiang, X.S.; Wang, J.; Wei, X.M. The complex network model for industrial data based on Spearman correlation coefficient. In Proceedings of the 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Atlanta, GA, USA, 14–17 July 2019; pp. 28–33. [Google Scholar] [CrossRef]
Tu, Y.; Chen, K.; Wang, H.; Li, Z. Regional water resources security evaluation based on a hybrid fuzzy BWM-TOPSIS method. Int. J. Environ. Res. Public Health 2020, 17, 4987. [Google Scholar] [CrossRef]
Trung, D.D. Application of TOPSIS and PIV methods for multi-criteria decision making in hard turning process. J. Mach. Eng. 2021, 21, 57–71. [Google Scholar] [CrossRef]
Aggarwal, V.; Gupta, S.; Patterh, M.S.; Singh, L.; Bhargava, C.; Sharma, P. Optimizing Solar Panel Selection: A Comparative Analysis using AHP, Entropy, and Equal Weights in TOPSIS Methodologies. In Proceedings of the 2023 Second IEEE International Conference on Measurement, Instrumentation, Control and Automation (ICMICA), Kurukshetra, India, 3–5 May 2024; pp. 1–5. [Google Scholar] [CrossRef]
Robles-Algarín, C.; Castrillo-Fernández, L.; Restrepo-Leal, D. Optimal Site Selection for Solar PV Systems in the Colombian Caribbean: Evaluating Weighting Methods in a TOPSIS Framework. Sustainability 2024, 16, 8716. [Google Scholar] [CrossRef]
Ou Yang, Y.P.; Shieh, H.M.; Tzeng, G.H. A VIKOR technique with applications based on DEMATEL and ANP. In Cutting-Edge Research Topics on Multiple Criteria Decision Making, Proceedings of the 20th International Conference, MCDM 2009, Chengdu/Jiuzhaigou, China, 21–26 June 2009; Springer: Berlin/Heidelberg, Germany, 2009; pp. 780–788. [Google Scholar] [CrossRef]
Hezer, S.; Gelmez, E.; Özceylan, E. Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment. J. Infect. Public Health 2021, 14, 775–786. [Google Scholar] [CrossRef] [PubMed]
Shanmugasundar, G.; Chohan, J.S.; Nag, A.; Samal, S.P.; Jangir, P.; Haldar, R. Parametric Optimization of Abrasive Water Jet Machining Process Using COPRAS and VIKOR Methods. MM Sci. J. 2024, 41, 7930–7938. [Google Scholar] [CrossRef]
Singh, A.; Malik, S.K. Major MCDM Techniques and their application—A Review. IOSR J. Eng. 2014, 4, 15–25. [Google Scholar] [CrossRef]
Mateusz, P.; Danuta, M.; Małgorzata, Ł.; Mariusz, B.; Kesra, N. TOPSIS and VIKOR methods in study of sustainable development in the EU countries. Procedia Comput. Sci. 2018, 126, 1683–1692. [Google Scholar] [CrossRef]
Papathanasiou, J.; Ploskas, N.; Bournaris, T.; Manos, B. A decision support system for multiple criteria alternative ranking using TOPSIS and VIKOR: A case study on social sustainability in agriculture. In Decision Support Systems VI-Addressing Sustainability and Societal Challenges, Proceedings of the 2nd International Conference, ICDSST 2016, Plymouth, UK, 23–25 May 2016; Springer International Publishing: Cham, Switzerland, 2016; pp. 3–15. [Google Scholar] [CrossRef]
Shekhovtsov, A.; Sałabun, W. A comparative case study of the VIKOR and TOPSIS rankings similarity. Procedia Comput. Sci. 2020, 176, 3730–3740. [Google Scholar] [CrossRef]
Taşabat, S.E.; Özkan, T.K. TOPSIS vs. VIKOR: A case study for determining development level of countries. In Multi-Criteria Decision Analysis in Management; IGI Global: Hershey, PA, USA, 2020; pp. 225–250. [Google Scholar] [CrossRef]
Mali, P.R.; Vishwakarma, R.J.; Isleem, H.F.; Khichad, J.S.; Patil, R.B. Performance evaluation of bamboo species for structural applications using TOPSIS and VIKOR: A comparative study. Constr. Build. Mater. 2024, 449, 138307. [Google Scholar] [CrossRef]
Sałabun, W.; Wątróbski, J.; Shekhovtsov, A. Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry 2020, 12, 1549. [Google Scholar] [CrossRef]
Sanchez-Lozano, J.; Alonso Larraz, M.; Fernandez-Martinez, M. A comparison between fuzzy TOPSIS and VIKOR to the selection of aircraft for airspace defense. Soc. Sci. Res. Netw. 2025, 24, 4166670. [Google Scholar] [CrossRef]
Serrai, W.; Abdelli, A.; Mokdad, L.; Hammal, Y. Towards an efficient and a more accurate web service selection using MCDM methods. J. Comput. Sci. 2017, 22, 253–267. [Google Scholar] [CrossRef]
Danış, Y.A. OECD Ülkelerinin TOPSIS, VIKOR ve GRA yöntemleri kullanılarak refah göstergelerine göre sıralanması ve bütünleşik bir çözüm önerisi. Eskişehir Osmangazi Univ. Iktis. Idari Bilim. Derg. 2022, 17, 433–454. Available online: https://dergipark.org.tr/en/pub/oguiibf/issue/70614/1073257 (accessed on 12 June 2025).
OECD. Agricultural Policy Monitoring and Evaluation 2024. 2024. Available online: https://www.oecd.org/en/publications/agricultural-policy-monitoring-and-evaluation-2024_74da57ed-en.html (accessed on 26 July 2025).

Figure 1. Flowchart of suggested Multi-Criteria Decision-Making framework.

Figure 2. Aggregated last ranks for Organization for Economic Co-Operation and Development countries.

Table 1. Decision matrix for Organization for Economic Co-Operation and Development countries.

	Criterion Codes
Organization for Economic Co-Operation and Development Countries	C1	C2	C3	C4	C5	C6
	(kg/ha)	(kg/ha)	(%)	(km²)	(%)	(%)
Germany	6998.1	130.14	0.742	165,910	1.246	0.882
United States	8252.2	130.56	0.945	4,058,104	1.662	2.044
Australia	2547.7	120.12	2.304	3,635,190	2.492	1.673
Austria	7112.4	128.26	1.216	25,974.59	3.735	1.812
Belgium	7906.2	265.71	0.655	13,656.7	0.936	1.230
United Kingdom	6966.8	241.44	0.708	172,151.4	1.024	0.541
Czechia	6113	148.62	1.759	35,297.9	2.548	1.585
Denmark	6355	132.84	0.989	26,180	2.044	2.277
Estonia	3502.4	103.49	1.922	9870	2.684	7.901
Finland	2777.3	98.08	2.159	22,680	4.108	8.429
France	7171.3	155.67	1.463	285,537.5	2.515	1.065
South Korea	6826.1	281.48	1.856	16,030	5.347	0.995
Netherlands	7872.3	273.25	1.655	18,120	2.256	3.032
Ireland	8606.5	1497.29	0.967	43,370	4.489	0.438
Spain	4227.4	157.99	2.768	262,284.5	4.059	1.104
Israel	3353.4	265.38	1.320	6435	0.796	0.727
Sweden	5064.7	112.91	1.115	30,029.1	1.973	4.767
Switzerland	5515.8	177.19	0.626	14,993.78	2.373	0.152
Italy	5562.8	112.80	1.833	124,030.3	4.050	0.786
Iceland	3120.8	139.87	4.364	18,720	4.094	0.595
Japan	6787.3	216.96	1.013	46,590	3.059	0.629
Canada	3120	126.06	2.223	569,910	1.365	4.841
Colombia	4280.2	526.89	7.771	427,180	15.928	4.567
Costa Rica	4672.8	728.37	4.367	18,110	15.205	1.689
Latvia	3900.2	111.81	4.206	19,700	6.789	12.907
Lithuania	3937.2	139.68	3.396	29,378	5.313	3.155
Luxembourg	5593.3	226.99	0.191	1328.1	1.144	0.884
Hungary	5920	165.49	3.456	50,436.9	4.393	0.703
Mexico	3878.9	109.25	3.783	971,260	13.312	0.195
Norway	4183.7	210.72	1.599	9850	2.348	0.5748
Poland	4562.5	156.06	2.245	144,994.6	8.399	0.999
Portugal	5379.7	178.36	2.162	39,622.9	2.713	2.370
Slovak Republic	5976.8	134.49	1.825	18,560	3.214	0.986
Slovenia	6864.8	249.02	1.511	6109.6	3.997	1.513
Chile	6491.2	287.17	3.504	105,955.4	6.548	5.174
Türkiye	2918.5	129.53	5.534	380,890	17.166	0.609
New Zealand	8812	1445.04	5.780	101,750	6.071	11.999
Greece	4194.7	188.19	3.532	58,671.9	11.358	2.327

Table 2. The pairwise comparison matrices of the Decision-Makers.

DM1 (Decision-Maker 1)
Criterion Codes
	C1	C2	C3	C4	C5	C6
C1: Grain yield (kg/ha)	1	1	3	1/2	5	3
C2: Fertilizer consumption (kg/ha)	1	1	3	1	3	4
C3: Value added by agriculture, forestry, and fishing (%)	1/3	1/3	1	2	3	2
C4: Agricultural land (km²)	2	1	1/2	1	5	7
C5: Employment in agriculture (%)	1/5	1/3	1/3	1/5	1	1
C6: Agricultural raw materials exports (%)	1/3	1/4	1/2	1/7	1	1
DM2 (Decision-Maker 2)
Criterion Codes
	C1	C2	C3	C4	C5	C6
C1: Grain yield (kg/ha)	1	2	5	3	1	3
C2: Fertilizer consumption (kg/ha)	1/2	1	4	2	3	2
C3: Value added by agriculture, forestry, and fishing (%)	1/5	1/4	1	3	1	2
C4: Agricultural land (km²)	1/3	1/2	1/3	1	1	1
C5: Employment in agriculture (%)	1	1/3	1	1	1	1
C6: Agricultural raw materials exports (%)	1/3	1/2	1/2	1	1	1
DM3 (Decision-Maker 3)
Criterion Codes
	C1	C2	C3	C4	C5	C6
C1: Grain yield (kg/ha)	1	2	4	3	2	5
C2: Fertilizer consumption (kg/ha)	1/2	1	4	1	2	1
C3: Value added by agriculture, forestry, and fishing (%)	1/4	1/4	1	1/2	1	2
C4: Agricultural land (km²)	1/3	1	2	1	3	2
C5: Employment in agriculture (%)	1/2	1/2	1	1/3	1	3
C6: Agricultural raw materials exports (%)	1/5	1	1/2	1/2	1/3	1

Table 3. The aggregated pairwise comparison matrix.

Criterion Codes
	C1	C2	C3	C4	C5	C6
C1: Grain yield (kg/ha)	1.000	1.587	3.915	1.651	2.154	3.557
C2: Fertilizer consumption (kg/ha)	0.630	1.000	3.634	1.260	2.621	2.000
C3: Value added by agriculture, forestry, and fishing (%)	0.255	0.275	1.000	1.442	1.442	2.000
C4: Agricultural land (km²)	0.606	0.794	0.693	1.000	2.466	2.410
C5: Employment in agriculture (%)	0.464	0.382	0.693	0.406	1.000	1.442
C6: Agricultural raw materials exports (%)	0.281	0.500	0.500	0.415	0.693	1.000

Table 4. Weights of agricultural performance evaluation criteria.

Criteria	$Weights (W_{i})$
C1: Grain yield (kg per hectare)	0.299
C2: Fertilizer consumption (kg per hectare of arable land)	0.230
C3: Value added by agriculture, forestry, and fishing (% of GDP)	0.128
C4: Agricultural land (in square kilometers)	0.171
C5: Employment in agriculture (% of total employment)	0.095
C6: Agricultural raw materials exports (% of merchandise exports)	0.077

Table 5. Ranking results for different weighting methods in Technique for Order Preference by Similarity to Ideal Solution and VlseKriterijumska Optimizacija I Kompromisno Resenje methods.

	AHP				Equal Weights
Organization for Economic Co-Operation and Development Countries	TOPSIS-AHP/Borda Values	VIKOR-AHP/Borda Values	Total Borda Scores	Last Rank-AHP	TOPSIS-Equal Weights/Borda Values	VIKOR-Equal Weights/Borda Values	Total Borda Scores	Last Rank-Equal Weights
Germany	21	16	37	22	16	26	42	15
United States	27	2	29	23	27	5	32	23
Australia	26	17	43	14	26	6	32	23
Austria	20	6	26	27	10	12	22	30
Belgium	7	3	10	37	7	8	15	36
United Kingdom	9	7	16	34	8	13	21	31
Czechia	14	13	27	25	13	22	35	21
Denmark	16	12	28	24	15	20	35	21
Estonia	17	35	52	8	21	21	42	15
Finland	13	34	47	11	22	17	39	19
France	22	5	27	25	18	11	29	28
South Korea	5	9	14	35	2	15	17	34
Netherlands	10	4	14	35	12	9	21	31
Ireland	0	1	1	38	0	5	5	38
Spain	15	27	42	16	14	35	49	9
Israel	3	36	39	18	6	37	43	14
Sweden	18	23	41	17	20	33	53	7
Switzerland	6	20	26	27	3	29	32	23
Italy	19	19	38	21	9	28	37	20
Iceland	11	32	43	14	17	23	40	17
Japan	8	10	18	33	4	16	20	33
Canada	24	37	61	3	23	34	57	5
Colombia	2	22	24	31	11	5	16	35
Costa Rica	1	24	25	29	1	7	8	37
Latvia	23	30	53	7	24	5	29	28
Lithuania	12	33	45	13	19	32	51	8
Luxembourg	4	18	22	32	5	27	32	23
Hungary	34	15	49	10	34	25	59	4
Mexico	37	31	68	1	37	10	47	10
Norway	36	29	65	2	36	36	72	1
Poland	32	25	57	5	32	30	62	2
Portugal	30	21	51	9	30	31	61	3
Slovak Republic	33	14	47	11	33	24	57	5
Slovenia	31	8	39	18	31	14	45	13
Chile	28	11	39	18	28	19	47	10
Türkiye	35	26	61	3	35	5	40	17
New Zealand	25	0	25	29	25	5	30	27
Greece	29	28	57	5	29	18	47	10

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Share and Cite

MDPI and ACS Style

Güler, E.; Yerel Kandemir, S. Assessment of Organization for Economic Co-Operation and Development Countries Based on Agricultural Performance Using Multi-Criteria Decision-Making Methods. Sustainability 2025, 17, 8291. https://doi.org/10.3390/su17188291

AMA Style

Güler E, Yerel Kandemir S. Assessment of Organization for Economic Co-Operation and Development Countries Based on Agricultural Performance Using Multi-Criteria Decision-Making Methods. Sustainability. 2025; 17(18):8291. https://doi.org/10.3390/su17188291

Chicago/Turabian Style

Güler, Ezgi, and Süheyla Yerel Kandemir. 2025. "Assessment of Organization for Economic Co-Operation and Development Countries Based on Agricultural Performance Using Multi-Criteria Decision-Making Methods" Sustainability 17, no. 18: 8291. https://doi.org/10.3390/su17188291

APA Style

Güler, E., & Yerel Kandemir, S. (2025). Assessment of Organization for Economic Co-Operation and Development Countries Based on Agricultural Performance Using Multi-Criteria Decision-Making Methods. Sustainability, 17(18), 8291. https://doi.org/10.3390/su17188291

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Assessment of Organization for Economic Co-Operation and Development Countries Based on Agricultural Performance Using Multi-Criteria Decision-Making Methods

Abstract

1. Introduction

2. Materials and Methods

2.1. Data Set

2.2. Analytical Hierarchy Process

2.3. Technique for Order Preference by Similarity to Ideal Solution

2.4. VlseKriterijumska Optimizacija I Kompromisno Resenje

2.5. Borda Count Technique

2.6. Spearman’s Rank Correlation Coefficient

3. Results

3.1. Weighting of Agricultural Performance Criteria

3.2. Technique for Order Preference by Similarity to Ideal Solution Analysis

3.3. VlseKriterijumska Optimizacija I Kompromisno Resenje Analysis

3.4. Comparison of Results

4. Discussion

4.1. Comparison to Prior Research

4.2. Research Implications

4.3. Policy Recommendations

5. Conclusions

Author Contributions

Funding

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Acknowledgments

Conflicts of Interest

Abbreviations

Appendix A

Appendix A.1. Steps of Analytical Hierarchy Process

Appendix A.2. Steps of Technique for Order Preference by Similarity to Ideal Solution

Appendix A.3. Steps of VlseKriterijumska Optimizacija I Kompromisno Resenje

References

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI